From b6e8b271c957e3e3c375d75b1ce2a1f8d94f2d6c Mon Sep 17 00:00:00 2001
From: rinaldia <antoniopio.rinaldi@sed.ethz.ch>
Date: Wed, 16 Oct 2024 22:02:30 +0200
Subject: [PATCH] First messy commit
---
LICENSE | 661 +++++++++++++++
README.md | 93 --
data/Cat_FORGE22.h5 | Bin 0 -> 895032 bytes
data/Hydr_FORGE22.h5 | Bin 0 -> 308752 bytes
data/Scenario.h5 | Bin 0 -> 7152 bytes
data/TrainingData.h5 | Bin 0 -> 17864 bytes
data/TrainingTarget.h5 | Bin 0 -> 1078792 bytes
data/seismic_data_full_located.h5 | Bin 0 -> 1179552 bytes
hm1d/FlowModel.py | 960 +++++++++++++++++++++
hm1d/SeismicityModel.py | 305 +++++++
hm1d/Worker_HM0_HM1_1D.py | 1322 +++++++++++++++++++++++++++++
hm1d/__init__.py | 0
hm1d/model.py | 882 +++++++++++++++++++
hm1d/ramsis.py | 44 +
hm1d/utils.py | 237 ++++++
ml1/FlowModel.py | 330 +++++++
ml1/__init__.py | 0
ml1/model.py | 455 ++++++++++
ml1/ramsis.py | 44 +
ml1/utils.py | 255 ++++++
model_plots.py | 201 +++++
model_plots_forge.py | 183 ++++
pyproject.toml | 3 +
run_model.py | 163 ++++
setup.cfg | 57 ++
tox.ini | 17 +
26 files changed, 6119 insertions(+), 93 deletions(-)
create mode 100644 LICENSE
delete mode 100644 README.md
create mode 100644 data/Cat_FORGE22.h5
create mode 100644 data/Hydr_FORGE22.h5
create mode 100644 data/Scenario.h5
create mode 100644 data/TrainingData.h5
create mode 100644 data/TrainingTarget.h5
create mode 100644 data/seismic_data_full_located.h5
create mode 100644 hm1d/FlowModel.py
create mode 100644 hm1d/SeismicityModel.py
create mode 100644 hm1d/Worker_HM0_HM1_1D.py
create mode 100644 hm1d/__init__.py
create mode 100644 hm1d/model.py
create mode 100644 hm1d/ramsis.py
create mode 100644 hm1d/utils.py
create mode 100644 ml1/FlowModel.py
create mode 100644 ml1/__init__.py
create mode 100644 ml1/model.py
create mode 100644 ml1/ramsis.py
create mode 100644 ml1/utils.py
create mode 100644 model_plots.py
create mode 100644 model_plots_forge.py
create mode 100644 pyproject.toml
create mode 100644 run_model.py
create mode 100644 setup.cfg
create mode 100644 tox.ini
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..839fee2
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,661 @@
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+
+14. Revised Versions of this License.
+
+The Free Software Foundation may publish revised and/or new versions of
+the GNU Affero General Public License from time to time. Such new versions
+will be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+Each version is given a distinguishing version number. If the
+Program specifies that a certain numbered version of the GNU Affero General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation. If the Program does not specify a version number of the
+GNU Affero General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+If the Program specifies that a proxy can decide which future
+versions of the GNU Affero General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+Later license versions may give you additional or different
+permissions. However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+15. Disclaimer of Warranty.
+
+THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+16. Limitation of Liability.
+
+IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+17. Interpretation of Sections 15 and 16.
+
+If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+ ramsis-sfm
+ Copyright (C) 2021 Indu
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published
+ by the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program. If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source. For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code. There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.
diff --git a/README.md b/README.md
deleted file mode 100644
index 15c8bb6..0000000
--- a/README.md
+++ /dev/null
@@ -1,93 +0,0 @@
-# HM-1D
-
-
-
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitlab.seismo.ethz.ch/indu/hm-1d.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.seismo.ethz.ch/indu/hm-1d/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
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diff --git a/hm1d/FlowModel.py b/hm1d/FlowModel.py
new file mode 100644
index 0000000..b48817f
--- /dev/null
+++ b/hm1d/FlowModel.py
@@ -0,0 +1,960 @@
+"""
+Flow Models for the HM0/1 classes
+by V. Clasen, A. P. Rinaldi, F. Ciardo (2024)
+"""
+
+import numpy as np
+from scipy.special import expi
+from scipy.special import erfc
+from scipy.optimize import differential_evolution
+from numba import njit
+
+########################################################################################################################
+########################################## ANALYTICAL FLOW MODEL #######################################################
+########################################################################################################################
+
+def Analytical_LineSource_Solution(InjectionStrategy, t_grid, r_grid, h, nu, S, k):
+ """
+ Args:
+ InjectionStrategy (2xNt array): doublets of each consecutive step:
+ duration of step and injection rate
+ (only Neumann's b.c. at the well)
+ t_grid (Numpy vector): consecutive time moments
+ r_grid (Numpy vector): radii
+ nu (scalar) : viscosity [Pa s]
+ k (scalar) : permeability [m]
+ S (scalar) : storage (porosity * compressibility) [Pa^-1]
+ Returns:
+ the analytical solution stimulation factor (Numpy vector) for the
+ input time moments and radii
+ """
+ Nt_end = len(t_grid)
+ sol = np.zeros([Nt_end, len(r_grid)])
+ Qsteps = np.array(InjectionStrategy[:, 1])
+ Qsteps[1:] = np.diff(Qsteps)
+ TimeSteps = np.array(InjectionStrategy[:, 0])
+ NumOfSteps = len(Qsteps)
+
+ for step in range(NumOfSteps):
+ JJ = np.where(t_grid > TimeSteps[step])
+ sol[JJ, :] = np.add(sol[JJ, :],
+ # StepInjection_Green_Function_linesource(r_grid, (t_grid[JJ] - TimeSteps[step]), Qsteps[step],
+ # Diff))
+ StepInjection_Green_Function_linesource(r_grid, (t_grid[JJ] - TimeSteps[step]), Qsteps[step],
+ h, nu, S, k))
+ return sol
+
+def Analytical_PointSource_Solution(InjectionStrategy, t_grid, r_grid, A, nu, S, k):
+ """
+ Args:
+ InjectionStrategy (2xNt array): doublets of each consecutive step:
+ duration of step and injection rate
+ (only Neumann's b.c. at the well)
+ t_grid (Numpy vector): consecutive time moments
+ r_grid (Numpy vector): radii
+ k (scalar) : permeability [m]
+ S (scalar) : storage (porosity * compressibility) [Pa^-1]
+ Returns:
+ the analytical solution stimulation factor (Numpy vector) for the
+ input time moments and radii
+ """
+ Nt_end = len(t_grid)
+ sol = np.zeros([Nt_end, len(r_grid)])
+ Qsteps = np.array(InjectionStrategy[:, 1])
+ Qsteps[1:] = np.diff(Qsteps)
+ TimeSteps = np.array(InjectionStrategy[:, 0])
+ NumOfSteps = len(Qsteps)
+
+ for step in range(NumOfSteps):
+ JJ = np.where(t_grid > TimeSteps[step])
+ sol[JJ, :] = np.add(sol[JJ, :],
+ # StepInjection_Green_Function_linesource(r_grid, (t_grid[JJ] - TimeSteps[step]), Qsteps[step],
+ # Diff))
+ StepInjection_Green_Function_pointsource(r_grid, (t_grid[JJ] - TimeSteps[step]), Qsteps[step],
+ A, nu, S, k))
+ return sol
+
+def StepInjection_Green_Function_linesource(r, t, Q, h, nu, S, k):
+ # def StepInjection_Green_Function_linesource(r, t, Q, Diff, val):
+ """
+ Args:
+ r (Numpy vector): radius of point(s) in meters
+ t (Numpy vector): time moment(s) in seconds
+ Q (scalar) : source term [m^3/(s)]
+ nu (scalar) : viscosity [Pa s]
+ k (scalar) : permeability [m]
+ S (scalar) : storage (porosity * compressibility) [Pa^-1]
+ Returns:
+ the 1D analytical solution (Numpy vector) for continuous line source type of injection
+ REF: Wu and Pruess, 2000, IJRMMS, 37, 51-61 -> Theis solution
+ similar with Diff REF: pag. 261, Eq. (2) - book: “Conduction of Heat in Solids†di Carslaw and Jaeger
+ """
+
+ X = expi(-np.matmul(np.reciprocal(t.reshape((-1, 1))), np.square(r.reshape((1, -1)))) * nu * S / k / 4.)
+
+ return (-Q * nu/ (4. * np.pi * k * h) ) * X
+
+def StepInjection_Green_Function_pointsource(r, t, Q, A, nu, S, k):
+ # def StepInjection_Green_Function_linesource(r, t, Q, Diff, val):
+ """
+ Args:
+ r (Numpy vector): radius of point(s) in meters
+ t (Numpy vector): time moment(s) in seconds
+ Q (scalar) : source term [m^3/(s)]
+ nu (scalar) : viscosity [Pa s]
+ k (scalar) : permeability [m]
+ S (scalar) : storage (porosity * compressibility) [Pa^-1]
+ Returns:
+ the 1D analytical solution (Numpy vector) for continuous point source type of injection
+ REF: Wu and Pruess, 2000, IJRMMS, 37, 51-61 -> Theis solution
+ """
+
+ xi = np.matmul(np.reciprocal(np.sqrt(t.reshape((-1, 1)))), r.reshape((1, -1))) / (np.sqrt(4. * k / S / mu))
+
+ X = 1./(np.sqrt(np.pi) * xi)*np.exp(-xi**2) - erfc(xi)
+
+ return (Q * nu * r.reshape((1, -1)) / (k * A)) * X
+
+class FlowModel0:
+ """
+ POINT SOURCE MODEL
+
+ Initialization with radius, numpy array with time and flow, as well as
+ crossectional area, model parameters initial guess, and their bounds when used for fitting
+ Model parameters are the storage coefficient (namely porosity times compressibility)
+ and the intrinsic rock permeability
+
+ Simple usage for forward simulation:
+
+ radius = np.geomspace(0.1, 2000, 30, endpoint=True) # radius for calculation
+ time_Q= np.linspace(0, 1e8, 2000) # time vector for flow
+ time_out = np.linspace(0, 1e8, 2000) # time vector for output, this can be different than time_Q as long as is larger
+ Q = np.ones((2000)*0.01 # volumetric injection rate in m3/s
+ QQ = np.vstack((time_Q.T,Q.T))
+ S = 1.e-9 # storage
+ k = 1.e-14 # permeability
+ h = 100 #layer thickness
+ rw = 0.15 #radius well
+ A = 2 * np.pi * h * rw # cross sectional area
+ Model = FlowModel1(radius,A,QQ,iniguess=(S, k))
+ Pressure = Model.predict() # numpy vector of pressure solution at time and radius
+
+ Simple usage for inversion:
+
+ radius = np.geomspace(0.1, 2000, 30, endpoint=True) # radius for calculation
+ TrainingTarget = 2xN array with the time moment and the pressure in Pa
+ time_Q= np.linspace(0, 1e8, 2000) # time vector for flow
+ Q = np.ones((2000)*0.01 # volumetric injection rate in m3/s
+ TrainingData = np.vstack((time_Q.T,Q.T))
+ h = 100 #layer thickness
+ rw = 0.15 #radius well
+ A = 2 * np.pi * h * rw # cross sectional area
+ Model = FlowModel1(radius,A,TrainingData,bounds=[(1e-15 1e-5), (1e-20, 1e-12)],iniguess=(1e-9, 1e-14))
+ Model.fit(TrainingTarget) # numpy vector of pressure solution at time and radius
+ Pressure = Model.predict()
+
+ For forecasting future pressure after fitting an injection Plan is needed
+ Q2 = Q*2
+ time2 = np.linspace(0, 1e8, 2000) +time[-1]
+ Plan = np.vstack((time2.T,Q2.T)
+ Prediction = Model.predict(Plan)
+ """
+
+ def __init__(self,
+ Q, # TrainingData for fitting
+ r_grid=np.geomspace(0.1, 2000, 30, endpoint=True),
+ A=90,
+ nu=1.e-3,
+ bounds=[(1e-15, 1e-5), (1e-20, 1e-12)],
+ iniguess=(1e-9, 1e-14)):
+ self.r = r_grid
+ self.A = A
+ self.nu = nu
+ self.Q = Q
+ # self.bounds = np.log10(bounds)
+ self.bounds = bounds
+ self.params = iniguess
+ self.model = Analytical_PointSource_Solution
+
+ def Cost(self, x):
+ S = np.power(10., x[0])
+ k = np.power(10., x[1])
+ _prediction = self.model(self.Q, self.t_target, self.r_target, self.A, self.nu, S, k) + self.observation[0]
+ # _prediction = self.model(self.Q, self.t_target, self.r_target, self.A, S, k)
+ Coeff_R2 = np.sum(np.square(_prediction / 1.e6 - self.observation / 1.e6)) / len(self.observation)
+
+ return Coeff_R2
+
+ def fit(self, TrainingTarget,
+ popsize=10,
+ polish=True,
+ disp=True,
+ maxiter=100,
+ seed=22,
+ tol=1e-5):
+ """
+ Args:
+ TrainingData (Numpy array) : 2xN array with the time moment and the injection rate until next measurement
+ TrainingTarget(Numpy array): 2xM array with the time moment and pressure measurement
+ Returns:
+
+ """
+ self.t_target = TrainingTarget[:, 0]
+ self.observation = TrainingTarget[:, 1:]
+ self.r_target = self.r[:len(self.observation[0, :])]
+
+ self.iter_results = []
+ result = differential_evolution(self.Cost, self.bounds, popsize=popsize, polish=polish,
+ disp=disp, maxiter=maxiter, seed=seed, tol=tol, callback=self.fm_iteration)
+ self.params = np.power(10., result.x[0]), \
+ np.power(10., result.x[1])
+
+ self.hidden_mechanism = self.predict()
+
+ def fm_iteration(self, xk, convergence):
+ self.iter_results.append(xk)
+
+ def predict(self, Plan=[], ):
+ """
+ Args:
+ Plan (Numpy array) : 2xNf array with the time moment and the injection rate expected from that moment.
+ Returns:
+ (Numpy array) : 2x(Nf+N) array with the time moment and the pressure due to the plan and the training data
+ """
+ Q = self.Q
+ t_grid = Q[:, 0]
+ if (len(Plan) > 0):
+ Q = np.vstack((Q, Plan))
+ t_grid = Plan[:, 0]
+
+ Prediction = self.model(Q, t_grid, self.r, self.A, self.nu, self.params[0], self.params[1]) + self.observation[0]
+ return Prediction
+
+class FlowModel1:
+ """
+ LINE SOURCE MODEL (Theis' solution)
+
+ Initialization with radius, numpy array with time and flow, as well as
+ reservoir thickness, model parameters initial guess, and their bounds when used for fitting
+ Model parameters are the storage coefficient (namely porosity times compressibility)
+ and the intrinsic rock permeability
+
+ Simple usage for forward simulation:
+
+ radius = np.geomspace(0.1, 2000, 30, endpoint=True) # radius for calculation
+ time_Q= np.linspace(0, 1e8, 2000) # time vector for flow
+ time_out = np.linspace(0, 1e8, 2000) # time vector for output, this can be different than time_Q as long as is larger
+ Q = np.ones((2000)*0.01 # volumetric injection rate in m3/s
+ QQ = np.vstack((time_Q.T,Q.T))
+ S = 1.e-9 # storage
+ nu = 1.e-3 # viscosity
+ k = 1.e-14 # permeability
+ h = 100 #layer thickness
+ Model = FlowModel1(radius,h,QQ,iniguess=(S, k))
+ Pressure = Model.predict() # numpy vector of pressure solution at time and radius
+
+ Simple usage for inversion:
+
+ radius = np.geomspace(0.1, 2000, 30, endpoint=True) # radius for calculation
+ TrainingTarget = 2xN array with the time moment and the pressure in Pa
+ time_Q= np.linspace(0, 1e8, 2000) # time vector for flow
+ Q = np.ones((2000)*0.01 # volumetric injection rate in m3/s
+ TrainingData = np.vstack((time_Q.T,Q.T))
+ h = 100 #layer thickness
+ Model = FlowModel1(radius,h,TrainingData,bounds=[(1e-15 1e-5), (1e-20, 1e-12)],iniguess=(1e-9, 1e-14))
+ Model.fit(TrainingTarget) # numpy vector of pressure solution at time and radius
+ Pressure = Model.predict()
+
+ For forecasting future pressure after fitting an injection Plan is needed
+ Q2 = Q*2
+ time2 = np.linspace(0, 1e8, 2000) +time[-1]
+ Plan = np.vstack((time2.T,Q2.T)
+ Prediction = Model.predict(Plan)
+ """
+
+ def __init__(self,
+ Q,
+ r_grid=np.geomspace(0.1, 2000, 30, endpoint=True),
+ h=100,
+ nu=1.e-3,
+ bounds=[(1e-15, 1e-5), (1e-20, 1e-12)],
+ iniguess=(1e-9, 1e-14)):
+ self.r = r_grid
+ self.h = h
+ self.nu = nu
+ self.Q = Q
+
+ #self.bounds = np.log10(bounds)
+ self.bounds = bounds
+ self.params = iniguess
+ self.model = Analytical_LineSource_Solution
+ # for callback
+ self.best_x = None
+ self.minf = np.inf
+
+ def save_iteration_step(self, x, convergence):
+ """
+ function to save history of parameters and residual during DEA
+ """
+ # check if equal
+ np.testing.assert_equal(x, self.best_x)
+ self.dea_vals.append([np.power(10., x[0]),
+ np.power(10., x[1])])
+ self.dea_func.append(self.minf)
+ return
+
+ def Cost(self, x):
+ S = np.power(10., x[0])
+ k = np.power(10., x[1])
+ _prediction = self.model(self.Q, self.t_target, self.r_target, self.h, self.nu, S, k)+self.observation[0]
+ #_prediction = self.model(self.Q, self.t_target, self.r_target, self.h, S, k)
+ Coeff_R2 = np.sum(np.square(_prediction / 1.e6 - self.observation / 1.e6)) / len(self.observation)
+
+ if Coeff_R2 < self.minf:
+ self.minf = Coeff_R2
+ self.best_x = x
+
+ return Coeff_R2
+
+ def fit(self, TrainingTarget,
+ popsize=10,
+ polish=True,
+ disp=True,
+ maxiter=100,
+ seed=22,
+ tol=1e-5):
+ """
+ Args:
+ TrainingData (Numpy array) : 2xN array with the time moment and the injection rate until next measurement
+ TrainingTarget(Numpy array): 2xM array with the time moment and pressure measurement
+ Returns:
+
+ """
+ self.t_target = TrainingTarget[:, 0]
+ self.observation = TrainingTarget[:, 1:] # gets rid of the column in index nr. 1 (so the second column)
+ self.r_target = self.r[:len(self.observation[0, :])]
+
+ # open list to save the progress of the minimization
+ self.dea_vals = []
+ self.dea_func = []
+
+ result = differential_evolution(self.Cost, self.bounds, popsize=popsize, polish=polish,
+ disp=disp, maxiter=maxiter, seed=seed, tol=tol, callback=self.save_iteration_step)
+ self.params = np.power(10., result.x[0]), \
+ np.power(10., result.x[1])
+
+ self.hidden_mechanism = self.predict()
+
+ def predict(self, Plan=[], ):
+ """
+ Args:
+ Plan (Numpy array) : 2xNf array with the time moment and the injection rate expected from that moment.
+ Returns:
+ (Numpy array) : 2x(Nf+N) array with the time moment and the pressure due to the plan and the training data
+ """
+ Q = self.Q
+ t_grid = Q[:, 0]
+ if (len(Plan) > 0):
+ Q = np.vstack((Q, Plan))
+ t_grid = Plan[:, 0]
+ Prediction = self.model(Q, t_grid, self.r, self.h, self.nu, self.params[0], self.params[1]) + self.observation[0]
+ return Prediction
+
+########################################################################################################################
+##################################### NUMERICAL FLOW MODEL #############################################################
+########################################################################################################################
+"""
+Solving the RADIAL diffusion equation using the implicit finite difference (FD) approach with backward difference in
+time and second order central difference in space.
+"""
+
+def create_con_matrix(n_el):
+ """
+ builds up the connectivity matrix
+ """
+ con_mat = np.zeros((n_el, 2))
+ for m in range(n_el):
+ con_mat[m, 0] = m
+ con_mat[m, 1] = m + 1
+ return con_mat
+
+def times_step_func(i, t):
+ """
+ creates the time steps in case the time stepping is not the same
+ """
+ return t[i+1] - t[i]
+
+def create_radius(r, dr):
+ """
+ creates the radius taking the radial position and half the distance to the next radial position
+ """
+ return r + 0.5 * dr
+
+def tridiag(nx, diag, diag_above, diag_below):
+ L = np.zeros((nx, nx))
+ for k in range(nx-1):
+ L[k, k] = diag[k]
+ L[k, k+1] = diag_above[k]
+ L[k+1, k] = diag_below[k]
+ L[nx-1, nx-1] = diag[nx-1]
+ return L
+
+def Nonuniform_Tridiag(nr, con_mat, D, dt, dr_array, r):
+ """
+ -> solves for the matrix entries for the 1D linear pressure diffusion solution
+ """
+ L = np.zeros((nr, nr))
+ V = np.zeros((nr, nr))
+ for n in range(nr):
+ elements = np.where(con_mat==n)[0]
+ nod_rep = []
+ el = []
+ for e in elements:
+ el.append(int(e))
+ nodes = con_mat[e,:]
+ for l in nodes:
+ if not l == n:
+ idx = np.where(nodes==l)
+ nod_rep.append(int(nodes[idx]))
+ if len(el) == 1:
+ el.append(int(el[0]))
+ nod = sorted(list(set(nod_rep)))
+ beta_md = (2 * D[n] * dt) / (dr_array[el[0]] * dr_array[el[1]])
+ L[n, n] = beta_md
+ gamma_md = 0
+ V[n, n] = gamma_md
+ if len(nod) > 1:
+ beta_ld = (2 * D[nod[0]] * dt) / (dr_array[el[0]] * (dr_array[el[0]] + dr_array[el[1]]))
+ L[n, nod[0]] = - beta_ld
+ gamma_ld = (D[nod[0]] * dt) / (r[n] * (dr_array[el[0]] + dr_array[el[1]]))
+ V[n, nod[0]] = gamma_ld
+ beta_ud = (2 * D[nod[-1]] * dt) / (dr_array[el[1]] * (dr_array[el[0]] + dr_array[el[1]]))
+ L[n, nod[-1]] = - beta_ud
+ gamma_ud = (D[nod[-1]] * dt) / (r[n] * (dr_array[el[0]] + dr_array[el[1]]))
+ V[n, nod[-1]] = - gamma_ud
+ else:
+ beta_l_u_d = (2 * D[nod[0]] * dt) / (dr_array[el[0]]**2)
+ L[n, nod[0]] = - beta_l_u_d
+ del el, nod_rep
+ V[-2, -1] = 0
+ V[-1, -2] = 0
+ V[-1, -1] = 0
+ I = np.identity(nr)
+ return I + L + V
+
+def Numerical_Functions_Kf_change(sigma_n, pn, K_n, ap_0, ap_res, fr_s, dt, S, nu, dr_array, nr, q_f, con_mat, r):
+ """
+ -> solves for the pressure solution for one timestep for the radial problem including changes in permeability
+ """
+ q_inj = np.zeros(nr)
+ sigma_n_eff = sigma_n - pn
+ enc_rev = sigma_n_eff/(K_n + sigma_n_eff/(ap_0 - ap_res))
+ wh = ap_0 - enc_rev
+ kf = (wh**3)/(12*fr_s)
+ kf_max = (ap_0**3)/(12 * fr_s)
+ if np.any(sigma_n_eff < 0.):
+ idx = np.where(sigma_n_eff < 0.)
+ kf[idx] = kf_max
+ q_inj[0] = q_f / (wh[0] * np.min(r)**2 * np.pi * S)
+ b = q_inj * dt + pn
+ D = kf / (S * nu)
+ L = Nonuniform_Tridiag(nr, con_mat, D, dt, dr_array, r)
+ pres = np.linalg.solve(L, b)
+ return pres
+
+def Numerical_RadialSolution_Kf_invariable(S, kf, q_f, dt, nu, dr_array, nr, n_el, h, r, nt):
+ """
+ -> final pressure solution for all timesteps for radial problem excluding changes in permeability
+ """
+ q_inj = np.zeros(nr)
+ p0 = np.zeros(nr)
+ p_num = np.zeros((nt,nr))
+ kf = np.full_like(q_inj, kf)
+ pn = p0
+ con_mat = create_con_matrix(n_el)
+ for i in range(nt):
+ q_inj[0] = q_f[i] / (h * np.min(r)**2 * np.pi * S)
+ b = q_inj * dt + pn
+ D = kf / (S * nu)
+ L = Nonuniform_Tridiag(nr, con_mat, D, dt, dr_array, r)
+ p_num_step = np.linalg.solve(L, b)
+ p_num[i] = p_num_step
+ pn = p_num_step
+ return p_num
+
+def Numerical_RadialSolution_Kf_variable(ap_0, ap_res, K_n, S, sigma_n, fr_s, dt, nu, dr_array, nr, n_el, q_f, r, nt):
+ """
+ -> final pressure solution for all timesteps for radial problem including changes in permeability
+ """
+ p0 = np.zeros(nr)
+ p_num = np.zeros((nt, nr))
+ pn = p0
+ con_mat = create_con_matrix(n_el)
+ for i in range(nt):
+ p_num_step = Numerical_Functions_Kf_change(sigma_n, pn, K_n, ap_0, ap_res, fr_s, dt, S, nu, dr_array,
+ nr, q_f[i], con_mat, r)
+ p_num[i] = p_num_step
+ pn = p_num_step
+ return p_num
+
+class FlowModel2:
+ """
+ ADD DESCRIPTION: same as FlowModel3, but without permeability changes (read below)
+ """
+ def __init__(self,
+ Q,
+ r_grid,
+ h,
+ params_fix,
+ bounds=[(1e-15, 1e-5), (1e-20, 1e-12)],
+ iniguess=(1e-9, 1e-14)):
+ self.r = r_grid
+ self.h = h
+ self.params_fix = params_fix
+ self.Q = Q
+
+ #self.bounds = np.log10(bounds)
+ self.bounds = bounds
+ self.params = (params_fix['storage'],
+ params_fix['permeability'])
+ self.model = Numerical_RadialSolution_Kf_invariable
+
+ def translate_training(self, TrainingData, TrainingTarget, params_fix):
+
+ self.observation = TrainingTarget[:, 1]
+ self.Q = TrainingData[:, :2]
+
+ Q = TrainingData[:, 1]
+ t_grid = TrainingData[:, 0]
+ self.t_grid = t_grid
+ dt = abs(t_grid[0] - t_grid[1])
+ nt = len(t_grid)
+ r_grid = self.r # node positions (radial)
+ dr_array = np.diff(r_grid) # spacing between nodes
+ nr = len(r_grid) # number of nodes
+ n_el = len(dr_array) # number of elements
+
+ nu = np.power(10., params_fix['viscosity'])
+
+ self.args = (Q, dt, nu, dr_array, nr, n_el, self.h, r_grid, nt)
+
+ return
+
+ def Cost(self, x, porosity, Q, dt, nu, dr_array, nr, n_el, h, r, nt):
+ S = np.power(10., x[0])
+ kf = np.power(10., x[1])
+ p_num = self.model(S, kf, porosity, Q, dt, nu, dr_array, nr, n_el, h, r, nt)
+ p_pred = p_num[:,0] + self.observation[0]
+ Coeff = np.sum(np.square(p_pred/1.e6 - self.observation/1.e6))/len(self.observation)
+
+ return Coeff
+
+ def fit(self, TrainingData, TrainingTarget):
+
+ self.translate_training(TrainingData, TrainingTarget, self.params_fix)
+
+ self.result = differential_evolution(func=self.Cost, bounds=self.bounds, args=self.args,
+ maxiter=100, popsize=10, polish=True, disp=True)
+ self.params = np.power(10., self.result.x[0]), \
+ np.power(10., self.result.x[1])
+
+ self.hidden_mechanism = self.predict()
+
+ def predict(self, Plan=[],):
+ Q = self.Q
+ t_grid = Q[:, 0]
+ if (len(Plan) > 0):
+ Q = np.vstack((Q, Plan))
+ #Q = Plan[:,1]
+ t_grid = Q[:,0]
+ self.args = list(self.args)
+ self.args[1] = Q[:,1]
+ self.args[2] = abs(t_grid[0] - t_grid[1])
+ self.args[-1] = len(t_grid)
+ #r_grid = self.r
+ pres_num = self.model(self.params[0], self.params[1], self.args[0], self.args[1], self.args[2], self.args[3],
+ self.args[4], self.args[5], self.args[6], self.args[7], self.args[8], self.args[9])
+ if len(self.Q) < len(pres_num):
+ Prediction = pres_num[len(self.Q):,:] + self.observation[0]
+ else:
+ Prediction = pres_num + self.observation[0]
+ return Prediction
+
+class FlowModel3:
+ """
+ ADD DESCRIPTION
+ """
+
+ def __init__(self,
+ Q,
+ r_grid,
+ h,
+ params_fix,
+ bounds=[(1e-5, 1e-2), (1e-9, 1e-5), (8, 14), (-11, -7)],
+ iniguess=(1e-4, 1e-8, 1e11, 1e-8)):
+ self.r = r_grid
+ self.h = h
+ self.params_fix = params_fix
+ self.Q = Q
+
+ #self.bounds = np.log10(bounds)
+ self.bounds = bounds
+ self.params = (params_fix['zero_stress_aperture'],
+ params_fix['residual_aperture'],
+ params_fix['frac_normal_stiffness'],
+ params_fix['storage'])
+ self.model = Numerical_RadialSolution_Kf_variable
+
+ def translate_training(self, TrainingData, TrainingTarget, params_fix):
+
+ self.observation = TrainingTarget[:, 1]
+ self.Q = TrainingData[:, :2]
+
+ Q = TrainingData[:, 1]
+ t_grid = TrainingData[:, 0]
+ self.t_grid = t_grid
+ dt = abs(t_grid[0] - t_grid[1])
+ nt = len(t_grid)
+ r_grid = self.r # node positions (radial)
+ dr_array = np.diff(r_grid) # spacing between nodes
+ nr = len(r_grid) # number of nodes
+ n_el = len(dr_array) # number of elements
+
+ sigma_n = params_fix['fix_normal_stress'] * (1e6)
+ fr_s = np.power(10., params_fix['frac_spacing'])
+ nu = np.power(10., params_fix['viscosity'])
+
+ self.args = (sigma_n, fr_s, dt, nu, dr_array, nr, n_el, Q, r_grid, nt)
+
+ return
+
+ def Cost(self, x, porosity, sigma_n, fr_s, dt, nu, dr_array, nr, n_el, Q, r_grid, nt):
+ # parameters to be calibrated
+ ap_0 = np.power(10., x[0]) # aperture at zero stress
+ ap_res = np.power(10., x[1]) # residual aperture
+ K_n = np.power(10., x[2]) # fracture normal stiffness
+ S = np.power(10., x[3]) # storage
+ p_num = self.model(ap_0, ap_res, K_n, S, porosity, sigma_n, fr_s, dt, nu, dr_array, nr, n_el, Q, r_grid, nt)
+ p_pred = p_num[:,0] + self.observation[0]
+ Coeff_R2 = np.sum(np.square(p_pred/1.e6 - self.observation/1.e6))/len(self.observation)
+
+ return Coeff_R2
+
+ def fit(self, TrainingData, TrainingTarget,
+ popsize=10,
+ polish=True,
+ disp=True,
+ maxiter=100,
+ seed=22,
+ tol=1e-5):
+
+ self.translate_training(TrainingData, TrainingTarget, self.params_fix)
+
+ self.result = differential_evolution(self.Cost, self.bounds, args=self.args, popsize=popsize, polish=polish,
+ disp=disp, maxiter=maxiter, seed=seed, tol=tol)
+ self.params = np.power(10., self.result.x[0]), \
+ np.power(10., self.result.x[1]), \
+ np.power(10., self.result.x[2]), \
+ np.power(10., self.result.x[3])
+
+ self.hidden_mechanism = self.predict()
+
+ def predict(self, Plan=[],):
+ Q = self.Q
+ t_grid = Q[:, 0]
+ if (len(Plan) > 0):
+ Q = np.vstack((Q, Plan))
+ #Q = Plan[:,1]
+ t_grid = Q[:,0]
+ self.args = list(self.args)
+ self.args[-3] = Q[:,1]
+ self.args[3] = abs(t_grid[0] - t_grid[1])
+ self.args[-1] = len(t_grid)
+ #r_grid = self.r
+ pres_num = self.model(self.params[0], self.params[1], self.params[2], self.params[3], self.args[0], self.args[1],
+ self.args[2], self.args[3], self.args[4], self.args[5], self.args[6], self.args[7],
+ self.args[8], self.args[9], self.args[10])
+ if len(self.Q) < len(pres_num):
+ Prediction = pres_num[len(self.Q):,:] + self.observation[0]
+ else:
+ Prediction = pres_num + self.observation[0]
+ return Prediction
+
+"""
+Solving the RADIAL diffusion equation using the implicit finite volume (FV) approach.
+"""
+
+@njit(nopython=True) # activate NUMBA
+def flow_model_nonlinear(params, Rw, Np, nu, h, q_f, S, t, dr, x):
+ # Here we unpack the parameters
+ sigma_n = params[0] # fixed normal stress
+ K_n = params[1] # fracture normal stiffness
+ a_el_max = params[2] # maximum or zero-stress fracture aperture
+ a_res = params[3] # residual fracture aperture
+ fr_s = params[4] # fracture spacing
+ a_shear_f = params[5] # constant shearing aperture factor
+ a_shear_max = params[6] # maximum shearing aperture
+ p_t = params[7] # threshold pressure
+ alpha = params[8] # scaling factor for shearing aperture magnitude
+ w = params[9] # weighting parameter for numerical stability
+ # setting up empty matrix diagonals and the variables to store
+ lwd = np.zeros(Np, dtype=float)
+ md = np.zeros(Np+1, dtype=float)
+ upd = np.zeros(Np, dtype=float)
+ b = np.zeros(Np+1, dtype=float)
+ pres_step = np.zeros(Np+1, dtype=float)
+ kf_var = np.zeros((len(t), Np+1), dtype=float)
+ a_vals_1 = np.zeros((len(t), Np+1), dtype=float)
+ a_vals_2 = np.zeros((len(t), Np+1), dtype=float)
+ phi_vals = np.zeros((len(t), Np+1), dtype=float)
+ pres_cum = np.zeros((len(t), Np+1), dtype=float)
+ pres_total = np.zeros((len(t), Np+1), dtype=float)
+ # # # set an array full of phi:
+ phi = np.full(len(pres_step), 1)
+ phi_ = phi # delete this to include porosity changes
+ # # DETECT SHUT-IN #
+ # v_inj = np.cumsum(np.maximum(q_f[1:], 0) * np.diff(t))
+ # t_shut = t[np.argmax(np.round(v_inj, decimals=5)) + 1]
+ # idx_shut = np.where(t_shut==t)[0][0]
+ # S_shut = S * 100
+ for t_step in range(len(t)):
+ # print(t_step)
+ # dt = times_step_func(t_step, t)
+ dt = abs(t[1] - t[0])
+ if dt==0:
+ continue
+ # PERMEABILITY CHANGES DUE TO FRACTURE APERTURE VARIATION #
+ # Elastic fracture aperture #
+ sigma_n_eff = sigma_n - pres_step # effective normal stress
+ a_el = sigma_n_eff / (K_n + sigma_n_eff/(a_el_max - a_res)) # reversible elastic normal closure
+ # mask for all normal effective stress equal to zero or negative
+ mask1 = sigma_n_eff <= 0
+ a_el[mask1] = 0.0
+ # Shearing fracture aperture #
+ if t_step==0:
+ pres_cum[t_step] = pres_step
+ a_vals_2[t_step] = np.zeros_like(pres_step)
+ # PRESSURE DEPENDENT PROSITY CHANGES #
+ # phi_ = phi.copy()
+ # Cp = S / phi0[0]
+ else:
+ # set new pressure array
+ pres_update = np.copy(pres_cum[t_step-1])
+ # create an array full of zeros
+ sf2 = np.zeros_like(pres_step)
+ # mask if pressure increases w.r.t. the previous time step
+ mask2 = (pres_step - pres_update) / dt > 0
+ # modify the array to mimick the step function with mask2
+ sf2[mask2] = 1
+ # create an array full of zeros
+ sf3 = np.zeros_like(pres_step)
+ # mask if pressure is above the threshold value
+ mask3 = pres_step > p_t
+ # modify the array to mimick the step function with mask3
+ sf3[mask3] = 1
+ a_shear = a_shear_f * sf2 * sf3 * (1 + alpha * (pres_step - pres_update))
+ # mask if new (cumulated) shearing aperture is above threshold
+ mask4 = (a_vals_2[t_step-1] + a_shear) > a_shear_max
+ # modify the array to mimick the step function with mask4
+ a_shear[mask4] = 0
+ # add time step contribution to the shear aperture history
+ a_vals_2[t_step] = a_vals_2[t_step-1] + a_shear
+ # apply the smoothing factor for numerical stability (here only for a_el)
+ a_el = (1 - w) * a_vals_1[t_step - 1] + w * a_el
+ # # PRESSURE DEPENDENT POROSITY CHANGES #
+ # phi_ = phi * S * (pres_step - pres_update)
+ # phi_ = phi + phi_
+ # print(f'{phi_[0], t_step, pres_step[0]/1.e6, S}')
+ # phi_vals[t_step] = phi_
+ # compute the total fracture aperture
+ a = a_el_max - a_el + a_vals_2[t_step]
+ # save the history of the fracture aperture contributions
+ a_vals_1[t_step] = a_el
+ # a_vals_2[t_step] = a_shear
+ kf = (a**3)/(12*fr_s)
+ # save the permeability history
+ kf_var[t_step] = kf
+ # boundary conditions at injection point
+ md[0] = - kf[0] * 2 * np.pi * Rw * h / (nu * dr[0]) + (S * phi_[0])/dt
+ upd[0] = kf[0] * 2 * np.pi * Rw * h / (nu * dr[0])
+ b[0] = - q_f[t_step] + (S * phi_[0]) * pres_step[0] / dt
+ for j in range(1, Np):
+ r = (x[j-1] + 0.5 * dr[j-1]) * dt
+ lwd[j-1] = kf[j-1] * (r - 0.5 * dr[j-1]) / (nu * dr[j-1] * dr[j-1])
+ md[j] = - ((2. * kf[j-1] * r) / (nu * dr[j-1] * dr[j-1]) + (r * (S * phi_[j-1]) / dt))
+ upd[j] = kf[j-1] * (r + 0.5 * dr[j-1]) / (nu * dr[j-1] * dr[j-1])
+ b[j] = - r * (S * phi_[j]) * pres_step[j] / dt
+ # boundary conditions at the far field
+ lwd[Np-1] = 0.
+ md[Np] = 1.
+ upd[Np-1] = 0.
+ b[Np] = 0.
+ # Setting up the matrix and solving the system of linear equations
+ L = np.zeros((Np+1, Np+1))
+ for k in range(Np):
+ L[k, k] = md[k]
+ L[k, k+1] = upd[k]
+ L[k+1, k] = lwd[k]
+ L[Np, Np] = md[Np]
+ # if np.linalg.det(L)==0.0:
+ # pdb.set_trace()
+ pres_step = np.linalg.solve(L, b)
+ # store the new pressure step
+ pres_total[t_step] = pres_step
+ # print(S)
+
+ return pres_total, kf_var, a_vals_1, a_vals_2, phi_vals
+
+class FlowModel4:
+ """
+ ADD DESCRIPTION
+ """
+
+ def __init__(self,
+ Q,
+ r_grid,
+ h,
+ params_fix,
+ # bounds=[(1e-5, 1e-2), (1e-8, 1e-5), (1e-7, 1e-3), (1e-10, 1e-6), (-11, -7)],
+ bounds=[(1e-5, 1e-2), (1e-8, 1e-5), (1e-8, 1e-4), (1e-6, 1e-4), (1e-12, 1e-7), (-11, -7)],
+ # iniguess=(1e-5, 1e-6, 5e-6, 1e-8, 1e-9),
+ iniguess=(4e-5, 1e-5, 5e-7, 2e-5, 3e-10, 1e-9),
+ forward_model=False):
+ self.r = r_grid
+ self.h = h
+ self.params_fix = params_fix
+ self.Q = Q
+ self.bounds = bounds
+ self.params = iniguess
+ self.model = flow_model_nonlinear
+ # for callback
+ self.best_x = None
+ forward_model_ = forward_model,
+ self.forward_model = forward_model_[0]
+ self.minf = np.inf
+
+ def save_iteration_step(self, x, convergence):
+ """
+ function to save history of parameters and residual during DEA
+ """
+ # check if equal
+ #np.testing.assert_equal(x, self.best_x)
+ self.dea_vals.append([np.power(10., x[0]),
+ np.power(10., x[1]),
+ np.power(10., x[2]),
+ np.power(10., x[3]),
+ np.power(10., x[4]),
+ np.power(10., x[5])])
+ self.dea_func.append(self.minf)
+ return
+
+ def translate_training(self, TrainingData, TrainingTarget, params_fix):
+
+ self.observation = TrainingTarget[:, 1]
+ self.Q = TrainingData[:, :2]
+
+ Q = TrainingData[:, 1]
+ t_grid = TrainingData[:, 0]
+ self.t_grid = t_grid
+ # r_grid = self.r # node positions (radial)
+ # dr_array = np.diff(r_grid) # spacing between nodes
+ # nr = len(r_grid) - 1 # number of nodes minus one (which is equal to the number of elements)
+
+ self.damping = params_fix['damping_parameter']
+ pt = params_fix['threshold_pressure'] * (1e6) - self.observation[0]
+ sigma_n = params_fix['fix_normal_stress'] * (1e6)
+ fr_s = params_fix['frac_spacing']
+ nu = np.power(10., params_fix['viscosity'])
+ K_n = np.power(10., params_fix['frac_normal_stiffness'])
+
+ self.args = (sigma_n, fr_s, K_n, t_grid, nu, np.diff(self.r), len(self.r) - 1, Q, self.r, self.h, self.damping, pt)
+
+ return
+
+ def Cost(self, x, sigma_n, fr_s, K_n, t, nu, dr, nr, Q, r, h, w, pt):
+ # parameters to be calibrated
+ a_max = np.power(10., x[0]) # aperture at zero stress
+ a_res = np.power(10., x[1]) # residual aperture
+ # a_shear_init = np.power(10., x[2]) # shear initial aperture
+ # gamma = np.power(10., x[3]) # shearing damping factor
+ a_shear_f = np.power(10., x[2]) # constant shearing aperture factor
+ a_shear_max = np.power(10., x[3]) # maximum threshold for shearing aperture
+ alpha = np.power(10., x[4]) # pressure difference scaling factor on shearing
+ S = np.power(10., x[5]) # storage
+ # params = (sigma_n, K_n, a_max, a_res, fr_s, a_shear_init, gamma, w)
+ params = (sigma_n, K_n, a_max, a_res, fr_s, a_shear_f, a_shear_max, pt, alpha, w)
+ p_num = self.model(params, r[0], nr, nu, h, Q, S, t, dr, r)[0]
+ p_pred = p_num[:,0] + self.observation[0]
+ Coeff_R2 = np.sum(np.square(p_pred/1.e6 - self.observation/1.e6))/len(self.observation)
+
+ if Coeff_R2 < self.minf:
+ self.minf = Coeff_R2
+ self.best_x = x
+
+ return Coeff_R2
+
+ def fit(self, TrainingData, TrainingTarget,
+ popsize=10,
+ polish=True,
+ disp=True,
+ maxiter=20,
+ seed=22,
+ tol=1e-5):
+
+ self.translate_training(TrainingData, TrainingTarget, self.params_fix)
+
+ # open list to save the progress of the minimization
+ self.dea_vals = []
+ self.dea_func = []
+
+ self.result = differential_evolution(self.Cost, self.bounds, args=self.args, popsize=popsize, polish=polish,
+ disp=disp, maxiter=maxiter, seed=seed, tol=tol, updating='immediate', callback=self.save_iteration_step)
+
+ self.params = np.power(10., self.result.x[0]), \
+ np.power(10., self.result.x[1]), \
+ np.power(10., self.result.x[2]), \
+ np.power(10., self.result.x[3]), \
+ np.power(10., self.result.x[4]), \
+ np.power(10., self.result.x[5])
+
+ self.hidden_mechanism = self.predict()
+
+ def predict(self, Plan=[],):
+ Q = self.Q
+ t_grid = Q[:, 0]
+ if (len(Plan) > 0):
+ if self.forward_model==True:
+ if len(Q) <= 1:
+ Q = Plan
+ else:
+ Q = np.vstack((Q, Plan))
+ else:
+ Q = np.vstack((Q, Plan))
+ t_grid = Q[:,0]
+ self.args = list(self.args)
+ self.args[3] = t_grid
+ self.args[7] = Q[:,1]
+
+ # params = (self.args[0], self.args[2], self.params[0], self.params[1], self.args[1], self.params[2], self.params[3], self.args[10])
+ # pres_num, self.param_evol_fit_1, self.param_evol_fit_2, self.param_evol_fit_3 = self.model(params, self.args[8][0], self.args[6], self.args[4],
+ # self.h, self.args[7], self.params[4], self.args[3],
+ # self.args[5], self.args[8])
+
+ params = (self.args[0], self.args[2], self.params[0], self.params[1], self.args[1], self.params[2], self.params[3], self.args[11], self.params[4], self.args[10])
+ pres_num, self.param_evol_fit_1, self.param_evol_fit_2, self.param_evol_fit_3, self.param_evol_fit_4 = self.model(params, self.args[8][0], self.args[6], self.args[4],
+ self.h, self.args[7], self.params[5], self.args[3],
+ self.args[5], self.args[8])
+
+ self.param_evol_pred_1 = self.param_evol_fit_1[len(self.Q):, :]
+ self.param_evol_pred_2 = self.param_evol_fit_2[len(self.Q):, :]
+ self.param_evol_pred_3 = self.param_evol_fit_3[len(self.Q):, :]
+ self.param_evol_pred_4 = self.param_evol_fit_4[len(self.Q):, :]
+
+ if len(self.Q) < len(pres_num):
+ if self.forward_model==False:
+ Prediction = pres_num[len(self.Q):,:] + self.observation[0] # somehow this was a solution before but now it is wrong! (20.08.2024)
+ else:
+ Prediction = pres_num + self.observation[0]
+ else:
+ Prediction = pres_num + self.observation[0]
+ return Prediction
\ No newline at end of file
diff --git a/hm1d/SeismicityModel.py b/hm1d/SeismicityModel.py
new file mode 100644
index 0000000..d571d2f
--- /dev/null
+++ b/hm1d/SeismicityModel.py
@@ -0,0 +1,305 @@
+"""
+Seismicity Models for the HM0/1 classes
+by V. Clasen, A. P. Rinaldi, D. Karvounis (2024)
+"""
+
+import numpy as np
+from scipy.stats import foldnorm
+import random
+from numba import njit
+
+"""
+CAPS model
+"""
+def Caps_Model_linear(Moments, Edges, MidEdges, max_pressure, CapsSettings, NSim, retriggering=False):
+ # get and rename parameters
+ sigma1_mean = CapsSettings['inter_sigma1']
+ sigma3_mean = CapsSettings['inter_sigma3']
+ sigma1_std = CapsSettings['sigma1_std']
+ sigma3_std = CapsSettings['sigma3_std']
+ cohesion = CapsSettings['cohesion_mean']
+ # cohesion_std = CapsSettings['cohesion_std']
+ mu = CapsSettings['friction_mean']
+ # mu_std = CapsSettings['friction_std']
+ # crit = CapsSettings['criticallity']
+ stressdrop_coeff = CapsSettings['stressdrop_coeff']
+ p_hydro = CapsSettings['phydro']
+ seed_density = CapsSettings['seed_density']
+ dw = CapsSettings['CAPS_sigma_distr']
+
+ # parameter uncertainties
+ if NSim > 1:
+ sigma1 = np.random.normal(loc=sigma1_mean, scale=0.2*sigma1_mean*dw, size=NSim)
+ sigma3 = np.random.normal(loc=sigma3_mean, scale=0.2*sigma3_mean*dw, size=NSim)
+ else:
+ sigma1 = np.array([sigma1_mean])
+ sigma3 = np.array([sigma3_mean])
+
+ total_events_all = np.zeros((NSim, len(Moments)))
+ total_rates_all = np.zeros((NSim, len(Moments) - 1))
+
+ for nrun in range(NSim):
+ theta_opt = np.pi / 4. + np.arctan(mu) / 2.
+
+ sigma_n_eff = (sigma1[nrun] + sigma3[nrun]) / 2. + ((sigma1[nrun] - sigma3[nrun]) / 2.) * np.cos(2 * theta_opt) - p_hydro
+ tau = np.abs(((sigma1[nrun] - sigma3[nrun]) / 2.) * np.sin(2 * theta_opt))
+
+ # if crit!=0:
+ # # attempt to account for criticallity definition
+ # min_fail_pres = cohesion / (mu - crit) + sigma_n_eff - tau / (mu - crit)
+ # min_fail_pres_std = np.sqrt(
+ # (np.square(sigma1_std) * np.square((1 + np.cos(2 * theta_opt) - np.sin(2 * theta_opt) / (mu - crit)) / 2.))
+ # + np.square(sigma3_std) * np.square((1 - np.cos(2 * theta_opt) + np.sin(2 * theta_opt) / (mu - crit)) / 2.))
+ #
+ # crit_check = NormalDist(mu=min_fail_pres, sigma=min_fail_pres_std).cdf(0) <= 0.05
+ #
+ # n_crit = crit + crit/10
+ # while crit_check == False:
+ # tau_temp = tau
+ # if (sigma_n_eff - min_fail_pres) > 0:
+ # tau = np.abs(cohesion + (mu - n_crit) * (sigma_n_eff - min_fail_pres))
+ # sigma_n_eff = min_fail_pres + (tau_temp - cohesion) / (mu - n_crit)
+ # else:
+ # tau = np.abs(cohesion + (mu + n_crit) * (sigma_n_eff - min_fail_pres))
+ # sigma_n_eff = min_fail_pres + (tau_temp - cohesion) / (mu + n_crit)
+ # min_fail_pres_temp = min_fail_pres
+ # min_fail_pres = cohesion / (mu - crit) + sigma_n_eff - tau / (mu - crit)
+ # if (min_fail_pres - min_fail_pres_temp) < 1.e-5:
+ # # print('Error: tau cannot get lower than cohesion: criticallity criterion is not met.')
+ # break
+ # crit_check = NormalDist(mu=min_fail_pres, sigma=min_fail_pres_std).cdf(0) <= 0.05
+ # n_crit = n_crit + crit / 10
+
+ fail_pres_mean = cohesion / mu + sigma_n_eff - tau / mu
+ # the standard deviation is calculated differently -> check this -> different than for criticallity
+ fail_pres_std = np.sqrt(
+ (np.square(sigma1_std) * np.square((1 + np.cos(2 * theta_opt) - np.sin(2 * theta_opt) / mu) / 2.))
+ + np.square(sigma3_std) * np.square((1 - np.cos(2 * theta_opt) + np.sin(2 * theta_opt) / mu) / 2.))
+
+ # differential stress:
+ #sigma_d = sigma1_eff - sigma3_eff
+
+ # scaling
+ seed_number = int(seed_density * np.max(Edges))
+ seed_spacebin = seed_number / len(MidEdges)
+
+ fn_cdf = np.zeros_like(max_pressure)
+ fn_cdf_rt = np.zeros_like(max_pressure)
+ tau_array = np.zeros((len(max_pressure[:,0])+1))
+ tau_array[0] = tau
+ for tt in range(len(Moments)):
+ fn_cdf[tt,:] += foldnorm.cdf(max_pressure[tt,:], np.abs(fail_pres_mean)/fail_pres_std, loc=0, scale=fail_pres_std)
+ if retriggering==True:
+ stressdrop = stressdrop_coeff * ((tau_array[tt] - cohesion) / mu)
+ if np.max(max_pressure[tt, :]) - fail_pres_mean > 0:
+ tau_array[tt + 1] = tau_array[tt] - (
+ stressdrop + mu * (np.max(max_pressure[tt, :]) - fail_pres_mean))
+ else:
+ tau_array[tt + 1] = tau_array[tt] - stressdrop
+ if tau_array[tt + 1] > tau_array[tt]:
+ tau_array[tt + 1] = tau_array[tt]
+ elif tau_array[tt + 1] < 0:
+ tau_array[tt + 1] = 0.
+ else:
+ continue
+ fail_pres_mean_trig = cohesion / mu + sigma_n_eff - tau_array[tt + 1] / mu
+ n_trig = int(np.sum(fn_cdf[tt, :])) / stressdrop_coeff
+ fn_cdf_rt[tt, :] += foldnorm.cdf(max_pressure[tt, :], np.abs(fail_pres_mean_trig) / fail_pres_std, loc=0, scale=fail_pres_std) * n_trig
+
+ fn_cdf = fn_cdf + fn_cdf_rt
+
+ total_events = np.zeros([len(Moments), len(MidEdges)])
+ for tt in range(len(Moments)):
+ total_events[tt], bins = np.histogram(MidEdges, bins=Edges, weights=fn_cdf[tt,:] * seed_spacebin)
+ total_rates = np.diff(total_events, axis=0)
+ total_events_all[nrun,:] = np.sum(total_events, axis=1)
+ total_rates_all[nrun,:] = np.sum(total_rates, axis=1)
+
+ return total_events_all, total_rates_all
+
+"""
+Seed Model (after Kiraly et al., 2016)
+"""
+@njit(nopython=True)
+def Seed_Model_linear(Moments, Edges, fm_r, fm_t, pres_t, SeedsSettings, NSim, retriggering=True):
+
+ #np.random.default_rng(seed=np.int64(datetime.now().timestamp()*1.e6))
+
+ # create the seed locations
+ max_ext = np.max(Edges) # furthest distance for the forecast
+ seed_density = SeedsSettings[0] # seed density
+ seed_number = int(seed_density * max_ext) # total number of seeds
+ Rcoord = max_ext * np.random.rand(seed_number * NSim) # placement of seeds
+
+ # get and rename parameters
+ sigma1_mean = SeedsSettings[1]
+ sigma3_mean = SeedsSettings[2]
+ sigma1_std = SeedsSettings[3]
+ sigma3_std = SeedsSettings[4]
+ cohesion = SeedsSettings[5]
+ # cohesion_std = SeedsSettings[5]
+ mu = SeedsSettings[6]
+ # mu_std = SeedsSettings[2]
+ crit = SeedsSettings[7]
+ # theta_std = SeedsSettings[10]
+ stressdrop_coeff = SeedsSettings[8]
+ p_hydro = SeedsSettings[9]
+
+ # b-values and corresponding differential stresses
+ b_min = SeedsSettings[10]
+ b_max = SeedsSettings[11]
+ b_min_diff_stress = SeedsSettings[12]
+ b_max_diff_stress = SeedsSettings[13]
+
+ Mc = SeedsSettings[14]
+
+ # setting arrays to be calculated
+ # optimal orientation of each seed
+ theta_opt = np.pi / 4. + np.arctan(mu) / 2.
+ sigma1 = sigma1_mean + sigma1_std * np.random.randn(seed_number*NSim)
+ sigma3 = sigma3_mean + sigma3_std * np.random.randn(seed_number*NSim)
+
+ sig_check = np.where((sigma3 - p_hydro) > (sigma1 - p_hydro))
+ while len(sig_check[0]):
+ sigma1[sig_check] = sigma1_mean + sigma1_std * np.random.randn(len(sig_check[0]))
+ sigma3[sig_check] = sigma3_mean + sigma3_std * np.random.randn(len(sig_check[0]))
+ sig_check = np.where((sigma3 - p_hydro) > (sigma1 - p_hydro))
+
+ sigma_n_eff = ((sigma1 + sigma3) / 2. + ((sigma1 - sigma3) / 2.) * np.cos(2. * theta_opt)) - p_hydro
+ tau = np.abs((sigma1 - sigma3) / 2. * np.sin(2. * theta_opt))
+
+ crit_check = np.where(cohesion / (mu - crit) + sigma_n_eff - tau / (mu - crit) < 0.)
+ while len(crit_check[0]):
+ tau_temp = tau[crit_check]
+ tau[crit_check] = np.abs(cohesion + (mu - crit) * sigma_n_eff[crit_check])
+ sigma_n_eff[crit_check] = (tau_temp - cohesion) / (mu - crit)
+ crit_check = np.where(cohesion / (mu - crit) + sigma_n_eff - tau / (mu - crit) < 0.)
+
+ fail_pressure = cohesion / mu + sigma_n_eff - tau / mu
+
+ # MidEdges = np.zeros(len(Edges) - 1)
+ # for i in range(len(MidEdges)):
+ # MidEdges[i] = (Edges[i] + Edges[i + 1]) / 2.
+
+ diff_stress = (sigma1 - sigma3) - p_hydro
+
+ # open lists of parameters:
+ b_val = []
+ mag = []
+ time = []
+ R = []
+ pressure = []
+ trig_index = []
+ differential_stress = []
+ sig1 = []
+ sig3 = []
+ stress_drop = []
+
+ Rind = np.argmin(np.abs(fm_r - np.reshape(Rcoord, (len(Rcoord), 1))).T, axis=0)
+
+ # comment previous and uncomment next 3 lines to make it work in a different environment than in the pycharm setup
+ #loc_dist = np.abs(fm_r - np.reshape(Rcoord, (len(Rcoord), 1)))
+ #Rind = np.empty((len(Rcoord)))
+ #for i in range(len(Rcoord)): Rind[i] = (np.argmin(loc_dist[i]))
+
+ events_map = np.ravel((np.ones((seed_number, NSim)) * np.array(list(range(0, NSim)))))
+
+ pres_map = np.empty((len(Moments), len(Rind)))
+ for j in range(len(Rind)): pres_map[:, j] = pres_t[int(Rind[j]), :]
+
+ # total_events = np.zeros((NSim, len(Moments), len(MidEdges)))
+ total_events = np.zeros((NSim, len(Moments)))
+
+ t_events = 0
+ tt = 0
+
+ for t in range(len(Moments)):
+ # for t in Moments:
+ OverShootPressure = pres_map[t] - fail_pressure
+ # OverShootPressure = pres_func(Rcoord, t) - fail_pressure
+ trig_event_tbin = np.where(OverShootPressure > 0)
+ trig_event_total_array = trig_event_tbin[0]
+
+ if len(trig_event_total_array):
+ # go over all triggered seeds
+ for i in np.nditer(trig_event_total_array):
+ # if differential stress is higher than the differential stress corresponding to b_min
+ if diff_stress[i] > b_min_diff_stress:
+ b_value = b_min
+ else:
+ # otherwise find the corresponding b-value
+ mm = ((b_max - b_min) / (b_max_diff_stress - b_min_diff_stress))
+ qq = b_max - mm * b_max_diff_stress
+ b_value = mm * diff_stress[i] + qq
+
+ # idx_pos = np.argmin(np.abs(MidEdges - Rcoord[i]))
+ # total_events[int(event_map[i]), t_events, idx_pos] += 1
+ total_events[int(events_map[i]), t_events] += 1
+
+ # save for each triggered seed into the catalog the...
+ # b-value
+ b_val.append(b_value)
+ # magnitude
+ mag.append(Mc + np.random.exponential(1. / (b_value * np.log(10))))
+ # time
+ time.append(t)
+ # x-location
+ R.append(Rcoord[i])
+ # pressure
+ pressure.append(OverShootPressure[i] + fail_pressure[i])
+ # triggering index
+ trig_index.append(float(i + 1))
+ # differential stress
+ differential_stress.append(diff_stress[i])
+ # maximum effective principal stress
+ sig1.append(sigma1[i] - p_hydro)
+ # minimum effective principal stress
+ sig3.append(sigma3[i] - p_hydro)
+
+ if retriggering == True:
+ "1)"
+ # the stress drop is proportional to the normal stress
+ stressdrop = stressdrop_coeff * (sigma_n_eff[i] - fail_pressure[i])
+ # stressdrop = stressdrop_coeff * (((tau[i] - cohesion) / mu))
+ # shear stress on seeds after triggering
+ tau[i] = tau[i] - (stressdrop + mu * OverShootPressure[i])
+ # change/reduction in the maximum principal stress
+ sigma1[i] = sigma1[i] - 2 * (stressdrop + mu * OverShootPressure[i]) / np.abs(np.sin(2 * theta_opt))
+ # differential stress on seeds after triggering
+ diff_stress[i] = (sigma1[i] - sigma3[i]) - p_hydro
+ # normal effective stress on seeds after triggering (no effect for now)
+ # sigma_n_eff[i] = (sigma1[i] + sigma3[i])/2. + ((sigma1[i] - sigma3[i]) * np.cos(2*theta_opt) / 2.) - p_hydro
+ fail_pressure[i] = cohesion / mu + sigma_n_eff[i] - tau[i] / mu
+ # stressdrop
+ stress_drop.append(stressdrop)
+ else:
+ fail_pressure[i] = random.random() * np.square(np.max(pres_t))
+ # The following lines are commented (not really necessary) as they do not work with NUMBA...
+ # ind = np.where(fail_pressure[i] < np.max(pres_sol))
+ # while len(ind[0]):
+ # fail_pressure[i] = random.random() * np.square(np.max(pres_sol))
+ # ind = np.where(fail_pressure[i] < np.max(pres_sol))
+ stress_drop.append(0)
+
+ t_events = t_events + 1
+ if not t_events >= len(Moments):
+ # total_events[:, t_events, :] = total_events[:, t_events - 1, :]
+ total_events[:, t_events] = total_events[:, t_events - 1]
+ tt = tt + 1
+
+ EQ = (b_val, mag, time, R, pressure, trig_index, differential_stress, sig1, sig3, stress_drop)
+ total_rates = np.diff(total_events)
+ # total_events_noloc = np.sum(total_events, axis=2)
+ # total_rates_noloc = np.diff(total_events_noloc, axis=1)
+
+ return EQ, total_events, total_rates
+
+"""
+COMMENTS:
+1) The version: stressdrop = stressdrop_coeff * (((tau[i] - cohesion) / mu) + fail_pressure[i]),
+is not correct because the stressdrop is related to the normal stress (not effective normal stress and also not to the
+normal stress reduction necessary to reach failure). Then, the stress drop is directly related to the normal stress but
+the magnitude of reduction in the shear stress is actually related also to the amount of overpressure for that given seed.
+If the overpressure is large, tau will experience a larger reduction.
+2)
+"""
diff --git a/hm1d/Worker_HM0_HM1_1D.py b/hm1d/Worker_HM0_HM1_1D.py
new file mode 100644
index 0000000..c27c833
--- /dev/null
+++ b/hm1d/Worker_HM0_HM1_1D.py
@@ -0,0 +1,1322 @@
+import numpy as np
+import copy
+from utils import (Calibrate_Observed_GR, b_value_Lasso_calibration_1D, Refine_An_Edge,
+ LL_cost_function_1D, LS_cost_function_1D)
+
+
+from FlowModel import FlowModel0, FlowModel1, FlowModel2, FlowModel3, FlowModel4
+
+from SeismicityModel import Caps_Model_linear as CAPS
+from SeismicityModel import Seed_Model_linear as SEED
+from scipy.optimize import differential_evolution
+from scipy.interpolate import CloughTocher2DInterpolator
+
+import numba as nb
+
+
+class HM01:
+ def __init__(self,
+ TrainingData,
+ time_col='time',
+ flow_col='flow',
+ press_col='pressure',
+ source_times_col='ts',
+ hypocenter_cols=['Rs'],
+ Mw_cols='mag',
+ dM = 0.01,
+ max_iteration = -1,
+ cost_fun = 'LS',
+ inv_mod='seq',
+ seis_model='CAPS',
+ retriggering = False,
+ forward_model=False, # True: FWD, False: Inversion mode
+ flow_model='line_source',
+ params_method='Chi2crt',
+ NSim=1,
+ seed_model_version='1',
+ workers=1,
+ opt_algorithm='DE',
+ max_grid_ext=300,
+ layer_thick = 100,
+ cross_area = 94,
+ sensitivity_analysis=False,
+ init_params = [],
+ Settings=[]):
+
+ self.Columns = [time_col, flow_col, press_col]
+
+ self.opt_algorithm = opt_algorithm
+ self.sensitivity_analysis = sensitivity_analysis
+ self.workers = workers
+ self.retriggering = retriggering
+ self.forward_model = forward_model
+
+ # self.TrainingData = None
+ self.TrainingData = TrainingData
+ self.training_catalog = None
+
+ self.ts = source_times_col
+ self.Xs = hypocenter_cols
+ self.Mw = Mw_cols
+ self.mc_bin_size = dM
+
+ self.cost_fun = cost_fun
+ listOfStrings = ['LL' , 'LS']
+ if self.cost_fun not in listOfStrings :
+ print("Cost function '",self.cost_fun,"' not found. Possible values are:", listOfStrings,". Using default mode (LL).")
+ self.cost_fun='LL'
+
+ self.inv_mod = inv_mod
+ listOfStrings = ['seq' , 'joint']
+ if self.inv_mod not in listOfStrings :
+ print("Inversion mode '",self.cost_fun,"' not found. Possible values are:", listOfStrings,". Using default mode (seq).")
+ self.inv_mod='seq'
+
+ self.flw_model = flow_model
+ listOfStrings = ['point_source' , 'line_source', 'num_const_perm', 'num_var_perm', 'num_var_perm_FV']
+ if self.flw_model not in listOfStrings :
+ print("Fluid model '",self.cost_fun,"' not found. Possible values are:", listOfStrings,". Using default mode (line_source).")
+ self.flw_model='line_source'
+
+ self.NSim=NSim
+ self.seed_model_version = seed_model_version
+ self.workers=workers
+ self.seis_model = seis_model
+ self.params_method = params_method
+
+ self.seed_settings = Settings[0]
+ self.seis_bounds = Settings[1]
+ self.flow_bounds = Settings[2]
+ self.ExSolSettings = Settings[3]
+ self.CAPSSettings = Settings[4]
+ self.SEEDSettings = Settings[4]
+
+ self.CostFunction = None
+ self.LL_Edges = None
+ self.LL_Moments = None
+ self.Training_MaxPressure = None
+ self.Training_XYZ = None
+ self.Training_Volume = None
+
+ # input for the flow models
+ self.max_grid_ext = max_grid_ext
+ self.layer_thick = layer_thick
+ self.cross_area = cross_area
+
+ self.maxbval_bounds = [(1., self.seed_settings["b_max"])] # upper b-value limit
+
+ if self.retriggering == True:
+ if self.seed_settings['criticallity']!=0:
+ self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'stressdrop_coeff', 'cohesion_mean', 'friction_mean', 'seed_density']
+ self.SEISbounds = [(self.seis_bounds['sigma1_std'][0], self.seis_bounds['sigma1_std'][1]),
+ (self.seis_bounds['sigma3_std'][0], self.seis_bounds['sigma3_std'][1]),
+ (self.seis_bounds['stressdrop_coeff'][0], self.seis_bounds['stressdrop_coeff'][1]),
+ # (self.seis_bounds['criticallity'][0], self.seis_bounds['criticallity'][1]),
+ (self.seis_bounds['cohesion_mean'][0], self.seis_bounds['cohesion_mean'][1]),
+ (self.seis_bounds['friction_mean'][0], self.seis_bounds['friction_mean'][1]),
+ (self.seis_bounds['seed_density'][0], self.seis_bounds['seed_density'][1])]
+ self.logscale = [False, False, False, False, False, False]
+ else:
+ # self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'stressdrop_coeff', 'friction_mean', 'seed_density']
+ self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'stressdrop_coeff', 'cohesion_mean', 'friction_mean', 'seed_density']
+ self.SEISbounds = [(self.seis_bounds['sigma1_std'][0], self.seis_bounds['sigma1_std'][1]),
+ (self.seis_bounds['sigma3_std'][0], self.seis_bounds['sigma3_std'][1]),
+ (self.seis_bounds['stressdrop_coeff'][0], self.seis_bounds['stressdrop_coeff'][1]),
+ (self.seis_bounds['cohesion_mean'][0], self.seis_bounds['cohesion_mean'][1]),
+ (self.seis_bounds['friction_mean'][0], self.seis_bounds['friction_mean'][1]),
+ (self.seis_bounds['seed_density'][0], self.seis_bounds['seed_density'][1])]
+ self.logscale = [False, False, False, False, False, False]
+ else:
+ if self.seed_settings['criticallity']!=0:
+ # self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'friction_mean', 'seed_density']
+ self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'friction_mean', 'cohesion_mean', 'seed_density']
+ # self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'criticallity', 'friction_mean', 'cohesion_mean', 'seed_density']
+ self.SEISbounds = [(self.seis_bounds['sigma1_std'][0], self.seis_bounds['sigma1_std'][1]),
+ (self.seis_bounds['sigma3_std'][0], self.seis_bounds['sigma3_std'][1]),
+ # (self.seis_bounds['criticallity'][0], self.seis_bounds['criticallity'][1]),
+ (self.seis_bounds['friction_mean'][0], self.seis_bounds['friction_mean'][1]),
+ (self.seis_bounds['cohesion_mean'][0], self.seis_bounds['cohesion_mean'][1]),
+ (self.seis_bounds['seed_density'][0], self.seis_bounds['seed_density'][1])]
+ # self.logscale = [False, False, False, False]
+ self.logscale = [False, False, False, False, False]
+ # self.logscale = [False, False, False, False, False, False]
+ else:
+ self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'cohesion_mean', 'friction_mean', 'seed_density']
+ # self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'friction_mean', 'seed_density']
+ # self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'cohesion_mean', 'seed_density']
+ self.SEISbounds = [(self.seis_bounds['sigma1_std'][0], self.seis_bounds['sigma1_std'][1]),
+ (self.seis_bounds['sigma3_std'][0], self.seis_bounds['sigma3_std'][1]),
+ (self.seis_bounds['cohesion_mean'][0], self.seis_bounds['cohesion_mean'][1]),
+ (self.seis_bounds['friction_mean'][0], self.seis_bounds['friction_mean'][1]),
+ (self.seis_bounds['seed_density'][0], self.seis_bounds['seed_density'][1])]
+ self.logscale = [False, False, False, False, False]
+ # self.logscale = [False, False, False, False]
+
+ """
+ 4 different flow models can be choosen:
+ -> point_source: analytical point source solution. Two parameters, storage coefficient and permeability, are calibrated.
+ -> line_source: Theis' solution. Two parameters, storage coefficient and permeability, are calibrated.
+ -> num_const_perm: numerical solution without permeability changes. Only the permeability is calibrated and is a fixed value.
+ -> num_var_perm: numerical solution with permeability changes. Three parameters are calibrated that control the changes in permeability with pressure.
+ """
+
+ if self.flw_model == 'point_source':
+ self.OptimizeKeys = self.OptimizeKeys + ['storage', 'permeability'] # parameters to calibrate
+ self.FlowBounds = [(self.flow_bounds['storage'][0], self.flow_bounds['storage'][1]),
+ (self.flow_bounds['permeability'][0], self.flow_bounds['permeability'][1])]
+ self.logscale = self.logscale + [True, True]
+ # activate the flow model:
+ self.flow_model = FlowModel0(self.TrainingData[self.Columns[:2]].values,
+ np.geomspace(0.1, self.max_grid_ext, 30, endpoint=True), self.cross_area,
+ bounds=self.FlowBounds)
+
+ elif self.flw_model == 'line_source':
+ self.OptimizeKeys = self.OptimizeKeys + ['storage', 'permeability']
+ self.FlowBounds = [(self.flow_bounds['storage'][0], self.flow_bounds['storage'][1]),
+ (self.flow_bounds['permeability'][0], self.flow_bounds['permeability'][1])]
+ self.logscale = self.logscale + [True, True]
+ # activate the flow model:
+ self.flow_model = FlowModel1(self.TrainingData[self.Columns[:2]].values,
+ np.geomspace(0.1, self.max_grid_ext, 30, endpoint=True), self.layer_thick,
+ bounds=self.FlowBounds, iniguess=(self.seed_settings['storage'], self.seed_settings['permeability']))
+
+ elif self.flw_model == 'num_const_perm':
+ self.OptimizeKeys = self.OptimizeKeys + ['storage', 'permeability']
+ self.FlowBounds = [(self.flow_bounds['storage'][0], self.flow_bounds['storage'][1]),
+ (self.flow_bounds['permeability'][0], self.flow_bounds['permeability'][1])]
+ self.logscale = self.logscale + [True, True]
+ # activate the flow model:
+ self.flow_model = FlowModel2(self.TrainingData[self.Columns[:2]].values,
+ np.geomspace(0.1, self.max_grid_ext, 30, endpoint=True), self.layer_thick,
+ self.seed_settings, bounds=self.FlowBounds)
+
+ elif self.flw_model == 'num_var_perm': # HM1 class with FD
+ self.OptimizeKeys = self.OptimizeKeys + ['zero_stress_aperture', 'residual_aperture','frac_normal_stiffness','storage'] # parameters to calibrate
+ # self.FlowBounds = [(self.seed_settings['zero_stress_aperture'], -2), # zero-stress aperture
+ # (self.seed_settings['residual_aperture'], -7), # residual aperture
+ # (self.seed_settings['frac_normal_stiffness'], 14), # fracture normal stiffness
+ # (-9, -7)] # storage
+ self.FlowBounds = [(self.flow_bounds['zero_stress_aperture'][0], self.flow_bounds['zero_stress_aperture'][1]),
+ (self.flow_bounds['residual_aperture'][0], self.flow_bounds['residual_aperture'][1]),
+ (self.flow_bounds['frac_normal_stiffness'][0], self.flow_bounds['frac_normal_stiffness'][1]),
+ (self.flow_bounds['storage'][0], self.flow_bounds['storage'][1])]
+ self.logscale = self.logscale + [True, True, True, True]
+ # activate the flow model:
+ self.flow_model = FlowModel3(self.TrainingData[self.Columns[:2]].values,
+ np.geomspace(0.1, self.max_grid_ext, 30, endpoint=True), self.layer_thick,
+ self.seed_settings, bounds=self.FlowBounds)
+
+ else: # HM1 cass with FV
+ # self.OptimizeKeys = self.OptimizeKeys + ['zero_stress_aperture', 'residual_aperture', 'shearing_aperture_factor', 'shear_scaling_factor', 'storage'] # parameters to calibrate
+ self.OptimizeKeys = self.OptimizeKeys + ['zero_stress_aperture', 'residual_aperture', 'shearing_aperture_factor', 'max_shear_frac_aperture', 'shear_scaling_factor', 'storage']
+ self.FlowBounds = [(self.flow_bounds['zero_stress_aperture'][0], self.flow_bounds['zero_stress_aperture'][1]),
+ (self.flow_bounds['residual_aperture'][0], self.flow_bounds['residual_aperture'][1]),
+ (self.flow_bounds['shearing_aperture_factor'][0], self.flow_bounds['shearing_aperture_factor'][1]),
+ (self.flow_bounds['max_shear_frac_aperture'][0], self.flow_bounds['max_shear_frac_aperture'][1]),
+ (self.flow_bounds['shear_scaling_factor'][0], self.flow_bounds['shear_scaling_factor'][1]),
+ (self.flow_bounds['storage'][0], self.flow_bounds['storage'][1])]
+ # self.logscale = self.logscale + [True, True, True, True, True]
+ self.logscale = self.logscale + [True, True, True, True, True, True]
+ # activate the flow model:
+ self.flow_model = FlowModel4(self.TrainingData[self.Columns[:2]].values,
+ np.geomspace(0.1, self.max_grid_ext, 30, endpoint=True), self.layer_thick,
+ self.seed_settings, bounds=self.FlowBounds, forward_model=forward_model,
+ iniguess=(self.seed_settings['zero_stress_aperture'], self.seed_settings['residual_aperture'],
+ self.seed_settings['shearing_aperture_factor'], self.seed_settings['max_shear_frac_aperture'],
+ self.seed_settings['shear_scaling_factor'], self.seed_settings['storage']))
+
+ if seis_model=='SEED':
+ self.SEEDmodelKeys = ['seed_density', 'inter_sigma1', 'inter_sigma3', 'sigma1_std', 'sigma3_std',
+ 'cohesion_mean', 'friction_mean', 'criticallity', 'stressdrop_coeff', 'phydro',
+ 'b_min', 'b_max', 'b_min_diff_stress', 'b_max_diff_stress', 'Mag_Completeness']
+
+ self.disp = True
+ self.maxiter = max_iteration
+ self.seed = 42
+
+ # for callback
+ self.best_x = None
+ self.minf = np.inf
+
+ # self.init_params = [5, 0.2, 5.2, 0.44]
+
+ # currently not used:
+ # if self.opt_algorithm=='LMFIT':
+ # params = Parameters()
+ # for i in range(len(self.OptimizeKeys)-1):
+ # params.add(self.OptimizeKeys[i], self.init_params[i], min=self.SEISbounds[i][0], max=self.SEISbounds[i][1])
+ # self.SEISparams = params
+
+ def update_seeds_settings(self):
+ # would you rather the Mc was calculated or gained
+ # from the QuakeMl data?
+ Mc, b_value = Calibrate_Observed_GR(
+ self.training_catalog, self.mc_bin_size, self.Mw)
+ self.seed_settings["Mag_Completeness"] = Mc
+ self.seed_settings["b_value"] = b_value
+ self.training_catalog = self.training_catalog[
+ self.training_catalog[self.Mw] >= Mc]
+
+ if (self.seed_settings["b_vs_depth_range"]):
+ a, m = b_value_Lasso_calibration_1D(
+ np.array(self.training_catalog[self.Xs].values),
+ np.array(self.training_catalog[[self.Mw]])[:, 0],
+ bins=100)
+ else:
+ a, m = b_value, np.zeros(1)
+
+ self.seed_settings["a"] = a
+ self.seed_settings["m"] = m
+
+ def Seed_max_bval_fit(self, x):
+ b_min = self.seed_settings['b_min']
+ # to be optimized
+ b_max = x
+ self.seed_settings['b_max'] = b_max
+ b_max_diff_stress = self.seed_settings['b_max_diff_stress']
+ b_min_diff_stress = self.seed_settings['b_min_diff_stress']
+ # mean differential stress from seeds
+ diff_stress_mean = self.seed_settings['diff_stress_mean']
+ # b-value from observed data
+ b_val_obs = self.seed_settings['b_value']
+
+ mm = ((b_max - b_min)/(b_max_diff_stress - b_min_diff_stress))
+ qq = b_max - mm * b_max_diff_stress
+ b_val_model = mm * diff_stress_mean + qq
+
+ b_val_diff = np.abs(b_val_obs-b_val_model)
+
+ return b_val_diff
+
+ def ll_score(self, x):
+ y = self.CostFunction.y
+ log_factorial_y = self.CostFunction.logyfact
+ TestSettings = copy.deepcopy(self.seed_settings)
+
+ for i in range(len(self.SEISbounds)):
+ TestSettings[self.OptimizeKeys[i]] = x[i]
+ if (self.logscale[i]):
+ TestSettings[self.OptimizeKeys[i]] = np.power(10., x[i])
+
+ y0 = CAPS.Caps_Model_linear(self.LL_Moments[1:], self.LL_Edges[self.LL_Edges >= 0.], self.Training_R,
+ self.Training_MaxPressure, TestSettings, NSim=1, retriggering=self.retriggering)[0]
+
+ LL = np.sum(y * np.log(y0) - y0 - log_factorial_y)/len(self.training_catalog)# Poissonian LL
+ #LL = np.sum(y * np.log10(y1) - y1 - log_factorial_y) / float(len(y1))
+ if np.isfinite(LL):
+ return -LL
+ return 1.e20
+
+ def ls_score(self, x):
+ y = self.CostFunction.y
+ TestSettings = copy.deepcopy(self.seed_settings)
+
+ for i in range(len(self.SEISbounds)):
+ TestSettings[self.OptimizeKeys[i]] = x[i]
+ if (self.logscale[i]):
+ TestSettings[self.OptimizeKeys[i]] = np.power(10., x[i])
+
+ y0 = CAPS.Caps_Model_linear(self.LL_Moments[1:], self.LL_Edges[self.LL_Edges >= 0.], self.Training_R,
+ self.Training_MaxPressure, TestSettings, NSim=1, retriggering=self.retriggering)[0]
+
+ LS = np.sum(np.square(y-y0))/len(y)
+ if np.isfinite(LS):
+ if LS < self.minf:
+ self.minf = LS
+ self.best_x = x
+ return LS
+ return 1.e20
+
+ def ls_score_seed(self, x):
+ y = self.CostFunction.y
+ TestSettings = copy.deepcopy(self.seed_settings)
+
+ for i in range(len(self.SEISbounds)):
+ TestSettings[self.OptimizeKeys[i]] = x[i]
+ if (self.logscale[i]):
+ TestSettings[self.OptimizeKeys[i]] = np.power(10., x[i])
+
+ # typed.List instance which is a compiled library backed, type-homogeneous list data type that is an improvement over the reflected-list: SeedsSettings = []
+ SeedsSettings = nb.typed.List()
+ for j in range(len(self.SEEDmodelKeys)):
+ SeedsSettings.append(float(TestSettings[self.SEEDmodelKeys[j]]))
+
+ y0 = SEED.Seed_Model_linear(self.LL_Moments[1:], self.LL_Edges[self.LL_Edges >= 0.],
+ self.flow_model.r, self.flow_model.Q[:,0],
+ self.pres_t_fit,
+ SeedsSettings, self.Nreal, self.retriggering)[1]
+
+ y1 = np.median(y0, axis=0)
+
+ LS = np.sum(np.square(y - y1)) / len(y)
+
+ if np.isfinite(LS):
+ if LS < self.minf:
+ self.minf = LS
+ self.best_x = x
+ return LS
+ return 1.e20
+
+ def ll_score_joint(self, x):
+
+ TestSettings = copy.deepcopy(self.seed_settings)
+ #print(x)
+ for i in range(len(x)):
+ TestSettings[self.OptimizeKeys[i]] = x[i]
+ if (self.logscale[i]):
+ TestSettings[self.OptimizeKeys[i]] = np.power(10., x[i])
+ #end_time = time.time()
+
+ #self.flow_model.params = TestSettings[self.OptimizeKeys[4]], TestSettings[self.OptimizeKeys[5]]
+ self.flow_model.params = TestSettings[self.OptimizeKeys[4]]
+
+ self.flow_model.hidden_mechanism = self.flow_model.predict()
+
+ Coeff_R2 = np.sum(np.square(self.flow_model.hidden_mechanism[:,0]/1.e6 -
+ self.flow_model.observation.T/1.e6))/len(self.flow_model.observation)
+
+ self.timesteps = self.TrainingData.time.values
+ inipres=self.TrainingData[self.Columns[-1]].values[0]
+ self.PressureFunction = self.create_interpolator(self.timesteps,(self.flow_model.hidden_mechanism-inipres)/1.e6)
+
+ (self.Training_MaxPressure,
+ self.Training_XYZ,
+ self.Training_Volume) = self.Interpolate_1D(self.LL_Moments, self.LL_Edges,
+ self.ExSolSettings, self.CAPSSettings)
+
+
+ y1 = CAPS.Forecast_BinnedRate_only_1D(
+ self.LL_Moments[1:], self.LL_Edges, self.Training_MaxPressure,
+ self.Training_XYZ, self.Training_Volume, TestSettings)
+ #end_time = time.time() - end_time
+ #print("Elapsed time to run Forecast_Binned_rate was %g seconds" % (end_time))
+
+ y0=np.diff(y1,axis=(0))
+ y1=np.zeros(y1.shape)
+ y1[1:,:]=y0
+ y1 = y1.ravel()
+ y1[np.where(y1 <= 0.)] = 1.e-20
+
+ y = self.SeisCostFunction.y
+ log_factorial_y = self.SeisCostFunction.logyfact
+
+ LL = -np.sum(y * np.log(y1) - y1 - log_factorial_y)/len(self.training_catalog)
+ Cost = LL+Coeff_R2
+ #LL = np.sum(y * np.log10(y1) - y1 - log_factorial_y) / float(len(y1))
+ if np.isfinite(Cost):
+ return Cost
+ return 1.e20
+
+ def ls_score_joint(self, x):
+
+ TestSettings = copy.deepcopy(self.seed_settings)
+ #print(x)
+ for i in range(len(x)):
+ TestSettings[self.OptimizeKeys[i]] = x[i]
+ if (self.logscale[i]):
+ TestSettings[self.OptimizeKeys[i]] = np.power(10., x[i])
+ #end_time = time.time()
+
+ self.flow_model.params = TestSettings[self.OptimizeKeys[4]], TestSettings[self.OptimizeKeys[5]]
+ #self.flow_model.params = TestSettings[self.OptimizeKeys[4]]
+
+ self.flow_model.observation = self.TrainingData[self.Columns[::2]].values[:, 1:]
+
+ self.flow_model.hidden_mechanism = self.flow_model.predict()
+
+ Coeff_R2 = np.sum(np.square(self.flow_model.hidden_mechanism[:,0]/1.e6 -
+ self.flow_model.observation.T/1.e6))/np.max(self.flow_model.observation/1.e6)
+
+ self.timesteps = self.TrainingData.time.values
+ inipres=self.TrainingData[self.Columns[-1]].values[0]
+ self.PressureFunction = self.create_interpolator(self.timesteps,(self.flow_model.hidden_mechanism-inipres)/1.e6)
+
+ (self.Training_MaxPressure,
+ self.Training_XYZ,
+ self.Training_Volume) = self.Interpolate_1D(self.LL_Moments, self.LL_Edges,
+ self.ExSolSettings, self.CAPSSettings)
+
+ y0 = CAPS.Forecast_BinnedRate_only_1D(
+ self.LL_Moments[1:], self.LL_Edges, self.Training_MaxPressure,
+ self.Training_XYZ, self.Training_Volume, TestSettings)
+
+ y1 = np.sum(y0, axis=1)
+
+ #end_time = time.time() - end_time
+ #print("Elapsed time to run Forecast_Binned_rate was %g seconds" % (end_time))
+
+ y = self.SeisCostFunction.y
+
+ LS = np.sum(np.square(y-y1))/len(y)
+
+ Cost = LS+Coeff_R2
+ #LL = np.sum(y * np.log10(y1) - y1 - log_factorial_y) / float(len(y1))
+ if np.isfinite(Cost):
+ return Cost
+ return 1.e20
+
+ def save_iteration_step(self, x, convergence):
+ """
+ function to save history of parameters and residual during DEA
+ """
+ # check if equal
+ #np.testing.assert_equal(x, self.best_x)
+ self.dea_vals.append(x)
+ self.dea_func.append(self.minf)
+ return
+
+ def create_interpolator(self, t_grid, solution):
+ if len(self.flow_model.r)==len(solution[0]):
+ mesh = np.meshgrid(self.flow_model.r, t_grid)
+ else:
+ mesh = np.meshgrid(self.flow_model.r[1:], t_grid)
+ interpd = CloughTocher2DInterpolator(np.array([mesh[0].ravel(),mesh[1].ravel()]).T, solution.ravel(), fill_value=0)
+ return interpd
+
+ def mask_for_median(self, events_grid):
+
+ total_events_spacesum = np.sum(events_grid, axis=0)
+ total_events_median_new = np.zeros_like(total_events_spacesum)
+ total_events_median_new_single = np.zeros_like(total_events_spacesum[:,0])
+
+ for i in range(len(total_events_spacesum)):
+ if np.sum(total_events_spacesum[i])==0:
+ total_events_median_new[i] = total_events_spacesum[i]
+ total_events_median_new_single[i] = np.median(total_events_median_new[i], axis=0)
+ else:
+ masked = np.ma.masked_where(total_events_spacesum[i]==0, total_events_spacesum[i])
+ total_events_median_new_single[i] = np.ma.median(masked)
+
+ return total_events_median_new_single
+
+ def Interpolate_1D(self, Forecasts_Moments, Forecasts_Edges, CAPS_KD_Settings):
+ """
+ Same as CAPS.InterpolateMaximumPressure, but using a self.PressureFunction created by the fit
+ """
+ RR=CAPS_KD_Settings["RefinementRatio"]
+ MyR=Refine_An_Edge(Forecasts_Edges, RR) # for 1D we use directly the cartesian grid as the radius
+ LaterKeepIndices=np.logical_and(np.isfinite(MyR), MyR > 0.)
+ My_Working_R =MyR[LaterKeepIndices]
+ DT=np.concatenate((self.timesteps, Forecasts_Moments))
+ MyT=np.sort(np.unique(DT))
+ MyPressure, MaxPressureAll =np.zeros([2, len(MyT), len(My_Working_R)])
+ PM=np.zeros(len(My_Working_R))
+ for timeIter in range(len(MyT)):
+ ConsiderTime=MyT[timeIter]
+ MyPressure[timeIter,:]=self.PressureFunction(My_Working_R,ConsiderTime)
+ MaxPressureAll[timeIter,:]=np.maximum(PM, MyPressure[timeIter,:])
+ PM=MaxPressureAll[timeIter,:]
+
+ TimeBinIndeces=np.intersect1d(Forecasts_Moments, MyT, return_indices=True)[2] # discard unecessary time discretization now
+ MaxPressure=MaxPressureAll[TimeBinIndeces]
+ dV = np.unique(np.diff(Forecasts_Edges, axis=0))
+ #dV=np.unique(np.diff(Forecasts_Edges, axis=0), axis=0)
+ #if(len(dV)>1):
+ #print ("Warning: non equidistant edges have been passed in the one direction. Correct array of control distances.")
+ #\todo: assume equidistant edges and refinement
+ My_Cell_Width=np.prod(np.amin(dV,axis=0)/RR)
+ return MaxPressure, My_Working_R, np.ones(len(My_Working_R))*My_Cell_Width
+
+ def ls_params_score(self, percent):
+
+ par_grd_2 = []
+ for p in range(len(self.CAPSbounds)):
+ # par_distr = self.Params_CAPS(forecast_edges, max_pressure_past, working_xyz_past, cells_volume_past, p)
+ par_distr = self.Params_CAPS_2(percent, p)
+ par_grd_2.append(par_distr)
+ cal_val_2 = np.median(par_grd_2, axis=1)
+
+ LS = np.sum(np.square(self.cal_val-cal_val_2))/len(self.cal_val)
+
+ if np.isfinite(LS):
+ return LS
+ return 1.e20
+
+ # def sensitivity_analysis_fun(self, params_distr, z_i, f_z_i, forecast_edges, method='sobol_global'):
+ #
+ # if method == 'sobol_global':
+ # """
+ # GLOBAL SENSITIVITY ANALYSIS
+ #
+ # Ref. -> E. Qian, B. Peherstorfer, D. O'Malley, V. V. Vesselinov, and K. Willcox.
+ # Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices
+ # SIAM/ASA J. Uncertainty Quantification. Vol. 6, No. 2, pp. 683-706
+ #
+ # Explanation parameters:
+ # params_distr -> distribution of the input parameters
+ # z_i -> values of parameters from FIRST random sampling (from NSim Monte Carlo simulations)
+ # f_z_i -> output of the model from z_i
+ # z_i_ -> values of input parameters from SECOND random sampling (from NSim Monte Carlo simulations)
+ # f_z_i_ -> output of the model from z_i_
+ # y_i_j -> same as z_i_ but fixing the one input parameter of interest from z_i
+ # f_y_i_j -> output of the model from y_i_j
+ #
+ # E -> sample mean (mean of the entire output, over all times and space)
+ # V -> variance of all inputs
+ # V_j -> variance contributed by the input j
+ # T_j -> total variance contributed by the input j
+ # s_j -> Sobol' main sensitivity index for input j (portion of the output that can be attributed to the influence of the input j only, fraction of variance of input j alone)
+ # also: first order sensitivity indices
+ # s_j_t -> Sobol' total effect sensitivity index for input j (additionally takes into account the sum of the contributions)
+ # also: total-order sensitivity indices
+ # """
+ #
+ # z_i = np.array(z_i).T
+ #
+ # f_z_i_copy = np.array(f_z_i)
+ # f_z_i_new = []
+ # for l in range(len(f_z_i)):
+ # f_z_i_new.append(np.sum(f_z_i_copy[l][-1]))
+ # f_z_i = np.array(f_z_i_new)
+ # del f_z_i_copy, f_z_i_new
+ # # f_z_i = np.sum(np.sum(np.array(f_z_i), axis=2), axis=1)
+ #
+ # E = np.sum(f_z_i) / self.NSim
+ #
+ # V = (np.sum((f_z_i - E) ** 2)) / (self.NSim - 1)
+ #
+ # pool = mp.Pool(self.nr_cpu)
+ # result = pool.starmap(self.Parallel_CAPS_fitting,
+ # [(nr_sim, self.TrainingData.time.values[1:], forecast_edges, self.par_grd) for nr_sim
+ # in range(self.NSim)])
+ # total_events = [result[a][0] for a in range(len(result))]
+ # params = [result[a][1] for a in range(len(result))]
+ #
+ # z_i_ = np.array(params).T
+ #
+ # total_events_copy = np.array(total_events)
+ # total_events_new = []
+ # for l in range(len(total_events)):
+ # total_events_new.append(np.sum(total_events_copy[l][-1]))
+ # f_z_i_ = np.array(total_events_new)
+ # # f_z_i_ = np.sum(np.sum(np.array(total_events), axis=2), axis=1)
+ #
+ # s_j_array = []
+ # s_j_t_array = []
+ #
+ # for p in range(len(self.CAPSbounds)):
+ # y_i_j = np.copy(z_i_)
+ # # y_i_j = np.copy(z_i)
+ # y_i_j[p, :] = z_i[p, :]
+ # # y_i_j[p,:] = np.full_like(z_i[0], self.seed_settings_optimal[self.OptimizeKeys[p]])
+ # # y_i_j[p, :] = z_i_[p, :]
+ # random_method = False
+ # total_events_array = []
+ # for i in range(len(y_i_j[0])):
+ # for j in range(len(self.CAPSbounds)):
+ # self.seed_settings[self.OptimizeKeys[j]] = y_i_j[j, i]
+ # total_events = CAPS.Forecast_BinnedRate_only_1D(self.TrainingData.time.values[1:],
+ # forecast_edges[0], self.max_pressure_past,
+ # self.working_xyz_past, self.cells_volume_past,
+ # self.seed_settings)
+ # total_events_array.append(total_events)
+ #
+ # total_events_array_copy = np.array(total_events_array)
+ # total_events_array_new = []
+ # for l in range(len(total_events_array)):
+ # total_events_array_new.append(np.sum(total_events_array_copy[l][-1]))
+ # f_y_i_j = np.array(total_events_array_new)
+ # del total_events_array_copy, total_events_array_new
+ # # f_y_i_j = np.sum(np.sum(np.array(total_events_array), axis=2), axis=1)
+ #
+ # del y_i_j
+ #
+ # V_j = np.sum(f_z_i * f_y_i_j - E ** 2) / self.NSim
+ #
+ # # Calculate Sobol's main effect sensitivity index for each input
+ # s_j = np.abs(
+ # V_j / V) # here I take the absolute value (otherwise some of them are negative, negative effect? - not possible)
+ # s_j_array.append(s_j)
+ #
+ # T_j = np.sum((f_z_i_ - f_y_i_j) ** 2) / (2 * self.NSim)
+ #
+ # # Calculate Sobol's total effect sensitivity index for each input
+ # s_j_t = np.abs(T_j / V) # same as above for the absolute value
+ # s_j_t_array.append(s_j_t)
+ #
+ # result1 = (s_j_array, s_j_t_array)
+ #
+ # print("Sensitivity analysis (Sobol global) result (paramater's contribution to output):")
+ # for i in range(len(self.CAPSbounds)):
+ # print(self.OptimizeKeys[i] + ":", s_j_array[i], )
+ #
+ # print("Sum of main effect sensitivity indexes:", np.sum(result1[0]), )
+ # print("Sum of total effect sensitivity indexes:", np.sum(result1[1]), )
+ #
+ # elif method == 'hoffmann_gardener':
+ #
+ # """
+ # Hoffmann and Gardner (1983) calculate the output percent difference when varying one input parameter
+ # from its minimum value to its maximum value. It was introduced to account for all possible values when
+ # determining parameter sensitivity.
+ # """
+ #
+ # # self.seed_settings = np.copy(self.seed_settings_optimal)
+ #
+ # min_seed_settings = dict.copy(self.seed_settings_optimal)
+ # max_seed_settings = dict.copy(min_seed_settings)
+ # for j in range(len(self.CAPSbounds)):
+ # min_seed_settings[self.OptimizeKeys[j]] = np.min(params_distr[j])
+ # max_seed_settings[self.OptimizeKeys[j]] = np.max(params_distr[j])
+ #
+ # # min values
+ # Y_min = []
+ # Y_max = []
+ # s_j = []
+ # for j in range(len(self.CAPSbounds)):
+ # self.seed_settings = dict.copy(self.seed_settings_optimal)
+ # self.seed_settings[self.OptimizeKeys[j]] = min_seed_settings[self.OptimizeKeys[j]]
+ # total_events = CAPS.Forecast_BinnedRate_only_1D(self.TrainingData.time.values[1:], forecast_edges[0],
+ # self.max_pressure_past, self.working_xyz_past,
+ # self.cells_volume_past, self.seed_settings)
+ # Y_min.append(np.sum(total_events[-1, :]))
+ # self.seed_settings = dict.copy(self.seed_settings_optimal)
+ # self.seed_settings[self.OptimizeKeys[j]] = max_seed_settings[self.OptimizeKeys[j]]
+ # total_events = CAPS.Forecast_BinnedRate_only_1D(self.TrainingData.time.values[1:], forecast_edges[0],
+ # self.max_pressure_past, self.working_xyz_past,
+ # self.cells_volume_past, self.seed_settings)
+ # Y_max.append(np.sum(total_events[-1, :]))
+ # s_j.append(abs((Y_max[-1] - Y_min[-1]) / (Y_max[-1])))
+ #
+ # print("Sensitivity analysis (Hoffmann & Gardener, 1983) result (paramater's contribution to output):")
+ # for j in range(len(self.CAPSbounds)):
+ # print(self.OptimizeKeys[j] + ":", s_j[j], )
+ #
+ # result1 = s_j
+ #
+ # else:
+ # """
+ # Local sensitivity analysis: partial derivatives on parameters (very small changes):
+ #
+ # Differential sensitivity analysis is based on the partial differentiation of the aggregated model.
+ # A small change in one parameter's value with respect to their calibrated value is tested while the other
+ # parameters are set equal to their calibrated value. The corresponding change in the model's output is evaluated
+ # for that parameter (partial derivative of the dependent variable (input) with respect to the independent variable (output).
+ # The derivatives are then multiplied by the ratio of the parameter value to the model result (normalized by the mean),
+ # to normalize the results by removing the effects of units.
+ # """
+ # z_i = np.array(z_i).T
+ #
+ # f_z_i_copy = np.array(f_z_i)
+ # f_z_i_new = []
+ # for l in range(len(f_z_i)):
+ # f_z_i_new.append(np.sum(f_z_i_copy[l][-1]))
+ # f_z_i = np.array(f_z_i_new)
+ # del f_z_i_copy, f_z_i_new
+ # # f_z_i = np.sum(np.sum(np.array(f_z_i), axis=2), axis=1)
+ #
+ # z_i_ = z_i + (0.00001 * z_i) # changing all parameter values by 1/10 of 1% (very small change)
+ #
+ # total_events_array_global = []
+ # for j in range(len(self.CAPSbounds)):
+ # z_i_copy = np.copy(z_i)
+ # # replace the added derivative change for one parameter
+ # z_i_copy[j] = z_i_[j]
+ # total_events_array = []
+ # for i in range(len(z_i[0])):
+ # for l in range(len(self.CAPSbounds)):
+ # self.seed_settings[self.OptimizeKeys[l]] = z_i_copy[l, i]
+ # total_events = CAPS.Forecast_BinnedRate_only_1D(self.TrainingData.time.values[1:],
+ # forecast_edges[0],
+ # self.max_pressure_past, self.working_xyz_past,
+ # self.cells_volume_past, self.seed_settings)
+ # total_events_array.append(np.sum(total_events[-1, :]))
+ # total_events_array_global.append(np.array(total_events_array))
+ # f_z_i_ = np.array(total_events_array_global)
+ #
+ # s_j = np.zeros_like(z_i)
+ # s_j_norm = np.copy(s_j)
+ # for j in range(len(self.CAPSbounds)):
+ # for i in range(len(s_j[0])):
+ # s_j[j, i] = np.abs((f_z_i[i] - f_z_i_[j, i]) / (z_i[j, i] - z_i_[j, i]))
+ # s_j_norm[j, i] = s_j[j, i] * (z_i[j, i] / f_z_i[i])
+ #
+ # # set all values below 0 and above 1 to NaN
+ # s_j_norm[s_j_norm < 0] = np.nan
+ # s_j_norm[s_j_norm > 1] = np.nan
+ #
+ # print("Sensitivity analysis (local) mean result (paramater's contribution to output):")
+ # for j in range(len(self.CAPSbounds)):
+ # print(self.OptimizeKeys[j] + ":", np.nanmean(s_j_norm[j]), )
+ #
+ # result1 = s_j_norm
+ #
+ # return result1
+
+ def Calibrate_CAPS(self, forecast_edges):
+ self.update_seeds_settings() # update b-value
+
+ if self.cost_fun=='LL':
+ self.CostFunction = LL_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ bins=[self.TrainingData.time.values[1:],
+ forecast_edges[0]])
+ else:
+ self.CostFunction = LS_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ bins=[self.TrainingData.time.values[1:],
+ forecast_edges[0]])
+ # self.CostFunction = LS_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ # bins=[self.TrainingData.time.values[1:],
+ # forecast_edges])
+
+ self.LL_Edges = self.CostFunction.edges_x2 # (radial) spatial bins in negative and positive directions
+ self.LL_Moments = self.CostFunction.edges_x1 # time moments
+
+ (self.Training_MaxPressure,
+ self.Training_R,
+ self.Training_Volume) = self.Interpolate_1D(self.LL_Moments, self.LL_Edges, self.CAPSSettings)
+
+ print("Maximum iterations CAPS:", self.maxiter)
+
+ # open list to save the progress of the minimization
+ self.dea_vals = []
+ self.dea_func = []
+
+ if self.cost_fun=='LL':
+ result = differential_evolution(self.ll_score, self.SEISbounds, polish=True, disp=self.disp,
+ maxiter=self.maxiter, seed=self.seed, popsize=10)
+ else:
+ result = differential_evolution(self.ls_score, self.SEISbounds, polish=True, disp=self.disp,
+ maxiter=self.maxiter, seed=self.seed, popsize=10,
+ updating='immediate', callback=self.save_iteration_step)
+ print("End CAPS differential evolution")
+
+ print('--------------------------------')
+ print('Calibrated geomechanical parameters:')
+ for i in range(len(result.x)):
+ self.seed_settings[self.OptimizeKeys[i]] = result.x[i]
+ if (self.logscale[i]):
+ self.seed_settings[self.OptimizeKeys[i]] = np.power(10., result.x[i])
+ print(str(self.OptimizeKeys[i]) + ': ' + str(self.seed_settings[self.OptimizeKeys[i]]))
+ print('--------------------------------')
+
+ # self.total_events_past_single, self.total_rates_past_single = CAPS.Caps_Model_linear(self.LL_Moments,
+ # self.LL_Edges[self.LL_Edges >= 0.],
+ # self.Training_R,
+ # self.Training_MaxPressure,
+ # self.seed_settings,
+ # NSim=1,
+ # retriggering=self.retriggering)
+ #
+ # print("sum of total events after calibration", self.total_events_past_single[0,-1], )
+
+ # save optimal parameters for single forecast later
+ self.seed_settings_optimal = dict.copy(self.seed_settings)
+
+ print("**************************************************")
+ print("Summary of optimized parameters after calibration:")
+ for i in range(len(self.SEISbounds)):
+ print(self.OptimizeKeys[i] + ': ', self.seed_settings_optimal[self.OptimizeKeys[i]], )
+ j_init = len(self.SEISbounds)
+ for j in range(len(self.FlowBounds)):
+ print(self.OptimizeKeys[j_init+j] + ': ', self.seed_settings_optimal[self.OptimizeKeys[j_init+j]], )
+ print("**************************************************")
+
+ self.total_events_past_grid, self.total_rates_past_grid = CAPS.Caps_Model_linear(self.LL_Moments,
+ self.LL_Edges[self.LL_Edges >= 0.],
+ self.Training_R,
+ self.Training_MaxPressure,
+ self.seed_settings,
+ NSim=self.NSim,
+ retriggering=self.retriggering)
+
+ total_events_median = np.round(np.median(self.total_events_past_grid, axis=0)[-1])
+
+ print("sum of total events after calibration (median from grid)", total_events_median, )
+
+ # if total_events_median==0:
+ # total_events_median_masked = self.mask_for_median(self.total_events_past_grid[0])
+ #
+ # print("sum of total events after calibration and mask (median from grid)", total_events_median_masked[-1], )
+
+ return
+
+ def Calibrate_SEED(self, forecast_edges):
+ self.update_seeds_settings() # update b-value
+
+ if self.cost_fun=='LL':
+ self.CostFunction = LL_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ bins=[self.TrainingData.time.values,np.linspace(0,max(self.training_catalog[self.Xs].values),10)[:,0]])
+ else:
+ self.CostFunction = LS_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ bins=[self.TrainingData.time.values,np.linspace(0,max(self.training_catalog[self.Xs].values),10)[:,0]])
+
+ self.LL_Edges = self.CostFunction.edges_x2
+ self.LL_Moments = self.CostFunction.edges_x1
+
+ # 1D pressure interpolation (in time only)
+ pres_sol = (self.flow_model.hidden_mechanism - self.TrainingData[self.Columns[-1]].values[0])/(1.e6)
+ self.pres_t_fit = np.array([np.interp(self.LL_Moments[1:], self.flow_model.Q[:,0], ps_row) for ps_row in pres_sol.T])
+ # OR:
+ # Tind = np.argmin(np.abs(self.flow_model.Q[:,0] - self.LL_Moments[1:][:, np.newaxis]), axis=1)
+ # self.pres_t_fit = (pres_sol[Tind,:]).T
+
+ # Decide for the seed model version (implemented since Poster FAM23)
+ if self.seed_model_version=='1':
+ self.Nreal = 100 # self.NSim
+ elif self.seed_model_version=='2':
+ self.Nreal = 1
+ else:
+ self.Nreal = self.NSim
+
+ print("Maximum iterations SEED:", self.maxiter)
+
+ # open list to save the progress of the minimization
+ self.dea_vals = []
+ self.dea_func = []
+
+ if self.cost_fun=='LL':
+ result = differential_evolution(self.ll_score_seed, self.SEISbounds,polish=True, disp=self.disp,
+ maxiter=self.maxiter, seed=self.seed,popsize=10)
+ else:
+ result = differential_evolution(self.ls_score_seed, self.SEISbounds, polish=True,
+ disp=self.disp, maxiter=self.maxiter,seed=self.seed, popsize=10,
+ updating='immediate', atol=1e-3, callback=self.save_iteration_step)
+ print("End SEED differential evolution")
+
+ print('------------------------------------')
+ print('Calibrated geomechanical parameters:')
+ for i in range(len(result.x)):
+ self.seed_settings[self.OptimizeKeys[i]] = result.x[i]
+ if (self.logscale[i]):
+ self.seed_settings[self.OptimizeKeys[i]] = np.power(10., result.x[i])
+ print(str(self.OptimizeKeys[i]) + ': ' + str(self.seed_settings[self.OptimizeKeys[i]]))
+ print('------------------------------------')
+
+ # save optimal parameters for forecast later
+ self.seed_settings_optimal = dict.copy(self.seed_settings)
+
+ print("**************************************************")
+ print("Summary of optimized parameters after calibration:")
+ for i in range(len(self.SEISbounds)):
+ print(self.OptimizeKeys[i] + ': ', self.seed_settings_optimal[self.OptimizeKeys[i]], )
+ j_init = len(self.SEISbounds)
+ for j in range(len(self.FlowBounds)):
+ print(self.OptimizeKeys[j_init+j] + ': ', self.seed_settings_optimal[self.OptimizeKeys[j_init+j]], )
+ print("**************************************************")
+
+ # typed.List instance which is a compiled library backed, type-homogeneous list data type that is an improvement over the reflected-list: SeedsSettings = []
+ SeedsSettings = nb.typed.List()
+ for j in range(len(self.SEEDmodelKeys)):
+ SeedsSettings.append(float(self.seed_settings[self.SEEDmodelKeys[j]]))
+
+ self.training_catalog, self.total_events_past_grid, self.total_rates_past_grid = SEED.Seed_Model_linear(self.LL_Moments[1:],
+ self.LL_Edges[self.LL_Edges >= 0.],
+ self.flow_model.r,
+ self.flow_model.Q[:,0],
+ self.pres_t_fit,
+ SeedsSettings,
+ self.NSim,
+ self.retriggering)
+
+ total_events_median = np.round(np.median(self.total_events_past_grid, axis=0)[-1])
+
+ print("sum of total events after calibration (median from grid)", total_events_median, )
+
+ return
+
+ def _fit_sequential(self, HydraulicLogs_coarse_pd, catalog_incomplete_df, forecast_edges, deep=True):
+ self.TrainingData = HydraulicLogs_coarse_pd.copy(deep)
+ self.training_catalog = catalog_incomplete_df.copy(deep)
+
+ # filepath = '/home/victor/Desktop/pyatls/Data/Bedretto/FlowModels/'
+
+ if self.flw_model == 'point_source':
+ print("HM0: calibrating flow model (point source)....")
+ self.flow_model.fit(self.TrainingData[self.Columns[::2]].values)
+ # or:
+ # with open(filepath + 'HM0_point_source.pkl', 'rb') as f:
+ # self.flow_model = pickle.load(f)
+ # self.flow_model = self.flow_model[0]
+ elif self.flw_model == 'line_source':
+ print("HM0: calibrating flow model (line source)....")
+ self.flow_model.fit(self.TrainingData[self.Columns[::2]].values)
+ # or:
+ # with open(filepath + 'HM0_line_source.pkl', 'rb') as f:
+ # self.flow_model = pickle.load(f)
+ # self.flow_model = self.flow_model[0]
+ elif self.flw_model == 'num_const_perm':
+ print("HM0: calibrating flow model (numerical linear)....")
+ self.flow_model.fit(self.TrainingData[self.Columns[:2]].values, self.TrainingData[self.Columns[::2]].values)
+ # or:
+ # with open(filepath + 'HM0_numerical_linear.pkl', 'rb') as f:
+ # self.flow_model = pickle.load(f)
+ # self.flow_model = self.flow_model[0]
+ elif self.flw_model == 'num_var_perm':
+ print("HM1: calibrating flow model (numerical non-linear)....")
+ self.flow_model.fit(self.TrainingData[self.Columns[:2]].values, self.TrainingData[self.Columns[::2]].values)
+ # or:
+ # with open(filepath + 'HM1_2.pkl', 'rb') as f:
+ # self.flow_model = pickle.load(f)
+ # self.flow_model = self.flow_model[0]
+ else:
+ print("HM1: calibrating flow model (numerical non-linear with FV)....")
+ self.flow_model.fit(self.TrainingData[self.Columns[:2]].values, self.TrainingData[self.Columns[::2]].values)
+
+ print('--------------------------------')
+ print('Calibrated hydraulic parameters:')
+ for i in range(len(self.flow_model.params)):
+ self.seed_settings[self.OptimizeKeys[i-len(self.flow_model.params)]] = self.flow_model.params[i]
+ print(str(self.OptimizeKeys[i-len(self.flow_model.params)]) + ': ' + str(self.flow_model.params[i]))
+ print('--------------------------------')
+
+ self.timesteps = self.TrainingData.time.values
+ inipres=self.TrainingData[self.Columns[-1]].values[0]
+
+ if self.seis_model=='CAPS':
+ print("HM0/1: calibrating CAPS....")
+ self.PressureFunction = self.create_interpolator(self.timesteps, (self.flow_model.hidden_mechanism - inipres) / 1.e6)
+ self.Calibrate_CAPS(forecast_edges)
+ else:
+ print("HM0/1: calibrating SEED....")
+ self.Calibrate_SEED(forecast_edges)
+
+ return
+
+ # def _fit_joint(self, HydraulicLogs_coarse_pd, catalog_incomplete_df, forecast_edges, deep=True):
+ # self.TrainingData = HydraulicLogs_coarse_pd.copy(deep)
+ # self.training_catalog = catalog_incomplete_df.copy(deep)
+ #
+ # self.update_seeds_settings()
+ #
+ # if self.cost_fun == 'LL':
+ # self.SeisCostFunction = LL_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ # bins=[self.TrainingData.time.values[1:],
+ # forecast_edges[0]])
+ # else:
+ # self.SeisCostFunction = LS_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ # bins=[self.TrainingData.time.values[1:],
+ # forecast_edges[0]])
+ #
+ # self.LL_Edges = self.SeisCostFunction.edges_x2
+ # self.LL_Moments = self.SeisCostFunction.edges_x1
+ #
+ # # We need to join the CAPS and the Flow bounds stated previously:
+ # self.ParamBounds = self.CAPSbounds + self.FlowBounds
+ #
+ # print("Maximum iterations CAPS:", self.maxiter)
+ # self.CAPS_iter_results = []
+ # if self.cost_fun=='LL':
+ # result = differential_evolution(self.ll_score_joint, self.ParamBounds,polish=True,
+ # disp=self.disp, maxiter=self.maxiter, seed=self.seed,popsize=10, callback=self.caps_iteration)
+ # else:
+ # result = differential_evolution(self.ls_score_joint, self.ParamBounds,polish=True,
+ # disp=self.disp, maxiter=100, seed=self.seed,popsize=10, callback=self.caps_iteration)
+ #
+ # print("End CAPS differential evolution")
+ #
+ # for i in range(len(result.x)):
+ # self.seed_settings[self.OptimizeKeys[i]] = result.x[i]
+ # if (self.logscale[i]):
+ # self.seed_settings[self.OptimizeKeys[i]] = np.power(10., result.x[i])
+ #
+ # self.flow_model.params = self.seed_settings[self.OptimizeKeys[4]], self.seed_settings[self.OptimizeKeys[5]]
+ # #self.flow_model.params = self.seed_settings[self.OptimizeKeys[4]]
+ #
+ # self.flow_model.hidden_mechanism = self.flow_model.predict()
+ #
+ # self.timesteps = self.TrainingData.time.values
+ # inipres=self.TrainingData[self.Columns[-1]].values[0]
+ # self.PressureFunction = self.create_interpolator(self.timesteps,(self.flow_model.hidden_mechanism-inipres)/1.e6)
+ #
+ # (max_pressure_past,
+ # working_xyz_past,
+ # cells_volume_past) = self.Interpolate_1D(self.TrainingData.time.values[1:], forecast_edges[0],
+ # self.ExSolSettings, self.CAPSSettings)
+ #
+ # self.total_events_past_single = CAPS.Forecast_BinnedRate_only_1D(
+ # self.TrainingData.time.values[1:], forecast_edges[0], max_pressure_past, working_xyz_past,
+ # cells_volume_past, self.seed_settings)
+ #
+ # print('Poisson Density CAPS:', self.seed_settings["PoissonDensity"])
+ # print("sum of total events after calibration", np.sum(self.total_events_past_single[-1, :]), )
+ #
+ # # looking for index in event past corresponsing to time of last event
+ # index = np.where(self.TrainingData.time.values[1:] <= self.training_catalog.ts.values[-1])
+ # NewPoisson = (self.seed_settings['PoissonDensity']
+ # * len(self.training_catalog) / (
+ # np.sum(self.total_events_past_single[index[0][-1] + 1, :]))) # noqa
+ #
+ # # Only adapting the Poisson Density if it is a finite number and if the predicted cumulative events
+ # # are within a specific range from the observed ones:
+ # events_ratio = self.SeisCostFunction.y[-1]/(np.sum(self.total_events_past_single[index[0][-1]+1, :]))
+ # if np.isfinite(NewPoisson):
+ # if 0.85 <= events_ratio <= 1.15:
+ # self.seed_settings['PoissonDensity'] = NewPoisson
+ # print('Poisson density CAPS within the range. Corrected.')
+ # else:
+ # print('Poisson density CAPS is finite but not within the range. Not corrected.')
+ # else:
+ # print('Poisson Density CAPS is not finite. Not corrected.')
+ #
+ # print('Poisson Density CAPS:', self.seed_settings["PoissonDensity"])
+ #
+ # self.total_events_past_single = CAPS.Forecast_BinnedRate_only_1D(
+ # self.TrainingData.time.values[1:], forecast_edges[0], max_pressure_past, working_xyz_past,
+ # cells_volume_past, self.seed_settings)
+ #
+ # # save optimal parameters for single forecast later
+ # self.seed_settings_optimal = self.seed_settings
+ # # save maximum pressure past for later
+ # self.max_pressure_past = max_pressure_past
+ # self.working_xyz_past = working_xyz_past
+ # self.cells_volume_past = cells_volume_past
+ #
+ # print("sum of total events after Re-calibration (calibrated params)", np.sum(self.total_events_past_single[-1, :]), )
+ #
+ # print("Optimized parameters after calibration:")
+ # for i in range(len(self.CAPSbounds)):
+ # print(self.OptimizeKeys[i]+":", self.seed_settings[self.OptimizeKeys[i]], )
+ #
+ # j_init = len(self.CAPSbounds)
+ # for j in range(len(self.FlowBounds)):
+ # print(self.OptimizeKeys[j_init+j]+":", self.seed_settings[self.OptimizeKeys[j_init+j]], )
+ #
+ # #---------------------------------------------------------------------------------------------------------------
+ #
+ # # get uncertainty range for parameters
+ # # par_grd_1 = self.Params_flowmodel()
+ #
+ # cal_val = []
+ # for i in range(len(self.CAPSbounds)):
+ # cal_val.append(self.seed_settings[self.OptimizeKeys[i]])
+ # self.cal_val = np.array(cal_val)
+ #
+ # # result = differential_evolution(self.ls_params_score, [(90, 100)], polish=True, disp=self.disp,
+ # # maxiter=self.maxiter, seed=self.seed, popsize=10, callback=self.caps_iteration)
+ #
+ # # percent = result.x
+ # percent = 10
+ #
+ # # print("1sigma for normal trunc. distr. of params: " + str(percent[0]) + " divided by 100 times calib. parameter.")
+ #
+ # par_grd_2 = []
+ # for p in range(len(self.CAPSbounds)):
+ # # par_distr = self.Params_CAPS(forecast_edges, max_pressure_past, working_xyz_past, cells_volume_past, p)
+ # par_distr = self.Params_CAPS_2(percent, p)
+ # # par_grd_2.append((par_distr.T)[0])
+ # par_grd_2.append(par_distr)
+ #
+ # self.par_grd = np.array(par_grd_2)
+ # # self.par_grd = np.concatenate((par_grd_2, par_grd_1))
+ # # self.par_grd = np.vstack((par_grd_2, par_grd_1))
+ #
+ # # Plotting distributions
+ # #ATLS.PlotParamDist(self.par_grd, self.cal_val, seis_model='CAPS', filename='Bedretto_params_distr.png')
+ #
+ # if 1 <= self.workers <= mp.cpu_count():
+ # nr_cpu = self.workers
+ # else:
+ # print('Workers must be between 1 and 16 or -1 to indicate max. nr. of cores, which is 16')
+ # print('Default: using all 16 cores')
+ # nr_cpu = mp.cpu_count()
+ #
+ # self.nr_cpu = nr_cpu
+ #
+ # pool = mp.Pool(nr_cpu)
+ # result = pool.starmap(self.Parallel_CAPS_fitting, [(nr_sim, self.TrainingData.time.values[1:], forecast_edges, self.par_grd) for nr_sim in range(self.NSim)])
+ # pool.close()
+ #
+ # # total_events = [result[a][0] for a in range(len(result))]
+ # # press_fit = [result[a][1] for a in range(len(result))]
+ #
+ # self.total_events_past_grd = (np.array(result)).T
+ # # self.press_fit = (np.array(press_fit)).T
+ #
+ # total_events_median = np.round(np.sum(np.median(self.total_events_past_grd, axis=2), axis=0))[-1]
+ #
+ # print("sum of total events after Re-calibration (median from grid)", total_events_median, )
+ #
+ # if total_events_median == 0:
+ # total_events_median_masked = self.mask_for_median(self.total_events_past_grd)
+ #
+ # print("sum of total events after Re-calibration and mask (median from grid)",
+ # total_events_median_masked[-1], )
+ #
+ # return
+
+ def fit(self, HydraulicLogs_coarse_pd, catalog_incomplete_df, forecast_edges, deep=True):
+
+ if self.inv_mod == 'joint':
+ print("Model not available at the moment ... using sequential.")
+ self._fit_sequential(HydraulicLogs_coarse_pd, catalog_incomplete_df, forecast_edges, deep)
+ # self._fit_joint(HydraulicLogs_coarse_pd, catalog_incomplete_df, forecast_edges, deep)
+ else:
+ self._fit_sequential(HydraulicLogs_coarse_pd, catalog_incomplete_df, forecast_edges, deep)
+
+ def predict(self, PlannedStrategy, Forecasts_Moments, Forecasts_Edges):
+
+ # CAPS MODEL #
+
+ if self.seis_model=='CAPS':
+ self.seed_settings = self.seed_settings_optimal
+
+ (self.max_pressure_past,
+ self.Forecast_R,
+ self.Forecast_Volume) = self.Interpolate_1D(Forecasts_Moments, Forecasts_Edges[0], self.CAPSSettings)
+
+ self.press_prediction = self.flow_model.predict(np.array(PlannedStrategy[["time", "flow"]].values))
+
+ self.timesteps = PlannedStrategy.time.values
+ inipres = self.TrainingData[self.Columns[-1]].values[0]
+ self.PressureFunction = self.create_interpolator(self.timesteps, (self.press_prediction - inipres) / 1.e6)
+
+ (self.Forecast_MaxPressure,
+ self.Forecast_R,
+ self.Forecast_Volume) = self.Interpolate_1D(Forecasts_Moments, Forecasts_Edges[0], self.CAPSSettings)
+
+ max_pressure = np.maximum(self.max_pressure_past, self.Forecast_MaxPressure)
+
+ print('Starting CAPS forecast')
+
+ self.total_events_forecast_grid, self.total_rates_forecast_grid = CAPS.Caps_Model_linear(Forecasts_Moments,
+ Forecasts_Edges[0][Forecasts_Edges[0] >= 0.],
+ self.Forecast_R,
+ max_pressure,
+ self.seed_settings,
+ NSim=self.NSim,
+ retriggering=self.retriggering)
+
+ print('Ending CAPS forecast')
+
+ # This is a "trick" and needs to be deleted after the FAM23
+
+ # if np.median(self.total_events_forecast_grid, axis=0)[0]==0:
+ # if self.flow_model.Q[0, 1] <= 1.e-9:
+ # # repair events matrix
+ # last_col = self.total_events_past_grid[:, -1]
+ # total_events_forecast_grid_copy = np.zeros_like((self.total_events_forecast_grid))
+ # for i in range(len(self.total_events_forecast_grid[0])):
+ # total_events_forecast_grid_copy[:, i] = self.total_events_forecast_grid[:, i] + last_col
+ # del self.total_events_forecast_grid
+ # self.total_events_forecast_grid = total_events_forecast_grid_copy
+ # del total_events_forecast_grid_copy
+ # # repair rates matrix
+ # # self.total_rates_forecast_grid[:, 0] = self.total_rates_past_grid[:,-1]
+ # # Replace the first time moment for both events and rates by the last one from training
+ # self.total_events_forecast_grid[:, 0] = self.total_events_forecast_grid[:, 1]
+ # diff_col = abs(self.total_events_past_grid[:, -1] - self.total_events_forecast_grid[:, 0])
+ # total_events_forecast_grid_copy = np.zeros_like((self.total_events_forecast_grid))
+ # for i in range(len(self.total_events_forecast_grid[0])):
+ # total_events_forecast_grid_copy[:, i] = self.total_events_forecast_grid[:, i] - diff_col
+ # del self.total_events_forecast_grid
+ # self.total_events_forecast_grid = total_events_forecast_grid_copy
+ # else:
+ # # Replace the first time moment for both events and rates by the last one from training
+ # self.total_events_forecast_grid[:, 0] = self.total_events_past_grid[:, -1]
+
+ return
+
+ # SEED MODEL #
+
+ else:
+ self.seed_settings = self.seed_settings_optimal
+
+ self.timesteps = PlannedStrategy.time.values
+ self.press_prediction = self.flow_model.predict(np.array(PlannedStrategy[["time", "flow"]].values))
+
+ # 1D pressure interpolation (in time only)
+ pres_sol = (self.press_prediction - self.TrainingData[self.Columns[-1]].values[0]) / (1.e6)
+ self.pres_t_forecast = np.array([np.interp(Forecasts_Moments, PlannedStrategy['time'].values, ps_row)
+ for ps_row in pres_sol.T])
+ # OR:
+ # Tind = np.argmin(np.abs(PlannedStrategy['time'].values - Forecasts_Moments[:, np.newaxis]), axis=1)
+ # self.pres_t_fit = (pres_sol[Tind,:]).T
+
+ # typed.List instance which is a compiled library backed, type-homogeneous list data type that is an improvement over the reflected-list: SeedsSettings = []
+ SeedsSettings = nb.typed.List()
+ for j in range(len(self.SEEDmodelKeys)):
+ SeedsSettings.append(float(self.seed_settings[self.SEEDmodelKeys[j]]))
+
+ # self.synthetic_catalog, self.total_events_forecast_grid, self.total_rates_forecast_grid = SEED.Seed_Model_linear(Forecasts_Moments,
+ # Forecasts_Edges[0][Forecasts_Edges[0] >= 0.],
+ # self.flow_model.r,
+ # PlannedStrategy['time'].values,
+ # self.pres_t_forecast,
+ # SeedsSettings,
+ # self.NSim,
+ # self.retriggering)
+
+ self.synthetic_catalog, self.total_events_grid, self.total_rates_grid = SEED.Seed_Model_linear(np.concatenate((self.LL_Moments[1:], Forecasts_Moments[1:]), axis=0),
+ Forecasts_Edges[0][Forecasts_Edges[0] >= 0.],
+ self.flow_model.r,
+ np.concatenate((self.flow_model.Q[:,0], PlannedStrategy['time'].values[1:]), axis=0),
+ np.concatenate((self.pres_t_fit, self.pres_t_forecast[:,1:]), axis=1),
+ SeedsSettings,
+ self.NSim,
+ self.retriggering)
+ self.total_events_past_grid = self.total_events_grid[:, :len(self.LL_Moments[1:])]
+ self.total_events_forecast_grid = self.total_events_grid[:, (len(self.LL_Moments[1:])-1):]
+
+ # self.total_rates_forecast_grid = self.total_rates_grid[:, (len(self.total_rates_past_grid[0])-1):-1]
+
+ # OR:
+
+ # Take the rates of the forecast and sum up to the last events of the training
+ # total_events = np.zeros_like(self.total_events_forecast_grid)
+ # total_events[:,0] = self.total_events_past_grid[:,-1]
+ # for i in range(1, len(total_events[0])):
+ # total_events[:,i] = total_events[:,i-1] + self.total_rates_forecast_grid[:,i-1]
+ #
+ # self.total_events_forecast_grid = total_events
+
+ return
+
+ def forecast_only(self, PlannedStrategy, Forecasts_Moments, Forecasts_Edges):
+
+ # CAPS MODEL #
+
+ if self.seis_model=='CAPS':
+
+ self.press_prediction = self.flow_model.predict(np.array(PlannedStrategy[["time", "flow"]].values))
+
+ self.timesteps = PlannedStrategy.time.values
+ inipres = self.TrainingData[self.Columns[-1]].values[0]
+ self.PressureFunction = self.create_interpolator(self.timesteps, (self.press_prediction - inipres) / 1.e6)
+
+ (self.Forecast_MaxPressure,
+ self.Forecast_R,
+ self.Forecast_Volume) = self.Interpolate_1D(Forecasts_Moments, Forecasts_Edges[0], self.CAPSSettings)
+
+ print('Starting CAPS forecast')
+
+ self.total_events_forecast_grid, self.total_rates_forecast_grid = CAPS.Caps_Model_linear(Forecasts_Moments,
+ Forecasts_Edges[0][Forecasts_Edges[0] >= 0.],
+ self.Forecast_R,
+ self.Forecast_MaxPressure,
+ self.seed_settings,
+ NSim=self.NSim,
+ retriggering=self.retriggering)
+
+ print('Ending CAPS forecast')
+
+ return
+
+ # SEED MODEL #
+
+ else:
+
+ self.timesteps = PlannedStrategy.time.values
+ self.press_prediction = self.flow_model.predict(np.array(PlannedStrategy[["time", "flow"]].values))
+
+ # 1D pressure interpolation (in time only)
+ pres_sol = (self.press_prediction - self.TrainingData[self.Columns[-1]].values[0]) / (1.e6)
+ self.pres_t_forecast = np.array([np.interp(Forecasts_Moments, PlannedStrategy['time'].values, ps_row) for ps_row in pres_sol.T])
+
+ # typed.List instance which is a compiled library backed, type-homogeneous list data type that is an improvement over the reflected-list: SeedsSettings = []
+ SeedsSettings = nb.typed.List()
+ for j in range(len(self.SEEDmodelKeys)):
+ SeedsSettings.append(float(self.seed_settings[self.SEEDmodelKeys[j]]))
+
+ self.synthetic_catalog, self.total_events_grid, self.total_rates_grid = SEED.Seed_Model_linear(Forecasts_Moments,
+ Forecasts_Edges[0][Forecasts_Edges[0] >= 0.],
+ self.flow_model.r,
+ PlannedStrategy['time'].values,
+ self.pres_t_forecast,
+ SeedsSettings,
+ self.NSim,
+ self.retriggering)
+
+ # self.total_events_past_grid = self.total_events_grid[:, :len(self.LL_Moments[1:])]
+ # self.total_events_forecast_grid = self.total_events_grid[:, (len(self.LL_Moments[1:])-1):]
+
+ return
\ No newline at end of file
diff --git a/hm1d/__init__.py b/hm1d/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/hm1d/model.py b/hm1d/model.py
new file mode 100644
index 0000000..6cf2dd0
--- /dev/null
+++ b/hm1d/model.py
@@ -0,0 +1,882 @@
+import logging
+from datetime import timedelta
+
+import numpy as np
+import numba as nb
+import copy
+from hydws.parser import BoreholeHydraulics
+from ramsis_model import ModelInput
+from scipy import stats
+from scipy.interpolate import interp1d
+from seismostats.seismicity.catalog import Catalog
+from seismostats.seismicity.rategrid import ForecastGRRateGrid
+from seismostats.utils.coordinates import CoordinateTransformer
+from shapely import wkt
+from sklearn.linear_model import Lasso
+
+from hm1d.FlowModel import FlowModel0, FlowModel1, FlowModel2, FlowModel3, FlowModel4
+
+from hm1d.SeismicityModel import Caps_Model_linear as CAPS
+from hm1d.SeismicityModel import Seed_Model_linear as SEED
+
+from scipy.optimize import differential_evolution
+from scipy.interpolate import CloughTocher2DInterpolator
+
+from hm1d.utils import (daterange,Calibrate_Observed_GR, b_value_Lasso_calibration_1D, Refine_An_Edge,
+ LL_cost_function_1D, LS_cost_function_1D)
+
+
+class HM1D():
+ """ InducedSFM Class for Hydro-mechanical models in 1D
+
+ Available variables after successfull initialization:
+
+ Input Hydraulic and Seismic data is available as DataFrames as:
+ self.hydraulic_df,
+ self.scenario_df,
+ self.seismicity_df
+
+ Variables from input dict are also available as class properties:
+ self.model_parameters
+ self.forecast_start
+ self.forecast_end
+ self.epoch_duration
+ self.proj_string
+
+ Additionally are available:
+ self.forecast_moments
+ self.section_metadata
+ self.observation_start
+ self.observation_end
+
+ A logger is available with self._logger.* (info, warning, error, ...)
+
+ """
+
+ def __init__(self, model_input: ModelInput, **kwargs):
+ """
+ Gets called after the aforementioned variables are instantiated, eg.
+ model setup can live in here.
+ """
+ # set up logger
+ self._logger = logging.getLogger(__name__)
+ self._logger.info('Model Initialized')
+ self.proj_string=self.parameters['local_proj_string']
+
+ self.model_input = model_input
+ self.parameters = model_input.model_parameters
+
+ self.column_flow = "topflow"
+ self.column_pressure = "toppressure"
+
+ self.opt_algorithm = self.parameters['opt_algorithm']
+ self.sensitivity_analysis = self.parameters['sensitivity_analysis']
+
+
+ self.cost_fun = self.parameters['cost_fun']
+ listOfStrings = ['LL' , 'LS']
+ if self.cost_fun not in listOfStrings :
+ print("Cost function '",self.cost_fun,"' not found. Possible values are:", listOfStrings,". Using default mode (LL).")
+ self.cost_fun='LL'
+
+ self.flw_model = self.parameters['flow_model']
+ listOfStrings = ['point_source' , 'line_source', 'num_const_perm', 'num_var_perm']
+ if self.flw_model not in listOfStrings :
+ print("Fluid model '",self.cost_fun,"' not found. Possible values are:", listOfStrings,". Using default mode (line_source).")
+ self.flw_model='line_source'
+
+ self.NSim=self.parameters['NSim']
+ self.seed_model_version = self.parameters['seed_model_version']
+ self.seis_model = self.parameters['seis_model']
+ self.seed_settings =self.parameters['SeisSettings']
+ self.retriggering = self.seed_settings['retriggering']
+ self.SEISbounds = self.parameters.get('SeisBounds',{})
+ self.flow_bounds = self.parameters['Settings'][2]
+ self.ExSolSettings = self.parameters['Settings'][3]
+ self.CAPSSettings = self.parameters['Settings'][4]
+
+ self.CostFunction = None
+ self.LL_Edges = None
+ self.LL_Moments = None
+ self.Training_MaxPressure = None
+ self.Training_XYZ = None
+ self.Training_Volume = None
+
+ # input for the flow models
+ self.flow_settings = self.parameters['FlowSettings']
+ self.max_grid_ext = self.flow_settings.get('max_grid_ext',300)
+ self.layer_thick = self.flow_settings.get('layer_thick',100)
+ self.cross_area = self.flow_settings.get('cross_area',94)
+
+ self.maxbval_bounds = [(1., self.seed_settings["b_max"])] # upper b-value limit
+ self.dM = self.parameters['dM']
+
+ if self.retriggering == True:
+ if self.seed_settings['criticality']!=0:
+ self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'stressdrop_coeff', 'cohesion_mean', 'friction_mean', 'seed_density']
+ self.SEISbounds = [(self.seis_bounds['sigma1_std'][0], self.seis_bounds['sigma1_std'][1]),
+ (self.seis_bounds['sigma3_std'][0], self.seis_bounds['sigma3_std'][1]),
+ (self.seis_bounds['stressdrop_coeff'][0], self.seis_bounds['stressdrop_coeff'][1]),
+ # (self.seis_bounds['criticallity'][0], self.seis_bounds['criticallity'][1]),
+ (self.seis_bounds['cohesion_mean'][0], self.seis_bounds['cohesion_mean'][1]),
+ (self.seis_bounds['friction_mean'][0], self.seis_bounds['friction_mean'][1]),
+ (self.seis_bounds['seed_density'][0], self.seis_bounds['seed_density'][1])]
+ self.logscale = [False, False, False, False, False, False]
+ else:
+ self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'stressdrop_coeff', 'cohesion_mean', 'friction_mean', 'seed_density']
+ self.SEISbounds = [(self.seis_bounds['sigma1_std'][0], self.seis_bounds['sigma1_std'][1]),
+ (self.seis_bounds['sigma3_std'][0], self.seis_bounds['sigma3_std'][1]),
+ (self.seis_bounds['stressdrop_coeff'][0], self.seis_bounds['stressdrop_coeff'][1]),
+ (self.seis_bounds['cohesion_mean'][0], self.seis_bounds['cohesion_mean'][1]),
+ (self.seis_bounds['friction_mean'][0], self.seis_bounds['friction_mean'][1]),
+ (self.seis_bounds['seed_density'][0], self.seis_bounds['seed_density'][1])]
+ self.logscale = [False, False, False, False, False, False]
+ else:
+ if self.seed_settings['criticality']!=0:
+ self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'friction_mean', 'cohesion_mean', 'seed_density']
+ self.SEISbounds = [(self.seis_bounds['sigma1_std'][0], self.seis_bounds['sigma1_std'][1]),
+ (self.seis_bounds['sigma3_std'][0], self.seis_bounds['sigma3_std'][1]),
+ (self.seis_bounds['friction_mean'][0], self.seis_bounds['friction_mean'][1]),
+ (self.seis_bounds['cohesion_mean'][0], self.seis_bounds['cohesion_mean'][1]),
+ (self.seis_bounds['seed_density'][0], self.seis_bounds['seed_density'][1])]
+ self.logscale = [False, False, False, False, False]
+ else:
+ self.OptimizeKeys = ['sigma1_std', 'sigma3_std', 'cohesion_mean', 'friction_mean', 'seed_density']
+ self.SEISbounds = [(self.seis_bounds['sigma1_std'][0], self.seis_bounds['sigma1_std'][1]),
+ (self.seis_bounds['sigma3_std'][0], self.seis_bounds['sigma3_std'][1]),
+ (self.seis_bounds['cohesion_mean'][0], self.seis_bounds['cohesion_mean'][1]),
+ (self.seis_bounds['friction_mean'][0], self.seis_bounds['friction_mean'][1]),
+ (self.seis_bounds['seed_density'][0], self.seis_bounds['seed_density'][1])]
+ self.logscale = [False, False, False, False, False]
+
+ """
+ 4 different flow models can be choosen:
+ -> point_source: analytical point source solution. Two parameters, storage coefficient and permeability, are calibrated.
+ -> line_source: Theis' solution. Two parameters, storage coefficient and permeability, are calibrated.
+ -> num_const_perm: numerical solution without permeability changes. Storage and permeability are calibrated.
+ -> num_var_perm_FD: NOT WORKING, should be removed
+ -> num_var_perm: numerical with variable fracture permeability. Parameters to be calibrated are:
+ ['zero_stress_aperture',
+ 'residual_aperture',
+ 'shearing_aperture_factor',
+ 'max_shear_frac_aperture',
+ 'shear_scaling_factor',
+ 'storage']
+ """
+
+ if self.flw_model == 'point_source':
+ self.OptimizeKeys = self.OptimizeKeys + ['storage', 'permeability'] # parameters to calibrate
+ self.FlowBounds = [(self.flow_bounds['storage'][0], self.flow_bounds['storage'][1]),
+ (self.flow_bounds['permeability'][0], self.flow_bounds['permeability'][1])]
+ self.logscale = self.logscale + [True, True]
+ # activate the flow model:
+ self.flow_model = FlowModel0(self.TrainingData[self.Columns[:2]].values,
+ np.geomspace(0.1, self.max_grid_ext, 30, endpoint=True),
+ self.cross_area,
+ self.seed_settings['viscosity'],
+ bounds=self.FlowBounds,
+ iniguess=(self.seed_settings['storage'], self.seed_settings['permeability']))
+
+ elif self.flw_model == 'line_source':
+ self.OptimizeKeys = self.OptimizeKeys + ['storage', 'permeability']
+ self.FlowBounds = [(self.flow_bounds['storage'][0], self.flow_bounds['storage'][1]),
+ (self.flow_bounds['permeability'][0], self.flow_bounds['permeability'][1])]
+ self.logscale = self.logscale + [True, True]
+ # activate the flow model:
+ self.flow_model = FlowModel1(self.TrainingData[self.Columns[:2]].values,
+ np.geomspace(0.1, self.max_grid_ext, 30, endpoint=True),
+ self.layer_thick,
+ self.seed_settings['viscosity'],
+ bounds=self.FlowBounds,
+ iniguess=(self.seed_settings['storage'], self.seed_settings['permeability']))
+
+ elif self.flw_model == 'num_const_perm':
+ self.OptimizeKeys = self.OptimizeKeys + ['storage', 'permeability']
+ self.FlowBounds = [(self.flow_bounds['storage'][0], self.flow_bounds['storage'][1]),
+ (self.flow_bounds['permeability'][0], self.flow_bounds['permeability'][1])]
+ self.logscale = self.logscale + [True, True]
+ # activate the flow model:
+ self.flow_model = FlowModel2(self.TrainingData[self.Columns[:2]].values,
+ np.geomspace(0.1, self.max_grid_ext, 30, endpoint=True), self.layer_thick,
+ self.seed_settings, bounds=self.FlowBounds)
+
+ elif self.flw_model == 'num_var_perm_FD': # HM1 class with FD
+ self.OptimizeKeys = self.OptimizeKeys + ['zero_stress_aperture', 'residual_aperture','frac_normal_stiffness','storage'] # parameters to calibrate
+ self.FlowBounds = [(self.flow_bounds['zero_stress_aperture'][0], self.flow_bounds['zero_stress_aperture'][1]),
+ (self.flow_bounds['residual_aperture'][0], self.flow_bounds['residual_aperture'][1]),
+ (self.flow_bounds['frac_normal_stiffness'][0], self.flow_bounds['frac_normal_stiffness'][1]),
+ (self.flow_bounds['storage'][0], self.flow_bounds['storage'][1])]
+ self.logscale = self.logscale + [True, True, True, True]
+ # activate the flow model:
+ self.flow_model = FlowModel3(self.TrainingData[self.Columns[:2]].values,
+ np.geomspace(0.1, self.max_grid_ext, 30, endpoint=True), self.layer_thick,
+ self.seed_settings, bounds=self.FlowBounds)
+
+ elif self.flw_model == 'num_var_perm': # HM1 cass with FV
+ self.OptimizeKeys = self.OptimizeKeys + ['zero_stress_aperture', 'residual_aperture', 'shearing_aperture_factor', 'max_shear_frac_aperture', 'shear_scaling_factor', 'storage']
+ self.FlowBounds = [(self.flow_bounds['zero_stress_aperture'][0], self.flow_bounds['zero_stress_aperture'][1]),
+ (self.flow_bounds['residual_aperture'][0], self.flow_bounds['residual_aperture'][1]),
+ (self.flow_bounds['shearing_aperture_factor'][0], self.flow_bounds['shearing_aperture_factor'][1]),
+ (self.flow_bounds['max_shear_frac_aperture'][0], self.flow_bounds['max_shear_frac_aperture'][1]),
+ (self.flow_bounds['shear_scaling_factor'][0], self.flow_bounds['shear_scaling_factor'][1]),
+ (self.flow_bounds['storage'][0], self.flow_bounds['storage'][1])]
+ self.logscale = self.logscale + [True, True, True, True, True, True]
+ # activate the flow model:
+ self.flow_model = FlowModel4(self.TrainingData[self.Columns[:2]].values,
+ np.geomspace(0.1, self.max_grid_ext, 30, endpoint=True), self.layer_thick,
+ self.seed_settings, bounds=self.FlowBounds, forward_model=self.forward_model,
+ iniguess=(self.seed_settings['zero_stress_aperture'], self.seed_settings['residual_aperture'],
+ self.seed_settings['shearing_aperture_factor'], self.seed_settings['max_shear_frac_aperture'],
+ self.seed_settings['shear_scaling_factor'], self.seed_settings['storage']))
+
+
+ if self.seis_model=='SEED':
+ self.SEEDmodelKeys = ['seed_density', 'inter_sigma1', 'inter_sigma3', 'sigma1_std', 'sigma3_std',
+ 'cohesion_mean', 'friction_mean', 'criticality', 'stressdrop_coeff', 'phydro',
+ 'b_min', 'b_max', 'b_min_diff_stress', 'b_max_diff_stress', 'Mag_Completeness']
+
+ self.disp = self.parameters.get('verbose',True)
+ self.maxiter = self.parameters.get('maxiter',-1)
+ self.seed = 42
+
+
+ section_id = self.parameters['well_section_publicid']
+
+ self.geometry = model_input.geometry
+
+ self.forecast_start = model_input.forecast_start
+ self.forecast_end = model_input.forecast_end
+
+ self.hydraulics = BoreholeHydraulics(model_input.injection_well[0])
+
+ self.scenario = BoreholeHydraulics(model_input.injection_plan)
+
+ self.seismicity = Catalog.from_quakeml(model_input.seismic_catalog,
+ includequality=True)
+
+ # get dataframes from HYDSJONParser
+ self.hydraulic_df = self.hydraulics[section_id].hydraulics
+ self.hydraulic_df = self.hydraulic_df[~self.hydraulic_df['topflow'].isna()]
+ self.hydraulic_df = self.hydraulic_df[~self.hydraulic_df['toppressure'].isna()]
+
+ self.scenario_df = self.scenario[section_id].hydraulics
+
+ self.observation_start = self.hydraulic_df.index.min()
+ self.observation_end = np.array(
+ self.forecast_start).astype('datetime64')
+
+ self.hydraulic_df = self.hydraulic_df[self.hydraulic_df.index
+ < self.observation_end]
+ self.seismicity = self.seismicity[self.seismicity['time']
+ < self.forecast_start]
+
+ # self.observation_end = self.hydraulic_df.index.max()
+ # self.observation_end = min(self.observation_end, self.forecast_start)
+
+ self.seismicity = self.seismicity[~self.seismicity['magnitude'].isna()]
+
+ if 'associatedphasecount' in self.seismicity.columns:
+ self.seismicity['associatedphasecount'] = \
+ self.seismicity['associatedphasecount'].astype(float)
+ # is this sensible?
+ self.seismicity['associatedphasecount'] = \
+ self.seismicity['associatedphasecount'].fillna(0)
+
+ self.seismicity = \
+ self.seismicity[self.seismicity['associatedphasecount'] >= self.parameters['n_phases']]
+
+ # add ts column to seismicity dataframe
+ self.seismicity['ts'] = self.seismicity.time.map(
+ lambda x: (x - self.forecast_start).total_seconds())
+
+ self.ref_point = self.parameters.get('ref_point',[np.median(self.seismicity['Xs']),
+ np.median(self.seismicity['Ys']),
+ np.median(self.seismicity['Zs']),])
+
+ transformer = CoordinateTransformer(self.proj_string)
+ self.seismicity['Xs'], self.seismicity['Ys'], self.seismicity['Zs'] = \
+ transformer.to_local_coords(self.seismicity['longitude'],
+ self.seismicity['latitude'],
+ self.seismicity['depth'])
+
+ self.seismicity['Rs'] = np.sqrt((self.seismicity['Xs'] - self.ref_point[0]) ** 2 +
+ (self.seismicity['Ys'] - self.ref_point[1]) ** 2 +
+ (self.seismicity['Zs'] - self.ref_point[2]) ** 2)
+ # get the forecast times
+ self.forecast_moments = daterange(self.forecast_start,
+ self.forecast_end,
+ self.parameters['epoch_duration'],
+ self.observation_start)
+
+ self.scenario_df = self.scenario_df.resample(str(self.parameters['epoch_duration'])+'s').mean().interpolate(method='linear')
+
+ self.scenario_df = self.scenario_df[(self.scenario_df.index <= self.forecast_end) &
+ (self.scenario_df.index > self.forecast_start)]
+
+ sc_all_negative = (self.scenario_df['topflow'] < 0).all()
+ sc_all_identical = self.scenario_df['topflow'].nunique() == 1
+
+ if sc_all_negative and sc_all_identical:
+ self.scenario_df["topflow"]=(-self.scenario_df["topflow"].values[0]*
+ np.ones(len(self.scenario_df))*self.hydraulic_df["topflow"].values[-1])
+
+ sc_all_negative = (self.scenario_df['topflow'] < 0).all()
+ sc_all_identical = self.scenario_df['topflow'].nunique() == 1
+
+ if sc_all_negative and sc_all_identical:
+ self.scenario_df["topflow"]=(-self.scenario_df["topflow"].values[0]*
+ np.ones(len(self.scenario_df))*self.hydraulic_df["topflow"].values[-1])
+
+ self.training_catalog=self.seismicity.copy(deep=True)
+
+ def update_seeds_settings(self):
+ Mc, b_value = Calibrate_Observed_GR(
+ self.seismicity, self.dM, 'magnitude')
+ self.seed_settings["Mag_Completeness"] = Mc
+ self.seed_settings["b_value"] = b_value
+ self.training_catalog = self.seismicity[
+ self.seismicity['magnitude'] >= Mc]
+
+ if (self.seed_settings["b_vs_depth_range"]):
+ a, m = b_value_Lasso_calibration_1D(
+ np.array(self.training_catalog['Rs'].values),
+ np.array(self.training_catalog['magnitude'])[:, 0],
+ bins=100)
+ else:
+ a, m = b_value, np.zeros(1)
+
+ self.seed_settings["a"] = a
+ self.seed_settings["m"] = m
+
+ def ll_score(self, x):
+ y = self.CostFunction.y
+ log_factorial_y = self.CostFunction.logyfact
+ TestSettings = copy.deepcopy(self.seed_settings)
+
+ for i in range(len(self.SEISbounds)):
+ TestSettings[self.OptimizeKeys[i]] = x[i]
+ if (self.logscale[i]):
+ TestSettings[self.OptimizeKeys[i]] = np.power(10., x[i])
+
+ y0 = CAPS.Caps_Model_linear(self.LL_Moments[1:], self.LL_Edges[self.LL_Edges >= 0.], self.Training_R,
+ self.Training_MaxPressure, TestSettings, NSim=1, retriggering=self.retriggering)[0]
+
+ LL = np.sum(y * np.log(y0) - y0 - log_factorial_y)/len(self.training_catalog)# Poissonian LL
+ if np.isfinite(LL):
+ return -LL
+ return 1.e20
+
+ def ls_score(self, x):
+ y = self.CostFunction.y
+ TestSettings = copy.deepcopy(self.seed_settings)
+
+ for i in range(len(self.SEISbounds)):
+ TestSettings[self.OptimizeKeys[i]] = x[i]
+ if (self.logscale[i]):
+ TestSettings[self.OptimizeKeys[i]] = np.power(10., x[i])
+
+ y0 = CAPS.Caps_Model_linear(self.LL_Moments[1:], self.LL_Edges[self.LL_Edges >= 0.], self.Training_R,
+ self.Training_MaxPressure, TestSettings, NSim=1, retriggering=self.retriggering)[0]
+
+ LS = np.sum(np.square(y-y0))/len(y)
+ if np.isfinite(LS):
+ if LS < self.minf:
+ self.minf = LS
+ self.best_x = x
+ return LS
+ return 1.e20
+
+ def ls_score_seed(self, x):
+ y = self.CostFunction.y
+ TestSettings = copy.deepcopy(self.seed_settings)
+
+ for i in range(len(self.SEISbounds)):
+ TestSettings[self.OptimizeKeys[i]] = x[i]
+ if (self.logscale[i]):
+ TestSettings[self.OptimizeKeys[i]] = np.power(10., x[i])
+
+ # typed.List instance which is a compiled library backed, type-homogeneous list data type that is an improvement over the reflected-list: SeedsSettings = []
+ SeedsSettings = nb.typed.List()
+ for j in range(len(self.SEEDmodelKeys)):
+ SeedsSettings.append(float(TestSettings[self.SEEDmodelKeys[j]]))
+
+ y0 = SEED.Seed_Model_linear(self.LL_Moments[1:], self.LL_Edges[self.LL_Edges >= 0.],
+ self.flow_model.r, self.flow_model.Q[:,0],
+ self.pres_t_fit,
+ SeedsSettings, self.Nreal, self.retriggering)[1]
+
+ y1 = np.median(y0, axis=0)
+
+ LS = np.sum(np.square(y - y1)) / len(y)
+
+ if np.isfinite(LS):
+ if LS < self.minf:
+ self.minf = LS
+ self.best_x = x
+ return LS
+ return 1.e20
+
+ def save_iteration_step(self, x, convergence):
+ """
+ function to save history of parameters and residual during DEA
+ """
+ # check if equal
+ #np.testing.assert_equal(x, self.best_x)
+ self.dea_vals.append(x)
+ self.dea_func.append(self.minf)
+ return
+
+ def create_interpolator(self, t_grid, solution):
+ if len(self.flow_model.r)==len(solution[0]):
+ mesh = np.meshgrid(self.flow_model.r, t_grid)
+ else:
+ mesh = np.meshgrid(self.flow_model.r[1:], t_grid)
+ interpd = CloughTocher2DInterpolator(np.array([mesh[0].ravel(),mesh[1].ravel()]).T, solution.ravel(), fill_value=0)
+ return interpd
+
+ def Interpolate_1D(self, Forecasts_Moments, Forecasts_Edges):
+ """
+ Same as CAPS.InterpolateMaximumPressure, but using a self.PressureFunction created by the fit
+ """
+ RR=1#CAPS_KD_Settings["RefinementRatio"]
+ MyR=Refine_An_Edge(Forecasts_Edges, RR) # for 1D we use directly the cartesian grid as the radius
+ LaterKeepIndices=np.logical_and(np.isfinite(MyR), MyR > 0.)
+ My_Working_R =MyR[LaterKeepIndices]
+ DT=np.concatenate((self.timesteps, Forecasts_Moments))
+ MyT=np.sort(np.unique(DT))
+ MyPressure, MaxPressureAll =np.zeros([2, len(MyT), len(My_Working_R)])
+ PM=np.zeros(len(My_Working_R))
+ for timeIter in range(len(MyT)):
+ ConsiderTime=MyT[timeIter]
+ MyPressure[timeIter,:]=self.PressureFunction(My_Working_R,ConsiderTime)
+ MaxPressureAll[timeIter,:]=np.maximum(PM, MyPressure[timeIter,:])
+ PM=MaxPressureAll[timeIter,:]
+
+ TimeBinIndeces=np.intersect1d(Forecasts_Moments, MyT, return_indices=True)[2] # discard unecessary time discretization now
+ MaxPressure=MaxPressureAll[TimeBinIndeces]
+ dV = np.unique(np.diff(Forecasts_Edges, axis=0))
+ #dV=np.unique(np.diff(Forecasts_Edges, axis=0), axis=0)
+ #if(len(dV)>1):
+ #print ("Warning: non equidistant edges have been passed in the one direction. Correct array of control distances.")
+ #\todo: assume equidistant edges and refinement
+ My_Cell_Width=np.prod(np.amin(dV,axis=0)/RR)
+ return MaxPressure, My_Working_R, np.ones(len(My_Working_R))*My_Cell_Width
+
+ def Calibrate_CAPS(self, forecast_edges):
+ self.update_seeds_settings() # update b-value
+
+ if self.cost_fun=='LL':
+ self.CostFunction = LL_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ bins=[self.TrainingData.time.values[1:],
+ forecast_edges[0]])
+ else:
+ self.CostFunction = LS_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ bins=[self.TrainingData.time.values[1:],
+ forecast_edges[0]])
+ # self.CostFunction = LS_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ # bins=[self.TrainingData.time.values[1:],
+ # forecast_edges])
+
+ self.LL_Edges = self.CostFunction.edges_x2 # (radial) spatial bins in negative and positive directions
+ self.LL_Moments = self.CostFunction.edges_x1 # time moments
+
+ (self.Training_MaxPressure,
+ self.Training_R,
+ self.Training_Volume) = self.Interpolate_1D(self.LL_Moments, self.LL_Edges)
+
+ print("Maximum iterations CAPS:", self.maxiter)
+
+ # open list to save the progress of the minimization
+ self.dea_vals = []
+ self.dea_func = []
+
+ if self.cost_fun=='LL':
+ result = differential_evolution(self.ll_score, self.SEISbounds, polish=True, disp=self.disp,
+ maxiter=self.maxiter, seed=self.seed, popsize=10)
+ else:
+ result = differential_evolution(self.ls_score, self.SEISbounds, polish=True, disp=self.disp,
+ maxiter=self.maxiter, seed=self.seed, popsize=10,
+ updating='immediate', callback=self.save_iteration_step)
+ print("End CAPS differential evolution")
+
+ print('--------------------------------')
+ print('Calibrated geomechanical parameters:')
+ for i in range(len(result.x)):
+ self.seed_settings[self.OptimizeKeys[i]] = result.x[i]
+ if (self.logscale[i]):
+ self.seed_settings[self.OptimizeKeys[i]] = np.power(10., result.x[i])
+ print(str(self.OptimizeKeys[i]) + ': ' + str(self.seed_settings[self.OptimizeKeys[i]]))
+ print('--------------------------------')
+
+ # self.total_events_past_single, self.total_rates_past_single = CAPS.Caps_Model_linear(self.LL_Moments,
+ # self.LL_Edges[self.LL_Edges >= 0.],
+ # self.Training_R,
+ # self.Training_MaxPressure,
+ # self.seed_settings,
+ # NSim=1,
+ # retriggering=self.retriggering)
+ #
+ # print("sum of total events after calibration", self.total_events_past_single[0,-1], )
+
+ # save optimal parameters for single forecast later
+ self.seed_settings_optimal = dict.copy(self.seed_settings)
+
+ print("**************************************************")
+ print("Summary of optimized parameters after calibration:")
+ for i in range(len(self.SEISbounds)):
+ print(self.OptimizeKeys[i] + ': ', self.seed_settings_optimal[self.OptimizeKeys[i]], )
+ j_init = len(self.SEISbounds)
+ for j in range(len(self.FlowBounds)):
+ print(self.OptimizeKeys[j_init+j] + ': ', self.seed_settings_optimal[self.OptimizeKeys[j_init+j]], )
+ print("**************************************************")
+
+ self.total_events_past_grid, self.total_rates_past_grid = CAPS.Caps_Model_linear(self.LL_Moments,
+ self.LL_Edges[self.LL_Edges >= 0.],
+ self.Training_R,
+ self.Training_MaxPressure,
+ self.seed_settings,
+ NSim=self.NSim,
+ retriggering=self.retriggering)
+
+ total_events_median = np.round(np.median(self.total_events_past_grid, axis=0)[-1])
+
+ print("sum of total events after calibration (median from grid)", total_events_median, )
+
+ # if total_events_median==0:
+ # total_events_median_masked = self.mask_for_median(self.total_events_past_grid[0])
+ #
+ # print("sum of total events after calibration and mask (median from grid)", total_events_median_masked[-1], )
+
+ return
+
+ def Calibrate_SEED(self, forecast_edges):
+ self.update_seeds_settings() # update b-value
+
+ if self.cost_fun=='LL':
+ self.CostFunction = LL_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ bins=[self.TrainingData.time.values,np.linspace(0,max(self.training_catalog[self.Xs].values),10)[:,0]])
+ else:
+ self.CostFunction = LS_cost_function_1D(np.array(self.training_catalog[np.append(self.ts, self.Xs)]),
+ bins=[self.TrainingData.time.values,np.linspace(0,max(self.training_catalog[self.Xs].values),10)[:,0]])
+
+ self.LL_Edges = self.CostFunction.edges_x2
+ self.LL_Moments = self.CostFunction.edges_x1
+
+ # 1D pressure interpolation (in time only)
+ pres_sol = (self.flow_model.hidden_mechanism - self.TrainingData[self.Columns[-1]].values[0])/(1.e6)
+ self.pres_t_fit = np.array([np.interp(self.LL_Moments[1:], self.flow_model.Q[:,0], ps_row) for ps_row in pres_sol.T])
+ # OR:
+ # Tind = np.argmin(np.abs(self.flow_model.Q[:,0] - self.LL_Moments[1:][:, np.newaxis]), axis=1)
+ # self.pres_t_fit = (pres_sol[Tind,:]).T
+
+ # Decide for the seed model version (implemented since Poster FAM23)
+ if self.seed_model_version=='1':
+ self.Nreal = 100 # self.NSim
+ elif self.seed_model_version=='2':
+ self.Nreal = 1
+ else:
+ self.Nreal = self.NSim
+
+ print("Maximum iterations SEED:", self.maxiter)
+
+ # open list to save the progress of the minimization
+ self.dea_vals = []
+ self.dea_func = []
+
+ if self.cost_fun=='LL':
+ result = differential_evolution(self.ll_score_seed, self.SEISbounds,polish=True, disp=self.disp,
+ maxiter=self.maxiter, seed=self.seed,popsize=10)
+ else:
+ result = differential_evolution(self.ls_score_seed, self.SEISbounds, polish=True,
+ disp=self.disp, maxiter=self.maxiter,seed=self.seed, popsize=10,
+ updating='immediate', atol=1e-3, callback=self.save_iteration_step)
+ print("End SEED differential evolution")
+
+ print('------------------------------------')
+ print('Calibrated geomechanical parameters:')
+ for i in range(len(result.x)):
+ self.seed_settings[self.OptimizeKeys[i]] = result.x[i]
+ if (self.logscale[i]):
+ self.seed_settings[self.OptimizeKeys[i]] = np.power(10., result.x[i])
+ print(str(self.OptimizeKeys[i]) + ': ' + str(self.seed_settings[self.OptimizeKeys[i]]))
+ print('------------------------------------')
+
+ # save optimal parameters for forecast later
+ self.seed_settings_optimal = dict.copy(self.seed_settings)
+
+ print("**************************************************")
+ print("Summary of optimized parameters after calibration:")
+ for i in range(len(self.SEISbounds)):
+ print(self.OptimizeKeys[i] + ': ', self.seed_settings_optimal[self.OptimizeKeys[i]], )
+ j_init = len(self.SEISbounds)
+ for j in range(len(self.FlowBounds)):
+ print(self.OptimizeKeys[j_init+j] + ': ', self.seed_settings_optimal[self.OptimizeKeys[j_init+j]], )
+ print("**************************************************")
+
+ # typed.List instance which is a compiled library backed, type-homogeneous list data type that is an improvement over the reflected-list: SeedsSettings = []
+ SeedsSettings = nb.typed.List()
+ for j in range(len(self.SEEDmodelKeys)):
+ SeedsSettings.append(float(self.seed_settings[self.SEEDmodelKeys[j]]))
+
+ self.training_catalog, self.total_events_past_grid, self.total_rates_past_grid = SEED.Seed_Model_linear(self.LL_Moments[1:],
+ self.LL_Edges[self.LL_Edges >= 0.],
+ self.flow_model.r,
+ self.flow_model.Q[:,0],
+ self.pres_t_fit,
+ SeedsSettings,
+ self.NSim,
+ self.retriggering)
+
+ total_events_median = np.round(np.median(self.total_events_past_grid, axis=0)[-1])
+
+ print("sum of total events after calibration (median from grid)", total_events_median, )
+
+ return
+
+ def run(self):
+ """
+ Run the model. Calls self.fit() and returns the result of
+ self.predict().
+ """
+ self.fit()
+ results = self.predict()
+
+ results = self.parse_results(results)
+
+ return results
+
+ def fit(self):
+ self._logger.info('Model Fitting')
+
+ if self.flw_model == 'point_source':
+ self._logger.info('HM0: calibrating flow model (point source)....')
+ self.flow_model.fit(self.hydraulic_df[::2].values)
+ elif self.flw_model == 'line_source':
+ print("HM0: calibrating flow model (line source)....")
+ self.flow_model.fit(self.TrainingData[self.Columns[::2]].values)
+ # or:
+ # with open(filepath + 'HM0_line_source.pkl', 'rb') as f:
+ # self.flow_model = pickle.load(f)
+ # self.flow_model = self.flow_model[0]
+ elif self.flw_model == 'num_const_perm':
+ print("HM0: calibrating flow model (numerical linear)....")
+ self.flow_model.fit(self.TrainingData[self.Columns[:2]].values, self.TrainingData[self.Columns[::2]].values)
+ # or:
+ # with open(filepath + 'HM0_numerical_linear.pkl', 'rb') as f:
+ # self.flow_model = pickle.load(f)
+ # self.flow_model = self.flow_model[0]
+ elif self.flw_model == 'num_var_perm':
+ print("HM1: calibrating flow model (numerical non-linear)....")
+ self.flow_model.fit(self.TrainingData[self.Columns[:2]].values, self.TrainingData[self.Columns[::2]].values)
+ # or:
+ # with open(filepath + 'HM1_2.pkl', 'rb') as f:
+ # self.flow_model = pickle.load(f)
+ # self.flow_model = self.flow_model[0]
+ else:
+ print("HM1: calibrating flow model (numerical non-linear with FV)....")
+ self.flow_model.fit(self.TrainingData[self.Columns[:2]].values, self.TrainingData[self.Columns[::2]].values)
+
+ print('--------------------------------')
+ print('Calibrated hydraulic parameters:')
+ for i in range(len(self.flow_model.params)):
+ self.seed_settings[self.OptimizeKeys[i-len(self.flow_model.params)]] = self.flow_model.params[i]
+ print(str(self.OptimizeKeys[i-len(self.flow_model.params)]) + ': ' + str(self.flow_model.params[i]))
+ print('--------------------------------')
+
+ self.timesteps = self.TrainingData.time.values
+ inipres=self.TrainingData[self.Columns[-1]].values[0]
+
+ if self.seis_model=='CAPS':
+ print("HM0/1: calibrating CAPS....")
+ self.PressureFunction = self.create_interpolator(self.timesteps, (self.flow_model.hidden_mechanism - inipres) / 1.e6)
+ self.Calibrate_CAPS(forecast_edges)
+ else:
+ print("HM0/1: calibrating SEED....")
+ self.Calibrate_SEED(forecast_edges)
+
+ return
+
+ def predict(self):
+ self._logger.info('Model Predicting')
+ # **************************************************************
+ # Multi-LASSO model forecast #
+
+ # forecast data
+ plan_t = np.array((self.scenario_df.index.values
+ - self.hydraulic_df.index.values[0]).tolist()) / 1.e9
+ plan_dV = np.maximum(self.scenario_df[self.column_flow].values, 0)
+
+# if self.Dtau == self._epoch_duration:
+# forecast_moments_hires = self._forecast_moments
+# else:
+# taubins = np.arange(self._forecast_moments[0], self._forecast_moments[-1] + self.Dtau, self.Dtau)
+# forecast_moments_hires = taubins
+
+ forecast_moments_hires = np.arange(
+ self.forecast_moments[0], self.forecast_moments[-1] + self.Dtau, self.Dtau)
+ # forecast_moments_hires =self.forecast_moments
+ if self.m >= len(forecast_moments_hires):
+ self.m = len(forecast_moments_hires) - 1
+
+ forecast_dV = np.interp(forecast_moments_hires, plan_t, plan_dV)
+ planned_flow = np.vstack((forecast_moments_hires, forecast_dV)).T
+ if self.Pmodel == 'FlowModels0-1':
+ forecast_P0 = self.Pcalib0.predict(planned_flow)[:, 0]
+ forecast_P1 = self.Pcalib1.predict(planned_flow)[:, 0]
+ else:
+ forecast_P = self.Pcalib.predict(planned_flow)[:, 0]
+ X_test = self.X_train[-1, :][np.newaxis, :]
+
+ # LASSO prediction
+ y_pred_mLASSO = np.zeros(self.m)
+ for i in range(self.m):
+ X_test[:, 0] = forecast_dV[i]
+ if self.Pmodel == 'FlowModels0-1':
+ X_test[:, 7] = forecast_P0[i]
+ X_test[:, 8] = forecast_P1[i]
+ else:
+ X_test[:, 7] = forecast_P[i]
+ y_pred_mLASSO[i] = self.mLASSO_trained['subLASSO'
+ + str(i + 1)].predict(X_test)
+ # clip to zero in case of unstable result at very high m
+ y_pred_mLASSO[y_pred_mLASSO < 0] = 0
+
+ # filling gaps for tau_i after horizon m with EM1 frequentist (since LASSO tends to EM1 for m -> Inf.)
+ plan_V = np.cumsum(plan_dV * np.diff(np.append(plan_t[0], plan_t)))
+ forecast_V = np.interp(forecast_moments_hires, plan_t, plan_V)
+ # self.y_pred_fill = np.append(np.diff(10 ** (self.af - self.b_value * self.Mc) * forecast_V), 0)
+ self.y_pred_fill = np.diff(
+ 10 ** (self.af - self.b_value * self.Mc) * forecast_V)
+ self.y_pred = np.copy(self.y_pred_fill)
+ indLASSO = np.arange(self.m)
+ self.y_pred[indLASSO] = y_pred_mLASSO[indLASSO]
+
+ self.y_pred = self.y_pred.reshape((len(forecast_moments_hires[1:]), 1))
+ self.a_value = (np.log10(self.y_pred + 1e-20) + self.b_value
+ * self.Mc).reshape((len(forecast_moments_hires[1:]), 1))
+ if self.m > 0:
+ self.y_fit = self.mLASSO_trained['subLASSO1'].predict(self.X_train)
+ else:
+ self.y_fit = np.zeros(self.X_train.shape[0])
+ self.y_fit[self.y_fit <= 0] = 0
+
+ # resampling
+ if self.Dtau < self.parameters['epoch_duration']:
+ self._logger.info('Resampling Output')
+ factor = self.parameters['epoch_duration'] / self.Dtau
+ self.y_pred = factor * interp1d(forecast_moments_hires[1:], self.y_pred[:, 0], kind='linear')(
+ self.forecast_moments[1:])[:, np.newaxis]
+ self.a_value = (np.log10(self.y_pred + 1e-20) + self.b_value
+ * self.Mc).reshape((len(self.forecast_moments[1:]), 1))
+ self.y_pred_fill = factor * \
+ interp1d(forecast_moments_hires[1:], self.y_pred_fill, kind='linear')(
+ self.forecast_moments[1:])
+
+ NN = ((np.max(self.dt_fit) - np.min(self.dt_fit))
+ / self.parameters['epoch_duration']).astype(int) + 1
+ self._time_fit = np.linspace(
+ np.min(self.dt_fit), np.max(self.dt_fit), NN)
+ self.dt_fit_mids = self._time_fit[1:] - \
+ self.parameters['epoch_duration'] / 2
+ self.rate_fit = factor * \
+ interp1d(self.tmids, self.y_fit, kind='linear')(
+ self.dt_fit_mids)[:, np.newaxis]
+ self.rate_train = factor * \
+ interp1d(self.tmids, self.y_train, kind='linear')(
+ self.dt_fit_mids)[:, np.newaxis]
+
+ # simulations
+ if self.Nsim > 1:
+ mLASSO_pred = np.zeros((self.y_pred.shape[0], self.Nsim))
+ for t in range(self.y_pred.shape[0]):
+ mLASSO_pred[t, :] = np.random.poisson(
+ self.y_pred[t, 0], self.Nsim)
+
+ bsim = np.zeros(self.Nsim)
+ for sim in range(self.Nsim):
+ msim = self.Mc - \
+ np.log(1 - np.random.random(self.fit_N)) / \
+ (self.b_value * np.log(10))
+ bsim[sim] = np.log10(np.exp(1.)) / \
+ (np.mean(msim) - (self.Mc - self.dM / 2))
+ mLASSO_b = bsim # np.tile(bsim, (self.y_pred.shape[0], 1))
+
+ mLASSO_a = np.log10(mLASSO_pred + 1e-20) + mLASSO_b * self.Mc
+ mLASSO_afb = mLASSO_b * self.Mc + \
+ np.log10(self.fit_N_shutin) - np.log10(self.fit_Vtot)
+ else:
+ mLASSO_pred = self.y_pred
+ mLASSO_b = self.b_value # np.full_like(mLASSO_pred, self.b_value)
+ mLASSO_a = self.a_value
+ mLASSO_afb = self.af
+ # mLASSO_Mc = np.full_like(mLASSO_a, self.Mc)
+ ##############################
+
+ # For each volume and timestep generate 5 values of interest
+ # (a, alpha, mc, b, rate) with n_sim samples each.
+ return mLASSO_a, mLASSO_afb, mLASSO_b, self.rate_fit, mLASSO_pred
+
+ def parse_results(self, results):
+ """
+ Parse Results to a ForecastGRRateGrid per timestep.
+ A ForecastGRRateGrid requires at least the following columns:
+ 'number_events', 'longitude_min', 'longitude_max',
+ 'latitude_min', 'latitude_max', 'depth_min', 'depth_max',
+ 'a', 'b', 'mc', 'grid_id'
+
+ """
+ fit_datetimes = [self.observation_start + timedelta(seconds=t)
+ for t in self._time_fit]
+ forecast_datetimes = [self.observation_start + timedelta(seconds=t)
+ for t in self.forecast_moments]
+
+ bounds = list(wkt.loads(self.geometry.bounding_polygon).bounds)
+ bounds.insert(2, self.geometry.altitude_min)
+ bounds.insert(5, self.geometry.altitude_max)
+
+ parsed_results = []
+
+ for i in range(len(fit_datetimes) - 1):
+ rate = {'number_events': results[3][i],
+ 'longitude_min': bounds[0],
+ 'longitude_max': bounds[3],
+ 'latitude_min': bounds[1],
+ 'latitude_max': bounds[4],
+ 'depth_min': bounds[2],
+ 'depth_max': bounds[5],
+ # self.af,
+ 'a': np.log10(len(self.seismicity[self.seismicity['magnitude'] > self.Mc])) + self.b_value * self.Mc,
+ 'alpha': self.af,
+ 'b': self.b_value,
+ 'mc': self.Mc,
+ 'grid_id': 0}
+
+ # Create a ForecastGRRateGrid for each Timestep
+ rategrid = ForecastGRRateGrid(rate,
+ starttime=fit_datetimes[i],
+ endtime=fit_datetimes[i + 1])
+
+ parsed_results.append(rategrid)
+
+ for i in range(len(forecast_datetimes) - 1):
+ rate = {'number_events': results[4][i],
+ 'longitude_min': [bounds[0]] * self.Nsim,
+ 'longitude_max': [bounds[3]] * self.Nsim,
+ 'latitude_min': [bounds[1]] * self.Nsim,
+ 'latitude_max': [bounds[4]] * self.Nsim,
+ 'depth_min': [bounds[2]] * self.Nsim,
+ 'depth_max': [bounds[5]] * self.Nsim,
+ 'a': results[0][i],
+ 'alpha': results[1],
+ 'b': results[2],
+ 'mc': [self.Mc] * self.Nsim,
+ 'grid_id': list(range(self.Nsim))}
+
+ # Create a ForecastGRRateGrid for each Timestep
+ rategrid = ForecastGRRateGrid(rate,
+ starttime=forecast_datetimes[i],
+ endtime=forecast_datetimes[i + 1])
+
+ parsed_results.append(rategrid)
+
+ return parsed_results
diff --git a/hm1d/ramsis.py b/hm1d/ramsis.py
new file mode 100644
index 0000000..0a5fa4e
--- /dev/null
+++ b/hm1d/ramsis.py
@@ -0,0 +1,44 @@
+from ramsis_model import ModelInput, validate_entrypoint
+
+from hm1d.model import HM1D
+
+
+def run_hm1d(model_input: dict):
+ """
+ Entry Point for RT-RAMSIS
+
+ This method gets called by the RT-RAMSIS framework. It is responsible for
+ initializing the model and running the model with the given input.
+ """
+
+ model_input = ModelInput(**model_input)
+
+ model = HM1D(model_input)
+
+ model.fit()
+
+ results = model.predict()
+
+ results = model.parse_results(results)
+
+ return results
+
+
+@validate_entrypoint(induced=True)
+def run_hm1d_validated(model_input: ModelInput):
+ """
+ Entry Point for RT-RAMSIS
+
+ This method gets called by the RT-RAMSIS framework. It is responsible for
+ initializing the model and running the model with the given input.
+ """
+
+ model = HM1D(model_input)
+
+ model.fit()
+
+ results = model.predict()
+
+ results = model.parse_results(results)
+
+ return results
diff --git a/hm1d/utils.py b/hm1d/utils.py
new file mode 100644
index 0000000..f9dab85
--- /dev/null
+++ b/hm1d/utils.py
@@ -0,0 +1,237 @@
+# Copyright 2018, ETH Zurich - Swiss Seismological Service SED
+"""
+Miscellaneous HM1 model core facilities.
+"""
+
+import numpy as np
+from scipy import stats, special
+from sklearn import linear_model
+from matplotlib import pyplot as plt
+
+# SEISMOSTATS
+from seismostats.analysis.estimate_beta import estimate_b_tinti, shi_bolt_confidence
+from seismostats.analysis.estimate_mc import mc_max_curvature
+from seismostats.utils.binning import bin_to_precision
+
+def daterange(start_date,
+ end_date,
+ epoch_duration=None,
+ training_start_date=None):
+ """Numpy array returned with forecasting intervals in seconds
+ increasing with constant increment, inclusive of the end_date.
+
+ :param st art_date: Datetime for generator to start with.
+ :param end_date: Datetime for generator to finish with.
+ :param epoch_duration: Number of seconds between returned values.
+
+ :rtype: np.array of seconds from training start time to forecast for
+ """
+ total_seconds = (end_date - start_date).total_seconds()
+ if not epoch_duration:
+ epoch_duration = total_seconds
+
+ # the +1 is required to match the r code which gives the range
+ # including the final variable.
+ daterange_list = [
+ i * epoch_duration for i
+ in range(int(total_seconds / epoch_duration) + 1)]
+ if training_start_date:
+ daterange_list = [i + (
+ start_date - training_start_date).total_seconds()
+ for i in daterange_list]
+ date_range_array = np.array(daterange_list)
+ return date_range_array
+
+
+def Calibrate_Observed_GR(catalog, dM=0.02,col_name='Mw'):
+ """
+ Very similar to ramsis.EM1 mc_mode
+ The Frequency Mass Density (FMD) is found by smoothing
+ observed Magnitudes (M) with Gaussian kernels. Then
+ the completion Magnitude (Mc) is set to the rounded value
+ at the peak of the FMD.
+
+ The slope of the Gutenberg-Richter law is returned with
+ the MLE method, Aki (1965) and for the found Mc.
+
+ NOTE: It returns Nan when less than 4 events are passed.
+ The scientific implications behind it need to be further
+ studied (the same applies for the ramsis.EM1). E.g. binomial
+ is more possible; here the mode with the largest M is chosen.
+
+ :param Magnitudes: magnitudes of incomplete catalog
+ :param dM : size of binning and of rounding Mc
+
+ :rtype: float for the Mc
+ :rtype: float for the b-value
+ """
+ #print("starting the GR")
+ Magnitudes = np.array(catalog[col_name].values)
+ Mc, b = np.full(2, np.nan)
+ #print("inputs to gr: ", catalog)
+ #print("len magnitudes: ", len(Magnitudes))
+ if (len(Magnitudes) > 4):
+ Magnitudes = Magnitudes[~np.isnan(Magnitudes)]
+ Mrange = np.around(np.arange(np.amax(Magnitudes) + 5 * dM, np.amin(Magnitudes) - 5 * dM, -dM),
+ decimals=int(-np.log(dM)))
+ argMc = np.argmax(stats.gaussian_kde(Magnitudes)(Mrange))
+ #Mc = round(Mrange[argMc]/dM)*dM
+ Mc = Mrange[argMc]
+ print("mc", Mc)
+ # b=np.log10(np.exp(1.))/(np.mean(Magnitudes[np.where(Magnitudes>=Mc)])-np.min(Magnitudes[np.where(Magnitudes>=Mc)]))
+ b = np.log10(np.exp(1.)) / (np.mean(Magnitudes[np.where(Magnitudes >= Mc)]) - (Mc - dM / 2))
+ print('b', b)
+ return Mc, b
+
+def b_value_Lasso_calibration_1D(Xs, Mw, bins=100):
+ Xs = Xs[:,0] # this enables the right dimensions for the histogram (1-D)
+ counts, edges = np.histogram(Xs, bins=bins)
+ counts = counts.flatten() # returns a copy collapsed into one dimension
+ meanMw, edges = np.histogram(Xs, bins=edges, weights=Mw)
+ meanMw = meanMw.flatten()
+
+ BinsGrid = np.meshgrid(edges[:-1] + 0.5 * (edges[1:] - edges[:-1]))
+
+ X = np.array([BinsGrid[0].flatten()])
+
+ Keep = np.where(counts > 0)
+ X = X[:, Keep[0]].transpose()
+ Y = meanMw[Keep] * np.reciprocal(counts[Keep])
+
+ clf = linear_model.Lasso(alpha=0.1)
+ clf.fit(X, Y)
+ return clf.intercept_, clf.coef_
+
+def Refine_An_Edge (Coarse_Edges_1D, RR=5):
+ Coarse_Cells=np.sort(np.unique(Coarse_Edges_1D.ravel()), kind='mergesort')
+ MyFinerCells=np.zeros((len(Coarse_Cells)-1)*RR)
+ for i in range(len(Coarse_Cells)-1):
+ Delta=(Coarse_Cells[i+1]-Coarse_Cells[i])/RR
+ for ii in range(RR):
+ MyFinerCells[RR*i+ii]=Coarse_Cells[i]+Delta*(ii+0.5)
+ return MyFinerCells
+
+def catalogue_statistics_calculation(cat, binning=0.01, corr_factor=0.025):
+ """
+ Routine to calculate the statistics of an input catalogue using Seismostats
+ """
+ magnitudes = bin_to_precision(cat.mag.to_numpy(), delta_x=binning)
+ Mc = mc_max_curvature(sample=magnitudes, delta_m=binning, correction_factor=corr_factor)
+ b_value = estimate_b_tinti(magnitudes=magnitudes[magnitudes>=Mc], mc=Mc, b_parameter='b_value', delta_m=binning)
+ error_b_value = shi_bolt_confidence(magnitudes=magnitudes[magnitudes>=Mc], b=b_value, b_parameter='b_value')
+ return Mc, b_value, error_b_value
+
+class LL_cost_function_1D:
+ def __init__(self, x, bins=10):
+ self.y, self.edges_x1, self.edges_x2 = np.histogram2d(x[:,0],x[:,1], bins=bins, density=False)
+ #self.y = np.cumsum(self.y, axis=0)
+ self.y = self.y.ravel()
+ self.logyfact = np.log(special.factorial(self.y)).ravel()
+
+ def score(self, y1):
+ ll = self.y*np.log(y1.ravel())-y1.ravel()-self.logyfact
+ return np.sum(ll)
+
+class LS_cost_function_1D:
+ def __init__(self, x, bins=(10, 10, 10, 10)):
+ self.y, self.edges_x1, self.edges_x2 = np.histogram2d(x[:,0],x[:,1], bins=bins, density=False)
+ self.y = np.cumsum(np.sum(self.y,axis=1))
+
+
+ def score(self, y1):
+ ls = np.sum(np.square(self.y-y1))
+ return np.nanmean(ls)
+
+def plotting_data(model):
+ fig, ax = plt.subplots(1, 2, figsize=(20, 6))
+
+ ax[0].scatter(model.seismicity.time, (model.seismicity['magnitude'] - np.min(model.seismicity['magnitude'])) / \
+ (np.max(model.seismicity['magnitude'] - np.min(model.seismicity['magnitude']))) / 4, \
+ color='lightgrey', alpha=.5, zorder=2, label='seismics')
+ ax[0].step(model.hydraulic_df.index, model.hydraulic_df['topflow'] / \
+ np.max(model.hydraulic_df['topflow']) / 4, color='darkblue', label='hydraulics[topflow]')
+ ax[0].step(model.hydraulic_df.index, model.hydraulic_df['toppressure'] / \
+ np.max(model.hydraulic_df['toppressure']) / 4, color='darkred', label='hydraulics[toppressure]')
+ ax[0].set_xlabel('Date')
+ ax[0].set_ylabel('Normalised inputs')
+ ax[0].set_title('Training data')
+ ax[0].legend(loc='upper left')
+ ax[0].spines['right'].set_visible(False)
+ ax[0].spines['top'].set_visible(False)
+
+ ax[1].plot(model.hydraulic_df.index, model.hydraulic_df['topflow'], color='darkblue', \
+ label='hydraulics[topflow]')
+ ax[1].plot(model.scenario_df.index, model.scenario_df[model.column_flow], color='royalblue', label='scenario')
+ ax[1].set_xlabel('Date')
+ ax[1].set_ylabel('Flow rate [m$^3$/s]')
+ ax[1].set_title('Flow time series')
+ ax[1].legend()
+ ax[1].spines['right'].set_visible(False)
+ ax[1].spines['top'].set_visible(False)
+ fig.tight_layout();
+
+def plotting_results(model,model_output, seism_df=pd.DataFrame()):
+
+ inj_t = np.array((model.hydraulic_df.index.values - model.hydraulic_df.index.values[0]).tolist()) / 1.e9
+ Dtau = model.model_input.model_parameters['epoch_duration']
+
+ NN = ((inj_t[-1] - inj_t[0])
+ / Dtau).astype(int) + 1
+ time_fit = np.linspace(
+ inj_t[0], inj_t[-1], NN)
+
+ time_fit_mids = time_fit[1:]
+
+ time_forecast_mids = model.forecast_moments[1:]- Dtau/2
+
+ seism = model.seismicity
+ seism = seism[seism['magnitude'] >= model.Mc]
+ seism.sort_values(by='time',inplace=True)
+ seism_t = np.array((seism['time'].values - model.hydraulic_df.index.values[0]).tolist()) / 1.e9
+
+ # range pred
+
+ y_pred_mean = np.median(model_output[4], axis=1)
+ up = np.percentile(model_output[4],axis=1,q=75)
+ down = np.percentile(model_output[4],axis=1,q=25)
+ down[down < 0] = 0
+
+ if not seism_df.empty:
+ seism_t_all = np.array((seism_df['time'].values - model.hydraulic_df.index.values[0]).tolist()) / 1.e9
+ ind = np.logical_and(seism_t_all > 0, seism_t_all < model.forecast_moments[-1])
+ seism_df_filt = seism_df[ind]
+ seism_t_all = seism_t_all[ind]
+ seism_t_all_aboveMc = seism_t_all[seism_df_filt['magnitude'] >= model.Mc]
+ taubins_all = np.arange(0, model.forecast_moments[-1] + Dtau, Dtau)
+ tmids_all = taubins_all[1:] - Dtau / 2
+ rate_all = np.histogram(seism_t_all_aboveMc, bins=taubins_all)
+
+ ##
+ fig, ax = plt.subplots(1, 2, figsize=(20, 6))
+
+ ax[0].scatter(seism_t, np.arange(1, len(seism_t) + 1), color='darkgrey', label='observations')
+ ax[0].plot(time_fit_mids,np.cumsum(model_output[3]), color='darkgreen', label='model fit')
+ ax[0].set_xlabel('Time increment [s]')
+ ax[0].set_ylabel('Cumulative number of events')
+ ax[0].set_title('Training fit')
+ ax[0].legend()
+ ax[0].spines['right'].set_visible(False)
+ ax[0].spines['top'].set_visible(False)
+
+ # ax[1].plot(model.res_fit_t, model.rate_train, color = 'lightgrey', label = 'observations')
+ ax[1].plot(time_fit_mids, model_output[3], color='darkgreen', label='model fit')
+ ax[1].plot(time_forecast_mids, y_pred_mean, color='darkred', label='model prediction')
+ ax[1].scatter(time_forecast_mids, y_pred_mean, color='darkred')
+ ax[1].fill_between(time_forecast_mids, down, up, facecolor='darkred', alpha=.3)
+ if not seism_df.empty:
+ ax[1].step(tmids_all, rate_all[0], where='mid', label='data', color='darkgrey')
+ # ax[1].fill_between(tmids_all, 0, rate_all[0], color='grey', alpha = .5)
+ #ax[1].set_xlim(0, 50000)
+ #ax[1].set_ylim(0, 150)
+ ax[1].set_xlabel('Time increment [s]')
+ ax[1].set_ylabel('Event rate [time bin]')
+ ax[1].set_title('Event-rate time series')
+ ax[1].legend(loc='upper left')
+ ax[1].spines['right'].set_visible(False)
+ ax[1].spines['top'].set_visible(False)
+ #fig.tight_layout()
\ No newline at end of file
diff --git a/ml1/FlowModel.py b/ml1/FlowModel.py
new file mode 100644
index 0000000..688cd38
--- /dev/null
+++ b/ml1/FlowModel.py
@@ -0,0 +1,330 @@
+import numpy as np
+from scipy.special import expi
+from scipy.special import erfc
+from scipy.optimize import differential_evolution
+import pickle
+
+########################################################################################################################
+########################################## ANALYTICAL FLOW MODEL #######################################################
+########################################################################################################################
+
+def Analytical_LineSource_Solution(InjectionStrategy, t_grid, r_grid, h, S, k):
+ """
+ Args:
+ InjectionStrategy (2xNt array): doublets of each consecutive step:
+ duration of step and injection rate
+ (only Neumann's b.c. at the well)
+ t_grid (Numpy vector): consecutive time moments
+ r_grid (Numpy vector): radii
+ k (scalar) : permeability [m]
+ S (scalar) : storage (porosity * compressibility) [Pa^-1]
+ Returns:
+ the analytical solution stimulation factor (Numpy vector) for the
+ input time moments and radii
+ """
+ Nt_end = len(t_grid)
+ sol = np.zeros([Nt_end, len(r_grid)])
+ Qsteps = np.array(InjectionStrategy[:, 1])
+ Qsteps[1:] = np.diff(Qsteps)
+ TimeSteps = np.array(InjectionStrategy[:, 0])
+ NumOfSteps = len(Qsteps)
+
+ for step in range(NumOfSteps):
+ JJ = np.where(t_grid > TimeSteps[step])
+ sol[JJ, :] = np.add(sol[JJ, :],
+ # StepInjection_Green_Function_linesource(r_grid, (t_grid[JJ] - TimeSteps[step]), Qsteps[step],
+ # Diff))
+ StepInjection_Green_Function_linesource(r_grid, (t_grid[JJ] - TimeSteps[step]), Qsteps[step],
+ h, S, k))
+ return sol
+
+def Analytical_PointSource_Solution(InjectionStrategy, t_grid, r_grid, A, S, k):
+ """
+ Args:
+ InjectionStrategy (2xNt array): doublets of each consecutive step:
+ duration of step and injection rate
+ (only Neumann's b.c. at the well)
+ t_grid (Numpy vector): consecutive time moments
+ r_grid (Numpy vector): radii
+ k (scalar) : permeability [m]
+ S (scalar) : storage (porosity * compressibility) [Pa^-1]
+ Returns:
+ the analytical solution stimulation factor (Numpy vector) for the
+ input time moments and radii
+ """
+ Nt_end = len(t_grid)
+ sol = np.zeros([Nt_end, len(r_grid)])
+ Qsteps = np.array(InjectionStrategy[:, 1])
+ Qsteps[1:] = np.diff(Qsteps)
+ TimeSteps = np.array(InjectionStrategy[:, 0])
+ NumOfSteps = len(Qsteps)
+
+ for step in range(NumOfSteps):
+ JJ = np.where(t_grid > TimeSteps[step])
+ sol[JJ, :] = np.add(sol[JJ, :],
+ # StepInjection_Green_Function_linesource(r_grid, (t_grid[JJ] - TimeSteps[step]), Qsteps[step],
+ # Diff))
+ StepInjection_Green_Function_pointsource(r_grid, (t_grid[JJ] - TimeSteps[step]), Qsteps[step],
+ A, S, k))
+ return sol
+
+def StepInjection_Green_Function_linesource(r, t, Q, h, S, k):
+ # def StepInjection_Green_Function_linesource(r, t, Q, Diff, val):
+ """
+ Args:
+ r (Numpy vector): radius of point(s) in meters
+ t (Numpy vector): time moment(s) in seconds
+ Q (scalar) : source term [m^3/(s)]
+ k (scalar) : permeability [m]
+ S (scalar) : storage (porosity * compressibility) [Pa^-1]
+ Returns:
+ the 1D analytical solution (Numpy vector) for continuous line source type of injection
+ REF: Wu and Pruess, 2000, IJRMMS, 37, 51-61 -> Theis solution
+ similar with Diff REF: pag. 261, Eq. (2) - book: “Conduction of Heat in Solids†di Carslaw and Jaeger
+ """
+ mu = 0.0010016 # water visosity at 20 ËšC
+
+ X = expi(-np.matmul(np.reciprocal(t.reshape((-1, 1))), np.square(r.reshape((1, -1)))) * mu * S / k / 4.)
+
+ return (-Q * mu/ (4. * np.pi * k * h) ) * X
+
+def StepInjection_Green_Function_pointsource(r, t, Q, A, S, k):
+ # def StepInjection_Green_Function_linesource(r, t, Q, Diff, val):
+ """
+ Args:
+ r (Numpy vector): radius of point(s) in meters
+ t (Numpy vector): time moment(s) in seconds
+ Q (scalar) : source term [m^3/(s)]
+ k (scalar) : permeability [m]
+ S (scalar) : storage (porosity * compressibility) [Pa^-1]
+ Returns:
+ the 1D analytical solution (Numpy vector) for continuous point source type of injection
+ REF: Wu and Pruess, 2000, IJRMMS, 37, 51-61 -> Theis solution
+ """
+ mu = 0.0010016 # water visosity at 20 ËšC
+
+ xi = np.matmul(np.reciprocal(np.sqrt(t.reshape((-1, 1)))), r.reshape((1, -1))) / (np.sqrt(4. * k / S / mu))
+
+ X = 1./(np.sqrt(np.pi) * xi)*np.exp(-xi**2) - erfc(xi)
+
+ return (Q * mu * r.reshape((1, -1)) / (k * A)) * X
+
+class FlowModel0:
+ """
+ POINT SOURCE MODEL
+
+ Initialization with radius, numpy array with time and flow, as well as
+ crossectional area, model parameters initial guess, and their bounds when used for fitting
+ Model parameters are the storage coefficient (namely porosity times compressibility)
+ and the intrinsic rock permeability
+
+ Simple usage for forward simulation:
+
+ radius = np.geomspace(0.1, 2000, 30, endpoint=True) # radius for calculation
+ time_Q= np.linspace(0, 1e8, 2000) # time vector for flow
+ time_out = np.linspace(0, 1e8, 2000) # time vector for output, this can be different than time_Q as long as is larger
+ Q = np.ones((2000)*0.01 # volumetric injection rate in m3/s
+ QQ = np.vstack((time_Q.T,Q.T))
+ S = 1.e-9 # storage
+ k = 1.e-14 # permeability
+ rw = 0.15 #radius well
+ xsection = 2 * np.pi * rw**2 # cross sectional area
+ Model = FlowModel0(Q = QQ,r_grid = radius,A = xsection, iniguess=(S, k))
+ Pressure = Model.predict() # numpy vector of pressure solution at time and radius
+
+ Simple usage for inversion:
+
+ radius = np.geomspace(0.1, 2000, 30, endpoint=True) # radius for calculation
+ TrainingTarget = 2xN array with the time moment and the pressure in Pa
+ time_Q= np.linspace(0, 1e8, 2000) # time vector for flow
+ Q = np.ones((2000)*0.01 # volumetric injection rate in m3/s
+ TrainingData = np.vstack((time_Q.T,Q.T))
+ rw = 0.15 #radius well
+ xsection = 2 * np.pi * h * rw # cross sectional area
+ Model = FlowModel0(Q = TrainingData,r_grid = radius, A= xsection, bounds=[(1e-15 1e-5), (1e-20, 1e-12)], iniguess=(1e-9, 1e-14))
+ Model.fit(TrainingTarget) # numpy vector of pressure solution at time and radius
+ Pressure = Model.predict()
+
+ For forecasting future pressure after fitting an injection Plan is needed
+ Q2 = Q*2
+ time2 = np.linspace(0, 1e8, 2000) +time[-1]
+ Plan = np.vstack((time2.T,Q2.T)
+ Prediction = Model.predict(Plan)
+ """
+ def __init__(self,
+ Q, #TrainingData for fitting
+ r_grid=np.geomspace(0.1, 2000, 30, endpoint=True),
+ A=90,
+ bounds=[(1e-15, 1e-5), (1e-20, 1e-12)],
+ iniguess=(1e-9, 1e-14)):
+ self.r = r_grid
+ self.A = A
+ self.Q = Q
+ self.bounds = np.log10(bounds)
+ self.params = iniguess
+ self.observation = np.array([0])
+ self.model = Analytical_PointSource_Solution
+
+ def Cost(self, x):
+ S = np.power(10.,x[0])
+ k = np.power(10.,x[1])
+ _prediction = self.model(self.Q, self.t_target, self.r_target, self.A, S, k)+self.observation[0]
+ #_prediction = self.model(self.Q, self.t_target, self.r_target, self.A, S, k)
+ Coeff_R2 = np.sum(np.square(_prediction/1.e6 - self.observation/1.e6))/len(self.observation)
+
+
+ return Coeff_R2
+
+ def fit(self, TrainingTarget,
+ popsize=10,
+ polish=True,
+ disp=False,
+ maxiter=100,
+ seed=22,
+ tol = 1e-5):
+ """
+ Args:
+ TrainingData (Numpy array) : 2xN array with the time moment and the injection rate until next measurement
+ TrainingTarget(Numpy array): 2xM array with the time moment and pressure measurement
+ Returns:
+
+ """
+ self.t_target = TrainingTarget[:, 0]
+ self.observation = TrainingTarget[:, 1:]
+ self.r_target = self.r[:len(self.observation[0, :])]
+
+ self.iter_results = []
+ result = differential_evolution(self.Cost, self.bounds,popsize=popsize,polish=polish,
+ disp=disp, maxiter=maxiter, seed=seed, tol=tol)
+ self.params = np.power(10., result.x[0]), \
+ np.power(10., result.x[1])
+
+ self.hidden_mechanism = self.predict()
+
+ def fm_iteration(self, xk, convergence):
+ self.iter_results.append(xk)
+
+ def predict(self, Plan=[],):
+ """
+ Args:
+ Plan (Numpy array) : 2xNf array with the time moment and the injection rate expected from that moment.
+ Returns:
+ (Numpy array) : 2x(Nf+N) array with the time moment and the pressure due to the plan and the training data
+ """
+ Q = self.Q
+ t_grid = Q[:, 0]
+ if (len(Plan) > 0):
+ Q = np.vstack((Q, Plan))
+ t_grid = Plan[:, 0]
+
+ Prediction = self.model(Q, t_grid, self.r, self.A, self.params[0], self.params[1])+self.observation[0]
+ return Prediction
+
+class FlowModel1:
+ """
+ LINE SOURCE MODEL (Theis' solution)
+
+ Initialization with radius, numpy array with time and flow, as well as
+ reservoir thickness, model parameters initial guess, and their bounds when used for fitting
+ Model parameters are the storage coefficient (namely porosity times compressibility)
+ and the intrinsic rock permeability
+
+ Simple usage for forward simulation:
+
+ radius = np.geomspace(0.1, 2000, 30, endpoint=True) # radius for calculation
+ time_Q= np.linspace(0, 1e8, 2000) # time vector for flow
+ time_out = np.linspace(0, 1e8, 2000) # time vector for output, this can be different than time_Q as long as is larger
+ Q = np.ones((2000)*0.01 # volumetric injection rate in m3/s
+ QQ = np.vstack((time_Q.T,Q.T))
+ S = 1.e-9 # storage
+ k = 1.e-14 # permeability
+ thick = 100 #layer thickness
+ Model = FlowModel1(Q = QQ,r_grid = radius,h = thick, iniguess=(S, k))
+ Pressure = Model.predict() # numpy vector of pressure solution at time and radius
+
+ Simple usage for inversion:
+
+ radius = np.geomspace(0.1, 2000, 30, endpoint=True) # radius for calculation
+ TrainingTarget = 2xN array with the time moment and the pressure in Pa
+ time_Q= np.linspace(0, 1e8, 2000) # time vector for flow
+ Q = np.ones((2000)*0.01 # volumetric injection rate in m3/s
+ TrainingData = np.vstack((time_Q.T,Q.T))
+ thick = 100 #layer thickness
+ Model = FlowModel1(Q=TrainingData, r_grid=radius, h=thick, bounds=[(1e-15 1e-5), (1e-20, 1e-12)], iniguess=(1e-9, 1e-14))
+ Model.fit(TrainingTarget) # numpy vector of pressure solution at time and radius
+ Pressure = Model.predict()
+
+ For forecasting future pressure after fitting an injection Plan is needed
+ Q2 = Q*2
+ time2 = np.linspace(0, 1e8, 2000) +time[-1]
+ Plan = np.vstack((time2.T,Q2.T)
+ Prediction = Model.predict(Plan)
+ """
+ def __init__(self,
+ Q,
+ r_grid=np.geomspace(0.1, 2000, 30, endpoint=True),
+ h=100,
+ bounds=[(1e-15, 1e-5), (1e-20, 1e-12)],
+ iniguess=(1e-9, 1e-14)):
+ self.r = r_grid
+ self.h = h
+ self.Q = Q
+
+ self.bounds = np.log10(bounds)
+ self.params = iniguess
+ self.observation = np.array([0])
+ self.model = Analytical_LineSource_Solution
+
+ def Cost(self, x):
+ S = np.power(10.,x[0])
+ k = np.power(10.,x[1])
+# _prediction = self.model(self.Q, self.t_target, self.r_target, self.h, S, k)+self.observation[0]
+ _prediction = self.model(self.Q, self.t_target, self.r_target, self.h, S, k)
+ Coeff_R2 = np.sum(np.square(_prediction/1.e6 - self.observation/1.e6))/len(self.observation)
+
+ return Coeff_R2
+
+ def fit(self, TrainingTarget,
+ popsize=10,
+ polish=True,
+ disp=False,
+ maxiter=100,
+ seed=22,
+ tol = 1e-5):
+ """
+ Args:
+ TrainingData (Numpy array) : 2xN array with the time moment and the injection rate until next measurement
+ TrainingTarget(Numpy array): 2xM array with the time moment and pressure measurement
+ Returns:
+
+ """
+ self.t_target = TrainingTarget[:, 0]
+ self.observation = TrainingTarget[:, 1:] # gets rid of the column in index nr. 1 (so the second column)
+ self.r_target = self.r[:len(self.observation[0, :])]
+
+
+ self.iter_results = []
+ result = differential_evolution(self.Cost, self.bounds,popsize=popsize,polish=polish,
+ disp=disp, maxiter=maxiter, seed=seed, tol=tol, callback=self.fm_iteration)
+ self.params = np.power(10., result.x[0]), \
+ np.power(10., result.x[1])
+
+ self.hidden_mechanism = self.predict()
+
+ def fm_iteration(self, xk, convergence):
+ self.iter_results.append(xk)
+
+ def predict(self, Plan=[],):
+ """
+ Args:
+ Plan (Numpy array) : 2xNf array with the time moment and the injection rate expected from that moment.
+ Returns:
+ (Numpy array) : 2x(Nf+N) array with the time moment and the pressure due to the plan and the training data
+ """
+ Q = self.Q
+ t_grid = Q[:, 0]
+ if (len(Plan) > 0):
+ Q = np.vstack((Q, Plan))
+ t_grid = Plan[:, 0]
+ Prediction = self.model(Q, t_grid, self.r, self.h, self.params[0], self.params[1])+self.observation[0]
+ return Prediction
diff --git a/ml1/__init__.py b/ml1/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/ml1/model.py b/ml1/model.py
new file mode 100644
index 0000000..b5c1c6c
--- /dev/null
+++ b/ml1/model.py
@@ -0,0 +1,455 @@
+import logging
+from datetime import timedelta
+
+import numpy as np
+from hydws.parser import BoreholeHydraulics
+from ramsis_model import ModelInput
+from scipy import stats
+from scipy.interpolate import interp1d
+from seismostats.seismicity.catalog import Catalog
+from seismostats.seismicity.rategrid import ForecastGRRateGrid
+from shapely import wkt
+from sklearn.linear_model import Lasso
+
+from hm1d.utils import daterange, def_Xy, detect_shutin
+
+
+class HM1D():
+ """ InducedSFM Class for multi-LASSO model
+
+ Available variables after successfull initialization:
+
+ Input Hydraulic and Seismic data is available as DataFrames as:
+ self._hydraulics,
+ self._scenario,
+ self._seismics
+
+ Variables from input dict are also available as class properties:
+ self._model_parameters
+ self._forecast_start
+ self._forecast_end
+ self._epoch_duration
+ self._proj_string
+
+ Additionally are available:
+ self._forecast_moments
+ self._section_metadata
+ self._observation_start
+ self._observation_end
+
+ A logger is available with self._logger.* (info, warning, error, ...)
+
+ """
+
+ def __init__(self, model_input: ModelInput, **kwargs):
+ """
+ Gets called after the aforementioned variables are instantiated, eg.
+ model setup can live in here.
+ """
+ # set up logger
+ self._logger = logging.getLogger(__name__)
+ self._logger.info('Model Initialized')
+
+ self.model_input = model_input
+ self.parameters = model_input.model_parameters
+
+ self.dM = self.parameters['dM']
+ self.M_dec = self.parameters['M_decimals']
+ self.Mc = self.parameters['Mc']
+ self.column_flow = "topflow"
+ self.column_pressure = "toppressure"
+ self.m = self.parameters['LASSO_m']
+ self.alpha = self.parameters['LASSO_penalty']
+ self.Nmin_fit = self.parameters['LASSO_Nmin_fit']
+ self.Nsim = self.parameters['Nsim']
+ self.Dtau = self.parameters['Delta_tau']
+ self.Pmodel = self.parameters['Pmodel']
+
+ section_id = self.parameters['well_section_publicid']
+
+ self.geometry = model_input.geometry
+
+ self.forecast_start = model_input.forecast_start
+ self.forecast_end = model_input.forecast_end
+
+ self.hydraulics = BoreholeHydraulics(model_input.injection_well[0])
+
+ self.scenario = BoreholeHydraulics(model_input.injection_plan)
+
+ self.seismicity = Catalog.from_quakeml(model_input.seismic_catalog,
+ includequality=True)
+
+ # get dataframes from HYDSJONParser
+ self.hydraulic_df = self.hydraulics[section_id].hydraulics
+ self.hydraulic_df = self.hydraulic_df[~self.hydraulic_df['topflow'].isna()]
+ self.hydraulic_df = self.hydraulic_df[~self.hydraulic_df['toppressure'].isna()]
+
+ self.scenario_df = self.scenario[section_id].hydraulics
+
+ self.observation_start = self.hydraulic_df.index.min()
+ self.observation_end = np.array(
+ self.forecast_start).astype('datetime64')
+
+ self.hydraulic_df = self.hydraulic_df[self.hydraulic_df.index
+ < self.observation_end]
+ self.seismicity = self.seismicity[self.seismicity['time']
+ < self.forecast_start]
+
+ # self.observation_end = self.hydraulic_df.index.max()
+ # self.observation_end = min(self.observation_end, self.forecast_start)
+
+ self.seismicity = self.seismicity[~self.seismicity['magnitude'].isna()]
+
+ if 'associatedphasecount' in self.seismicity.columns:
+ self.seismicity['associatedphasecount'] = \
+ self.seismicity['associatedphasecount'].astype(float)
+ # is this sensible?
+ self.seismicity['associatedphasecount'] = \
+ self.seismicity['associatedphasecount'].fillna(0)
+
+ self.seismicity = \
+ self.seismicity[self.seismicity['associatedphasecount'] >= self.parameters['n_phases']]
+
+ # add ts column to seismicity dataframe
+ self.seismicity['ts'] = self.seismicity.time.map(
+ lambda x: (x - self.forecast_start).total_seconds())
+
+ # get the forecast times
+ self.forecast_moments = daterange(self.forecast_start,
+ self.forecast_end,
+ self.parameters['epoch_duration'],
+ self.observation_start)
+
+ self.scenario_df = self.scenario_df.resample(str(self.parameters['epoch_duration'])+'s').mean().interpolate(method='linear')
+
+ self.scenario_df = self.scenario_df[(self.scenario_df.index <= self.forecast_end) &
+ (self.scenario_df.index > self.forecast_start)]
+
+ sc_all_negative = (self.scenario_df['topflow'] < 0).all()
+ sc_all_identical = self.scenario_df['topflow'].nunique() == 1
+
+ if sc_all_negative and sc_all_identical:
+ self.scenario_df["topflow"]=(-self.scenario_df["topflow"].values[0]*
+ np.ones(len(self.scenario_df))*self.hydraulic_df["topflow"].values[-1])
+
+ sc_all_negative = (self.scenario_df['topflow'] < 0).all()
+ sc_all_identical = self.scenario_df['topflow'].nunique() == 1
+
+ if sc_all_negative and sc_all_identical:
+ self.scenario_df["topflow"]=(-self.scenario_df["topflow"].values[0]*
+ np.ones(len(self.scenario_df))*self.hydraulic_df["topflow"].values[-1])
+
+ def run(self):
+ """
+ Run the model. Calls self.fit() and returns the result of
+ self.predict().
+ """
+ self.fit()
+ results = self.predict()
+
+ results = self.parse_results(results)
+
+ return results
+
+ def fit(self):
+ self._logger.info('Model Fitting')
+
+ # ************************************************************
+ # Multi-LASSO model fitting #
+
+ # raw operational data
+ self.dt_fit = np.array(
+ (self.hydraulic_df.index.values
+ - self.hydraulic_df.index.values[0]).tolist()) / 1.e9
+ inj_dV = np.maximum(self.hydraulic_df[self.column_flow].values, 0)
+ inj_P = self.hydraulic_df[self.column_pressure].values
+
+ # raw seismicity data
+ seism = self.seismicity
+ # Mc estimation if not given as input
+ if self.Mc is None:
+ Mrange = np.around(np.arange(np.amax(seism['magnitude'].values)
+ + 5 * self.dM, np.amin(
+ seism['magnitude'].values) - 5 * self.dM, -self.dM),
+ decimals=int(-np.log(self.dM)))
+ argMc = np.argmax(stats.gaussian_kde(
+ seism['magnitude'].values)(Mrange))
+ self.Mc = Mrange[argMc]
+
+ seism = seism[seism['magnitude'] >= self.Mc]
+ seism_t = np.array(
+ (seism['time'].values
+ - self.hydraulic_df.index.values[0]).tolist()) / 1.e9
+ seism_m = seism['magnitude'].values
+
+ # find end of injection in training data, otherwise end of file
+ t_shutin, dV_shutin, Vtot = detect_shutin(self.dt_fit, inj_dV)
+ N_shutin = len(seism_t[seism_t <= t_shutin])
+
+ # Maximum Likelihood estimation b-value
+ self.b_value = np.log10(np.exp(1.)) / \
+ (np.mean(seism_m) - (self.Mc - self.dM / 2))
+
+ # for simulations in predict()
+ self.fit_N = len(seism_m)
+ self.fit_N_shutin = N_shutin
+ self.fit_Vtot = Vtot
+
+ self.af = self.b_value * self.Mc + np.log10(N_shutin) - np.log10(Vtot)
+
+ # features
+ taubins = np.arange(
+ self.dt_fit[0], self.dt_fit[-1] + self.Dtau, self.Dtau)
+ self.tmids = taubins[1:] - self.Dtau / 2
+
+ if self.Pmodel == 'FlowModels0-1':
+ self.X_train, self.y_train, self.Pcalib0, self.Pcalib1 = def_Xy(
+ seism_t, self.dt_fit, inj_dV, inj_P,
+ taubins, self.Dtau, self.Pmodel)
+ else:
+ self.X_train, self.y_train, self.Pcalib = def_Xy(
+ seism_t, self.dt_fit, inj_dV, inj_P,
+ taubins, self.Dtau, self.Pmodel)
+
+ # model training
+ self._logger.info('LASSO m pre:' + str(self.m))
+ mLASSO = {}
+ for i in range(self.m):
+ if i < len(self.y_train) - self.Nmin_fit:
+ y_train_sub = np.append(self.y_train[i:], np.repeat(np.nan, 0))
+ X_train_sub = np.copy(self.X_train)
+ X_train_sub[:, 0] = np.append(
+ X_train_sub[i:, 0], np.repeat(np.nan, i))
+ subLASSO = Lasso(alpha=self.alpha, positive=True)
+ indnoNA = np.where(~np.isnan(X_train_sub[:, 0]))[0]
+ subLASSO.fit(X_train_sub[indnoNA], y_train_sub[indnoNA])
+ mLASSO['subLASSO' + str(i + 1)] = subLASSO
+ self.mLASSO_trained = mLASSO
+ # updated when horizon too long for training
+ self.m = len(self.mLASSO_trained)
+ self._logger.info('LASSO m:' + str(self.m))
+
+ if self.m > 0:
+ if self.Pmodel == 'FlowModels0-1':
+ self._logger.info('Estimated parameters -> dVplan: '
+ + str(mLASSO['subLASSO1'].coef_[0])
+ + ', dVmean: '
+ + str(mLASSO['subLASSO1'].coef_[1])
+ + ', dVdiff: '
+ + str(mLASSO['subLASSO1'].coef_[2])
+ + ', Pmean: '
+ + str(mLASSO['subLASSO1'].coef_[3])
+ + ', Pdiff: '
+ + str(mLASSO['subLASSO1'].coef_[4])
+ + ', tauneg: '
+ + str(mLASSO['subLASSO1'].coef_[5])
+ + ', yprev:'
+ + str(mLASSO['subLASSO1'].coef_[6])
+ + ', Pplan0: '
+ + str(mLASSO['subLASSO1'].coef_[7])
+ + ', Pplan1: '
+ + str(mLASSO['subLASSO1'].coef_[8])
+ ) # tmp - to access LASSO fit
+ else:
+ self._logger.info('Estimated parameters -> dVplan: '
+ + str(mLASSO['subLASSO1'].coef_[0])
+ + ', dVmean: '
+ + str(mLASSO['subLASSO1'].coef_[1])
+ + ', dVdiff: '
+ + str(mLASSO['subLASSO1'].coef_[2])
+ + ', Pmean: '
+ + str(mLASSO['subLASSO1'].coef_[3])
+ + ', Pdiff: '
+ + str(mLASSO['subLASSO1'].coef_[4])
+ + ', tauneg: '
+ + str(mLASSO['subLASSO1'].coef_[5])
+ + ', yprev:'
+ + str(mLASSO['subLASSO1'].coef_[6])
+ + ', Pplan: '
+ + str(mLASSO['subLASSO1'].coef_[7])
+ ) # tmp - to access LASSO fit
+
+ def predict(self):
+ self._logger.info('Model Predicting')
+ # **************************************************************
+ # Multi-LASSO model forecast #
+
+ # forecast data
+ plan_t = np.array((self.scenario_df.index.values
+ - self.hydraulic_df.index.values[0]).tolist()) / 1.e9
+ plan_dV = np.maximum(self.scenario_df[self.column_flow].values, 0)
+
+# if self.Dtau == self._epoch_duration:
+# forecast_moments_hires = self._forecast_moments
+# else:
+# taubins = np.arange(self._forecast_moments[0], self._forecast_moments[-1] + self.Dtau, self.Dtau)
+# forecast_moments_hires = taubins
+
+ forecast_moments_hires = np.arange(
+ self.forecast_moments[0], self.forecast_moments[-1] + self.Dtau, self.Dtau)
+ # forecast_moments_hires =self.forecast_moments
+ if self.m >= len(forecast_moments_hires):
+ self.m = len(forecast_moments_hires) - 1
+
+ forecast_dV = np.interp(forecast_moments_hires, plan_t, plan_dV)
+ planned_flow = np.vstack((forecast_moments_hires, forecast_dV)).T
+ if self.Pmodel == 'FlowModels0-1':
+ forecast_P0 = self.Pcalib0.predict(planned_flow)[:, 0]
+ forecast_P1 = self.Pcalib1.predict(planned_flow)[:, 0]
+ else:
+ forecast_P = self.Pcalib.predict(planned_flow)[:, 0]
+ X_test = self.X_train[-1, :][np.newaxis, :]
+
+ # LASSO prediction
+ y_pred_mLASSO = np.zeros(self.m)
+ for i in range(self.m):
+ X_test[:, 0] = forecast_dV[i]
+ if self.Pmodel == 'FlowModels0-1':
+ X_test[:, 7] = forecast_P0[i]
+ X_test[:, 8] = forecast_P1[i]
+ else:
+ X_test[:, 7] = forecast_P[i]
+ y_pred_mLASSO[i] = self.mLASSO_trained['subLASSO'
+ + str(i + 1)].predict(X_test)
+ # clip to zero in case of unstable result at very high m
+ y_pred_mLASSO[y_pred_mLASSO < 0] = 0
+
+ # filling gaps for tau_i after horizon m with EM1 frequentist (since LASSO tends to EM1 for m -> Inf.)
+ plan_V = np.cumsum(plan_dV * np.diff(np.append(plan_t[0], plan_t)))
+ forecast_V = np.interp(forecast_moments_hires, plan_t, plan_V)
+ # self.y_pred_fill = np.append(np.diff(10 ** (self.af - self.b_value * self.Mc) * forecast_V), 0)
+ self.y_pred_fill = np.diff(
+ 10 ** (self.af - self.b_value * self.Mc) * forecast_V)
+ self.y_pred = np.copy(self.y_pred_fill)
+ indLASSO = np.arange(self.m)
+ self.y_pred[indLASSO] = y_pred_mLASSO[indLASSO]
+
+ self.y_pred = self.y_pred.reshape((len(forecast_moments_hires[1:]), 1))
+ self.a_value = (np.log10(self.y_pred + 1e-20) + self.b_value
+ * self.Mc).reshape((len(forecast_moments_hires[1:]), 1))
+ if self.m > 0:
+ self.y_fit = self.mLASSO_trained['subLASSO1'].predict(self.X_train)
+ else:
+ self.y_fit = np.zeros(self.X_train.shape[0])
+ self.y_fit[self.y_fit <= 0] = 0
+
+ # resampling
+ if self.Dtau < self.parameters['epoch_duration']:
+ self._logger.info('Resampling Output')
+ factor = self.parameters['epoch_duration'] / self.Dtau
+ self.y_pred = factor * interp1d(forecast_moments_hires[1:], self.y_pred[:, 0], kind='linear')(
+ self.forecast_moments[1:])[:, np.newaxis]
+ self.a_value = (np.log10(self.y_pred + 1e-20) + self.b_value
+ * self.Mc).reshape((len(self.forecast_moments[1:]), 1))
+ self.y_pred_fill = factor * \
+ interp1d(forecast_moments_hires[1:], self.y_pred_fill, kind='linear')(
+ self.forecast_moments[1:])
+
+ NN = ((np.max(self.dt_fit) - np.min(self.dt_fit))
+ / self.parameters['epoch_duration']).astype(int) + 1
+ self._time_fit = np.linspace(
+ np.min(self.dt_fit), np.max(self.dt_fit), NN)
+ self.dt_fit_mids = self._time_fit[1:] - \
+ self.parameters['epoch_duration'] / 2
+ self.rate_fit = factor * \
+ interp1d(self.tmids, self.y_fit, kind='linear')(
+ self.dt_fit_mids)[:, np.newaxis]
+ self.rate_train = factor * \
+ interp1d(self.tmids, self.y_train, kind='linear')(
+ self.dt_fit_mids)[:, np.newaxis]
+
+ # simulations
+ if self.Nsim > 1:
+ mLASSO_pred = np.zeros((self.y_pred.shape[0], self.Nsim))
+ for t in range(self.y_pred.shape[0]):
+ mLASSO_pred[t, :] = np.random.poisson(
+ self.y_pred[t, 0], self.Nsim)
+
+ bsim = np.zeros(self.Nsim)
+ for sim in range(self.Nsim):
+ msim = self.Mc - \
+ np.log(1 - np.random.random(self.fit_N)) / \
+ (self.b_value * np.log(10))
+ bsim[sim] = np.log10(np.exp(1.)) / \
+ (np.mean(msim) - (self.Mc - self.dM / 2))
+ mLASSO_b = bsim # np.tile(bsim, (self.y_pred.shape[0], 1))
+
+ mLASSO_a = np.log10(mLASSO_pred + 1e-20) + mLASSO_b * self.Mc
+ mLASSO_afb = mLASSO_b * self.Mc + \
+ np.log10(self.fit_N_shutin) - np.log10(self.fit_Vtot)
+ else:
+ mLASSO_pred = self.y_pred
+ mLASSO_b = self.b_value # np.full_like(mLASSO_pred, self.b_value)
+ mLASSO_a = self.a_value
+ mLASSO_afb = self.af
+ # mLASSO_Mc = np.full_like(mLASSO_a, self.Mc)
+ ##############################
+
+ # For each volume and timestep generate 5 values of interest
+ # (a, alpha, mc, b, rate) with n_sim samples each.
+ return mLASSO_a, mLASSO_afb, mLASSO_b, self.rate_fit, mLASSO_pred
+
+ def parse_results(self, results):
+ """
+ Parse Results to a ForecastGRRateGrid per timestep.
+ A ForecastGRRateGrid requires at least the following columns:
+ 'number_events', 'longitude_min', 'longitude_max',
+ 'latitude_min', 'latitude_max', 'depth_min', 'depth_max',
+ 'a', 'b', 'mc', 'grid_id'
+
+ """
+ fit_datetimes = [self.observation_start + timedelta(seconds=t)
+ for t in self._time_fit]
+ forecast_datetimes = [self.observation_start + timedelta(seconds=t)
+ for t in self.forecast_moments]
+
+ bounds = list(wkt.loads(self.geometry.bounding_polygon).bounds)
+ bounds.insert(2, self.geometry.altitude_min)
+ bounds.insert(5, self.geometry.altitude_max)
+
+ parsed_results = []
+
+ for i in range(len(fit_datetimes) - 1):
+ rate = {'number_events': results[3][i],
+ 'longitude_min': bounds[0],
+ 'longitude_max': bounds[3],
+ 'latitude_min': bounds[1],
+ 'latitude_max': bounds[4],
+ 'depth_min': bounds[2],
+ 'depth_max': bounds[5],
+ # self.af,
+ 'a': np.log10(len(self.seismicity[self.seismicity['magnitude'] > self.Mc])) + self.b_value * self.Mc,
+ 'alpha': self.af,
+ 'b': self.b_value,
+ 'mc': self.Mc,
+ 'grid_id': 0}
+
+ # Create a ForecastGRRateGrid for each Timestep
+ rategrid = ForecastGRRateGrid(rate,
+ starttime=fit_datetimes[i],
+ endtime=fit_datetimes[i + 1])
+
+ parsed_results.append(rategrid)
+
+ for i in range(len(forecast_datetimes) - 1):
+ rate = {'number_events': results[4][i],
+ 'longitude_min': [bounds[0]] * self.Nsim,
+ 'longitude_max': [bounds[3]] * self.Nsim,
+ 'latitude_min': [bounds[1]] * self.Nsim,
+ 'latitude_max': [bounds[4]] * self.Nsim,
+ 'depth_min': [bounds[2]] * self.Nsim,
+ 'depth_max': [bounds[5]] * self.Nsim,
+ 'a': results[0][i],
+ 'alpha': results[1],
+ 'b': results[2],
+ 'mc': [self.Mc] * self.Nsim,
+ 'grid_id': list(range(self.Nsim))}
+
+ # Create a ForecastGRRateGrid for each Timestep
+ rategrid = ForecastGRRateGrid(rate,
+ starttime=forecast_datetimes[i],
+ endtime=forecast_datetimes[i + 1])
+
+ parsed_results.append(rategrid)
+
+ return parsed_results
diff --git a/ml1/ramsis.py b/ml1/ramsis.py
new file mode 100644
index 0000000..c68c919
--- /dev/null
+++ b/ml1/ramsis.py
@@ -0,0 +1,44 @@
+from ramsis_model import ModelInput, validate_entrypoint
+
+from hm1d.model import HM1D
+
+
+def run_ml1(model_input: dict):
+ """
+ Entry Point for RT-RAMSIS
+
+ This method gets called by the RT-RAMSIS framework. It is responsible for
+ initializing the model and running the model with the given input.
+ """
+
+ model_input = ModelInput(**model_input)
+
+ model = HM1D(model_input)
+
+ model.fit()
+
+ results = model.predict()
+
+ results = model.parse_results(results)
+
+ return results
+
+
+@validate_entrypoint(induced=True)
+def run_ml1_validated(model_input: ModelInput):
+ """
+ Entry Point for RT-RAMSIS
+
+ This method gets called by the RT-RAMSIS framework. It is responsible for
+ initializing the model and running the model with the given input.
+ """
+
+ model = HM1D(model_input)
+
+ model.fit()
+
+ results = model.predict()
+
+ results = model.parse_results(results)
+
+ return results
diff --git a/ml1/utils.py b/ml1/utils.py
new file mode 100644
index 0000000..2e013db
--- /dev/null
+++ b/ml1/utils.py
@@ -0,0 +1,255 @@
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+
+from hm1d.FlowModel import FlowModel0, FlowModel1
+
+
+def fmd(m, mbin, decimals):
+ m = np.round(m, decimals = decimals)
+ mmin = np.min(m)
+ mmax = np.max(m)
+ mi = np.arange(np.round(np.floor(mmin / mbin) * mbin, decimals = 1), \
+ np.round(np.ceil(mmax / mbin) * mbin, decimals = 1), mbin)
+ bins = np.arange(np.round(np.floor(mmin / mbin) * mbin, decimals = 1), \
+ np.round(np.ceil(mmax / mbin) * mbin + mbin, decimals = 1), mbin)-mbin/2
+ ni, _ = np.histogram(m, bins = bins)
+ Ni = [np.sum(ni[i:len(ni)]) for i in range(len(ni))]
+ distr = pd.DataFrame()
+ distr['mi'] = mi
+ distr['ni'] = ni[0:len(mi)]
+ distr['Ni'] = Ni[0:len(mi)]
+ return distr
+
+def detect_shutin(t, dV): # used for the frequentist EM1 mask (to fill gaps after m)
+ V = np.cumsum(dV[1:] * np.diff(t))
+ t_shutin = t[np.argmax(V) + 1]
+ dV_shutin = dV[np.argmax(V) + 1]
+ Vtot = np.amax(V)
+ return t_shutin, dV_shutin, Vtot
+
+
+def daterange(start_date,
+ end_date,
+ epoch_duration=None,
+ training_start_date=None):
+ """Numpy array returned with forecasting intervals in seconds
+ increasing with constant increment, inclusive of the end_date.
+
+ :param st art_date: Datetime for generator to start with.
+ :param end_date: Datetime for generator to finish with.
+ :param epoch_duration: Number of seconds between returned values.
+
+ :rtype: np.array of seconds from training start time to forecast for
+ """
+ total_seconds = (end_date - start_date).total_seconds()
+ if not epoch_duration:
+ epoch_duration = total_seconds
+
+ # the +1 is required to match the r code which gives the range
+ # including the final variable.
+ daterange_list = [
+ i * epoch_duration for i
+ in range(int(total_seconds / epoch_duration) + 1)]
+ if training_start_date:
+ daterange_list = [i + (
+ start_date - training_start_date).total_seconds()
+ for i in daterange_list]
+ date_range_array = np.array(daterange_list)
+ return date_range_array
+
+
+def def_Xy(seism_t, inj_t, inj_dV, inj_P, tbins, dt, Pmodel):
+ tmids = tbins[1:] - dt / 2
+ x_dVplan = np.interp(tmids, inj_t, inj_dV) # flow rate at tau_i (known since planned) = X_1
+ y_obs, _ = np.histogram(seism_t, bins=tbins) # target seismicity rate at tau_i
+ x_yprev = np.insert(y_obs[:-1], 0, 0)
+
+ # define other X_2(tau_{i-1}) features below
+ N = len(tmids)
+ x_dVmean = np.zeros(N)
+ x_dVmin = np.zeros(N)
+ x_dVmax = np.zeros(N)
+ x_dVdiff = np.zeros(N)
+ x_Pmean = np.zeros(N)
+ x_Pmin = np.zeros(N)
+ x_Pmax = np.zeros(N)
+ x_Pdiff = np.zeros(N)
+ x_tauneg = np.zeros(N)
+ for tau in np.arange(1, N):
+ # at tau Minus 1 bin (observed)
+ ind_tauM1 = np.where(np.logical_and(inj_t >= tbins[tau - 1], inj_t < tbins[tau]))[0]
+ if len(ind_tauM1) > 0:
+ x_dVmean[tau] = np.nanmean(inj_dV[ind_tauM1].astype(float))
+ x_dVmin[tau] = np.nanmin(inj_dV[ind_tauM1].astype(float))
+ x_dVmax[tau] = np.nanmax(inj_dV[ind_tauM1].astype(float))
+ x_dVdiff[tau] = x_dVmax[tau] - x_dVmin[tau]
+ x_Pmean[tau] = np.nanmean(inj_P[ind_tauM1].astype(float))
+ x_Pmin[tau] = np.nanmin(inj_P[ind_tauM1].astype(float))
+ x_Pmax[tau] = np.nanmax(inj_P[ind_tauM1].astype(float))
+ x_Pdiff[tau] = x_Pmax[tau] - x_Pmin[tau]
+ else:
+ if tau > 0: # if no data in bin, considers previous bin as data
+ x_dVmean[tau] = x_dVmean[tau - 1]
+ x_dVmin[tau] = x_dVmin[tau - 1]
+ x_dVmax[tau] = x_dVmax[tau - 1]
+ x_dVdiff[tau] = x_dVdiff[tau - 1]
+ x_Pmean[tau] = x_Pmean[tau - 1]
+ x_Pmin[tau] = x_Pmin[tau - 1]
+ x_Pmax[tau] = x_Pmax[tau - 1]
+ x_Pdiff[tau] = x_Pdiff[tau - 1]
+ x_tauneg[tau] = -tau
+
+ ## pressure model ##
+ iniguess = [1e-09, 1e-14]
+ xsection = 90 # cross-sectional area for point source model (FlowModel0)
+ layer_thick = 30 # thickness for line source model (FlowModel1)
+ max_grid_ext = 2000 # meters
+ recorded_flow = np.vstack((tmids, x_dVmean)).T
+ recorded_pressure = np.vstack((tmids, x_Pmean)).T
+ planned_flow = np.vstack((tmids, x_dVplan)).T
+ if Pmodel == 'FlowModel0':
+ flow_model = FlowModel0(recorded_flow, r_grid=np.geomspace(.1, max_grid_ext, 30, endpoint=True), \
+ A=xsection, iniguess=iniguess)
+ flow_model.fit(recorded_pressure)
+ P_model = flow_model.predict(planned_flow) # Ntimes X Mdistances
+ x_Pplan = P_model[:, 0]
+ if Pmodel == 'FlowModel1':
+ flow_model = FlowModel1(recorded_flow, r_grid=np.geomspace(.1, max_grid_ext, 30, endpoint=True), \
+ h=layer_thick, iniguess=iniguess)
+ flow_model.fit(recorded_pressure)
+ P_model = flow_model.predict(planned_flow) # Ntimes X Mdistances
+ x_Pplan = P_model[:, 0]
+ if Pmodel == 'FlowModels0-1':
+ flow_model0 = FlowModel0(recorded_flow, r_grid=np.geomspace(.1, max_grid_ext, 30, endpoint=True), \
+ A=xsection, iniguess=iniguess)
+ flow_model0.fit(recorded_pressure)
+ P_model0 = flow_model0.predict(planned_flow) # Ntimes X Mdistances
+ x_Pplan0 = P_model0[:, 0]
+ flow_model1 = FlowModel1(recorded_flow, r_grid=np.geomspace(.1, max_grid_ext, 30, endpoint=True), \
+ h=layer_thick, iniguess=iniguess)
+ flow_model1.fit(recorded_pressure)
+ P_model1 = flow_model1.predict(planned_flow) # Ntimes X Mdistances
+ x_Pplan1 = P_model1[:, 0]
+
+ if Pmodel == 'FlowModels0-1':
+ X = np.concatenate((x_dVplan[:, np.newaxis],
+ x_dVmean[:, np.newaxis],
+ x_dVdiff[:, np.newaxis],
+ x_Pmean[:, np.newaxis],
+ x_Pdiff[:, np.newaxis],
+ x_tauneg[:, np.newaxis],
+ x_yprev[:, np.newaxis],
+ x_Pplan0[:, np.newaxis],
+ x_Pplan1[:, np.newaxis]), axis=1)
+ return X, y_obs, flow_model0, flow_model1
+ else:
+ X = np.concatenate((x_dVplan[:, np.newaxis],
+ x_dVmean[:, np.newaxis],
+ x_dVdiff[:, np.newaxis],
+ x_Pmean[:, np.newaxis],
+ x_Pdiff[:, np.newaxis],
+ x_tauneg[:, np.newaxis],
+ x_yprev[:, np.newaxis],
+ x_Pplan[:, np.newaxis]), axis=1)
+ return X, y_obs, flow_model
+
+# temporary function to log predictions
+def array_to_string(arr):
+ return ', '.join(str(x) for x in arr)
+
+
+
+def plotting_data(model):
+ fig, ax = plt.subplots(1, 2, figsize=(20, 6))
+
+ ax[0].scatter(model.seismicity.time, (model.seismicity['magnitude'] - np.min(model.seismicity['magnitude'])) / \
+ (np.max(model.seismicity['magnitude'] - np.min(model.seismicity['magnitude']))) / 4, \
+ color='lightgrey', alpha=.5, zorder=2, label='seismics')
+ ax[0].step(model.hydraulic_df.index, model.hydraulic_df['topflow'] / \
+ np.max(model.hydraulic_df['topflow']) / 4, color='darkblue', label='hydraulics[topflow]')
+ ax[0].step(model.hydraulic_df.index, model.hydraulic_df['toppressure'] / \
+ np.max(model.hydraulic_df['toppressure']) / 4, color='darkred', label='hydraulics[toppressure]')
+ ax[0].set_xlabel('Date')
+ ax[0].set_ylabel('Normalised inputs')
+ ax[0].set_title('Training data')
+ ax[0].legend(loc='upper left')
+ ax[0].spines['right'].set_visible(False)
+ ax[0].spines['top'].set_visible(False)
+
+ ax[1].plot(model.hydraulic_df.index, model.hydraulic_df['topflow'], color='darkblue', \
+ label='hydraulics[topflow]')
+ ax[1].plot(model.scenario_df.index, model.scenario_df[model.column_flow], color='royalblue', label='scenario')
+ ax[1].set_xlabel('Date')
+ ax[1].set_ylabel('Flow rate [m$^3$/s]')
+ ax[1].set_title('Flow time series')
+ ax[1].legend()
+ ax[1].spines['right'].set_visible(False)
+ ax[1].spines['top'].set_visible(False)
+ fig.tight_layout();
+
+def plotting_results(model,model_output, seism_df=pd.DataFrame()):
+
+ inj_t = np.array((model.hydraulic_df.index.values - model.hydraulic_df.index.values[0]).tolist()) / 1.e9
+ Dtau = model.model_input.model_parameters['epoch_duration']
+
+ NN = ((inj_t[-1] - inj_t[0])
+ / Dtau).astype(int) + 1
+ time_fit = np.linspace(
+ inj_t[0], inj_t[-1], NN)
+
+ time_fit_mids = time_fit[1:]#- Dtau / 2
+
+ time_forecast_mids = model.forecast_moments[1:]- Dtau/2
+
+ seism = model.seismicity
+ seism = seism[seism['magnitude'] >= model.Mc]
+ seism.sort_values(by='time', inplace=True)
+ seism_t = np.array((seism['time'].values - model.hydraulic_df.index.values[0]).tolist()) / 1.e9
+
+ # range pred
+
+ y_pred_mean = np.median(model_output[4], axis=1)
+ up = np.percentile(model_output[4],axis=1,q=75)
+ down = np.percentile(model_output[4],axis=1,q=25)
+ down[down < 0] = 0
+
+ if not seism_df.empty:
+ seism_t_all = np.array((seism_df['time'].values - model.hydraulic_df.index.values[0]).tolist()) / 1.e9
+ ind = np.logical_and(seism_t_all > 0, seism_t_all < model.forecast_moments[-1])
+ seism_df_filt = seism_df[ind]
+ seism_t_all = seism_t_all[ind]
+ seism_t_all_aboveMc = seism_t_all[seism_df_filt['magnitude'] >= model.Mc]
+ taubins_all = np.arange(0, model.forecast_moments[-1] + Dtau, Dtau)
+ tmids_all = taubins_all[1:] - Dtau / 2
+ rate_all = np.histogram(seism_t_all_aboveMc, bins=taubins_all)
+
+ ##
+ fig, ax = plt.subplots(1, 2, figsize=(20, 6))
+
+ ax[0].scatter(seism_t, np.arange(1, len(seism_t) + 1), color='darkgrey', label='observations')
+ ax[0].plot(time_fit_mids,np.cumsum(model_output[3]), color='darkgreen', label='model fit')
+ ax[0].set_xlabel('Time increment')
+ ax[0].set_ylabel('Cumulative number of events')
+ ax[0].set_title('Training fit')
+ ax[0].legend()
+ ax[0].spines['right'].set_visible(False)
+ ax[0].spines['top'].set_visible(False)
+
+ # ax[1].plot(model.res_fit_t, model.rate_train, color = 'lightgrey', label = 'observations')
+ ax[1].plot(time_fit_mids, model_output[3], color='darkgreen', label='model fit')
+ ax[1].plot(time_forecast_mids, y_pred_mean, color='darkred', label='model prediction')
+ ax[1].scatter(time_forecast_mids, y_pred_mean, color='darkred')
+ ax[1].fill_between(time_forecast_mids, down, up, facecolor='darkred', alpha=.3)
+ if not seism_df.empty:
+ ax[1].step(tmids_all, rate_all[0], where='mid', label='data', color='darkgrey')
+ # ax[1].fill_between(tmids_all, 0, rate_all[0], color='grey', alpha = .5)
+ #ax[1].set_xlim(0, 50000)
+ #ax[1].set_ylim(0, 150)
+ ax[1].set_xlabel('Time increment')
+ ax[1].set_ylabel('Event rate')
+ ax[1].set_title('Event-rate time series')
+ ax[1].legend(loc='upper left')
+ ax[1].spines['right'].set_visible(False)
+ ax[1].spines['top'].set_visible(False)
+ #fig.tight_layout()
\ No newline at end of file
diff --git a/model_plots.py b/model_plots.py
new file mode 100644
index 0000000..9966a00
--- /dev/null
+++ b/model_plots.py
@@ -0,0 +1,201 @@
+import logging
+import os
+from datetime import datetime
+
+import pandas as pd
+import numpy as np
+from hydws.parser import BoreholeHydraulics
+from seismostats.seismicity.catalog import Catalog
+from seismostats.utils.coordinates import CoordinateTransformer
+from shapely import Polygon
+from shapely.wkt import dumps
+
+from hm1d.ramsis import ModelInput, run_ml1, run_ml1_validated # noqa
+from hm1d.model import HM1D
+from hm1d.utils import plotting_data,plotting_results
+
+
+# Set up logger
+date_strftime_format = "%d-%m-%y %H:%M:%S"
+logging.basicConfig(
+ format='%(asctime)s %(levelname)s %(message)s',
+ datefmt=date_strftime_format,
+ level=logging.INFO)
+
+
+def main():
+ ############################################################
+ # HYDRAULICS
+ dirpath = os.path.dirname(os.path.abspath(__file__))
+ Data_Path = os.path.join(dirpath, "data/")
+ hydraulics = pd.read_hdf(Data_Path + 'TrainingData.h5')
+
+ hydraulics_plan = pd.read_hdf(Data_Path + 'Scenario.h5')
+
+ # define start and end of forecast
+ forecast_start = datetime.utcfromtimestamp(
+ hydraulics_plan.index.values[0].tolist() / 1e9)
+ forecast_end = datetime.utcfromtimestamp(
+ hydraulics_plan.index.values[-1].tolist() / 1e9)
+
+ # rename columns to match hydws
+ hydraulics = hydraulics.drop('time', axis=1).rename(
+ columns={'pressure': 'toppressure'})
+ hydraulics_plan.index.names = ['datetime']
+ hydraulics_plan["topflow"] = -1 * np.ones((len(hydraulics_plan), 1))
+
+ # generate empty parser and load dataframe to empty section
+ parser = BoreholeHydraulics()
+ section_id = parser.section_from_dataframe(hydraulics)
+ hydjson = parser.to_json()
+
+ # replace hydraulics with scenario hydraulics
+ parser[section_id].hydraulics = hydraulics_plan
+ scenario_hydjson = parser.to_json()
+
+ ############################################################
+ # SEISMICITY
+
+ proj_string = 'epsg:2056'
+
+ # define coordinate transformer, local <-> swiss, external <-> WGS84
+ transformer = CoordinateTransformer(proj_string)
+ top_section_swiss = transformer.to_local_coords(
+ 8.47449792444771,
+ 46.5098187019071,
+ 1271.43402303251
+ )
+
+ # CoordinateTransformer with reference point in epsg:2056
+ transformer = CoordinateTransformer(proj_string, *top_section_swiss)
+
+ # Read seismics, transform to local coordinate system
+ catalog_df = pd.read_hdf(Data_Path + 'TrainingTarget.h5')
+ catalog_df['Xs'], catalog_df['Ys'], catalog_df['Zs'] = \
+ transformer.from_local_coords(catalog_df.Xs.values,
+ catalog_df.Ys.values,
+ catalog_df.Zs.values)
+
+ catalog_df = catalog_df.reset_index().rename(
+ columns={
+ 'Xs': 'longitude',
+ 'Ys': 'latitude',
+ 'Zs': 'depth',
+ 'mag': 'magnitude',
+ 'datetime': 'time'})
+
+ catalog = Catalog(catalog_df)
+ catalog['magnitude_type'] = 'mag'
+
+ quakeml = catalog.to_quakeml()
+
+ bounding_polygon = Polygon([[-150, -150], [-150, 150], [150, 150],
+ [150, -150], [-150, -150]])
+ bounding_polygon = transformer.polygon_from_local_coords(bounding_polygon)
+
+ extent = {
+ 'bounding_polygon': dumps(bounding_polygon),
+ 'altitude_min': -150 + top_section_swiss[2],
+ 'altitude_max': 150 + top_section_swiss[2],
+ }
+
+ data = {
+ 'forecast_start': forecast_start,
+ 'forecast_end': forecast_end,
+ 'injection_well': [hydjson],
+ 'injection_plan': scenario_hydjson,
+ 'geometry': extent, # always in WGS84
+ 'seismic_catalog': quakeml,
+ 'model_parameters': {
+ 'seis_model':'SEED',
+ 'seed_model_version': 2,
+ 'SeisSettings': {
+ "seed_density": 10, #SEED & CAPS
+ "inter_sigma1": 22.5, #SEED & CAPS
+ "inter_sigma3": 10, #SEED & CAPS
+ "sigma1_std": 20, #SEED & CAPS
+ "sigma3_std": 10, #SEED & CAPS
+ "cohesion_mean": 2, #SEED & CAPS
+ "friction_mean": 0.6, #SEED & CAPS
+ "criticality": 0, #SEED
+ "stressdrop_coeff": 7, #SEED & CAPS
+ "phydro": 11.6, #SEED & CAPS
+ "b_min": 1.0, #SEED
+ "b_max": 5.0, #SEED
+ "b_min_diff_stress": 50, #SEED
+ "b_max_diff_stress": 0, #SEED
+ "b_vs_depth_range": True, #SEED
+ "CAPS_sigma_distr": 0.05, #CAPS
+ "retriggering": False
+ },
+ 'SeisBounds' : {
+ "sigma1_std": (0.01, 10),
+ "sigma3_std": (0.01, 10),
+ "stressdrop_coeff": (1.e-3, 7),
+ "cohesion_mean": (0.2, 2),
+ "friction_mean": (0.1, 1),
+ "seed_density": (1, 1e5),
+ },
+ 'flow_model': 'line_source',
+ 'FlowSettings': {
+ "permeability": -20,
+ "storage": -10,
+ "viscosity": -3,
+ 'max_grid_ext:':300,
+ 'layer_thick':100,
+ 'cross_area':94
+ },
+ 'FlowBounds' : {
+ "storage": (-11, -9),
+ "permeability": (-20, -13),
+ },
+ 'cost_fun': 'LS',
+ 'NSim':100,
+ 'dM': 0.05,
+ 'Mc': None,
+ 'well_section_publicid': str(section_id),
+ 'reference_point': top_section_swiss, # always in WGS84
+ 'local_proj_string': proj_string,
+ 'epoch_duration': 600,
+ 'n_phases': 8
+ }
+ }
+
+ model_input = ModelInput(**data)
+
+ model = HM1D(model_input)
+
+ plotting_data(model)
+
+ model.fit()
+
+ results = model.predict()
+
+ #results = model.parse_results(results)
+ #catalog_df_full = pd.read_hdf(Data_Path + 'seismic_Data_full_located.h5')
+ catalog_df_full = pd.read_hdf(Data_Path + 'Cat_FORGE22.h5')
+ catalog_df_full.index.names = ['datetime']
+ catalog_df_full = catalog_df_full.reset_index().rename(
+ columns={
+ 'Xs': 'longitude',
+ 'Ys': 'latitude',
+ 'Zs': 'depth',
+ 'mag': 'magnitude',
+ 'datetime': 'time'})
+
+ #catalog_df_full.rename(columns={"X": "Xs", "Y": "Ys", "Depth": "Zs", "MomMag": "magnitude", "Time(UTC)": "time"}, inplace=True)
+ #catalog_df_full.rename(columns={"mag": "magnitude"}, inplace=True)
+ catalog_df_full = catalog_df_full[catalog_df_full["magnitude"] > -5]
+ catalog_df_full.set_index("time", inplace=True)
+ catalog_df_full = catalog_df_full[catalog_df_full.index >= hydraulics.index.values[0]]
+ catalog_df_full = catalog_df_full[catalog_df_full.index <= forecast_end]
+ catalog_df_full = catalog_df_full.reset_index()
+
+ plotting_results(model,results,catalog_df_full)
+
+ return model
+
+
+if __name__ == "__main__":
+ result = main()
+
diff --git a/model_plots_forge.py b/model_plots_forge.py
new file mode 100644
index 0000000..a6c147f
--- /dev/null
+++ b/model_plots_forge.py
@@ -0,0 +1,183 @@
+import logging
+import os
+from datetime import datetime
+
+import pandas as pd
+import numpy as np
+from hydws.parser import BoreholeHydraulics
+from seismostats.seismicity.catalog import Catalog
+from seismostats.utils.coordinates import CoordinateTransformer
+from shapely import Polygon
+from shapely.wkt import dumps
+
+from ml1.ramsis import ModelInput, run_ml1, run_ml1_validated # noqa
+from ml1.model import ML1
+from ml1.utils import plotting_data,plotting_results
+
+
+# Set up logger
+date_strftime_format = "%d-%m-%y %H:%M:%S"
+logging.basicConfig(
+ format='%(asctime)s %(levelname)s %(message)s',
+ datefmt=date_strftime_format,
+ level=logging.INFO)
+
+
+def main():
+ ############################################################
+ # HYDRAULICS
+ dirpath = os.path.dirname(os.path.abspath(__file__))
+ Data_Path = os.path.join(dirpath, "data/")
+ #hydraulics = pd.read_hdf(Data_Path + 'TrainingData.h5')
+ hydraulics = pd.read_hdf(Data_Path + 'Hydr_FORGE22.h5')
+ hydraulics_plan = hydraulics[hydraulics.index.values >= np.array(datetime(2022,4,21,15,0)).astype('datetime64')]
+ hydraulics_plan = hydraulics_plan[hydraulics_plan.index.values < np.array(datetime(2022,4,21,18)).astype('datetime64')]
+
+
+ # define start and end of forecast
+ forecast_start =datetime(2022,4,21,15, 0)#datetime.utcfromtimestamp(hydraulics_plan.index.values[0].tolist() / 1e9)
+ forecast_end = datetime(2022,4,21,18,0)#datetime.utcfromtimestamp(hydraulics_plan.index.values[-1].tolist() / 1e9)
+
+ hydraulics = hydraulics[hydraulics.index.values >= np.array(datetime(2022,4,21,13,00,23)).astype('datetime64')]
+ hydraulics = hydraulics[hydraulics.index.values < np.array(datetime(2022,4,21,15,0)).astype('datetime64')]
+
+ hydraulics_plan["topflow"]=-1*np.ones((len(hydraulics_plan),1))
+ hydraulics_plan.index.values[-1]=np.array(datetime(2022,4,21,18)).astype('datetime64')
+ hydraulics_plan.topflow.values[-1] = hydraulics_plan.topflow.values[-2]
+
+ hydraulics_plan=hydraulics_plan[~hydraulics_plan.index.duplicated(keep='first')]
+
+ #hydraulics_plan = pd.read_hdf(Data_Path + 'Scenario.h5')
+
+
+ observation_start = datetime.utcfromtimestamp(hydraulics.index.values[0].tolist() / 1e9)
+ observation_end = datetime.utcfromtimestamp(hydraulics.index.values[-1].tolist() / 1e9)
+
+ # rename columns to match hydws
+ #hydraulics = hydraulics.drop('time', axis=1).rename(
+ # columns={'pressure': 'toppressure'})
+ hydraulics_plan.index.names = ['datetime']
+
+ # generate empty parser and load dataframe to empty section
+ parser = BoreholeHydraulics()
+ section_id = parser.section_from_dataframe(hydraulics)
+ hydjson = parser.to_json()
+
+ # replace hydraulics with scenario hydraulics
+ parser[section_id].hydraulics = hydraulics_plan
+ scenario_hydjson = parser.to_json()
+
+
+ ############################################################
+ # SEISMICITY
+
+ proj_string = 'epsg:2056'
+
+ # define coordinate transformer, local <-> swiss, external <-> WGS84
+ transformer = CoordinateTransformer(proj_string)
+ top_section_swiss = transformer.to_local_coords(
+ 8.47449792444771,
+ 46.5098187019071,
+ 1271.43402303251
+ )
+
+ # CoordinateTransformer with reference point in epsg:2056
+ transformer = CoordinateTransformer(proj_string, *top_section_swiss)
+
+ # Read seismics, transform to local coordinate system
+ #catalog_df = pd.read_hdf(Data_Path + 'TrainingTarget.h5')
+ catalog_df = pd.read_hdf(Data_Path + 'Cat_FORGE22.h5')
+ catalog_df = catalog_df[catalog_df.index.values < np.array(observation_end).astype('datetime64')]
+ catalog_df = catalog_df[catalog_df.index.values >= np.array(observation_start).astype('datetime64')]
+ catalog_df['Xs'], catalog_df['Ys'], catalog_df['Zs'] = \
+ transformer.from_local_coords(catalog_df.Xs.values,
+ catalog_df.Ys.values,
+ catalog_df.Zs.values)
+
+ catalog_df.index.names = ['datetime']
+ catalog_df = catalog_df.reset_index().rename(
+ columns={
+ 'Xs': 'longitude',
+ 'Ys': 'latitude',
+ 'Zs': 'depth',
+ 'mag': 'magnitude',
+ 'datetime': 'time'})
+
+ catalog = Catalog(catalog_df)
+ catalog['magnitude_type'] = 'mag'
+
+ quakeml = catalog.to_quakeml()
+
+ bounding_polygon = Polygon([[-150, -150], [-150, 150], [150, 150],
+ [150, -150], [-150, -150]])
+ bounding_polygon = transformer.polygon_from_local_coords(bounding_polygon)
+
+ extent = {
+ 'bounding_polygon': dumps(bounding_polygon),
+ 'altitude_min': -150 + top_section_swiss[2],
+ 'altitude_max': 150 + top_section_swiss[2],
+ }
+
+ data = {
+ 'forecast_start': forecast_start,
+ 'forecast_end': forecast_end,
+ 'injection_well': [hydjson],
+ 'injection_plan': scenario_hydjson,
+ 'geometry': extent, # always in WGS84
+ 'seismic_catalog': quakeml,
+ 'model_parameters': {
+ 'M_decimals': 2,
+ 'dM': 0.05,
+ 'Mc': None,
+ 'LASSO_m': 100,
+ 'LASSO_penalty': 1,
+ 'LASSO_Nmin_fit': 10,
+ 'Nsim': 150,
+ 'Delta_tau': 600,
+ 'Pmodel': 'FlowModels0-1',
+ 'well_section_publicid': str(section_id),
+ 'reference_point': top_section_swiss, # always in WGS84
+ 'local_proj_string': proj_string,
+ 'epoch_duration': 600,
+ 'n_phases': 8
+ }
+ }
+
+ model_input = ModelInput(**data)
+
+ model = ML1(model_input)
+
+ plotting_data(model)
+
+ model.fit()
+
+ results = model.predict()
+
+ #results = model.parse_results(results)
+ #catalog_df_full = pd.read_hdf(Data_Path + 'seismic_Data_full_located.h5')
+ catalog_df_full = pd.read_hdf(Data_Path + 'Cat_FORGE22.h5')
+ catalog_df_full.index.names = ['datetime']
+ catalog_df_full = catalog_df_full.reset_index().rename(
+ columns={
+ 'Xs': 'longitude',
+ 'Ys': 'latitude',
+ 'Zs': 'depth',
+ 'mag': 'magnitude',
+ 'datetime': 'time'})
+
+ #catalog_df_full.rename(columns={"X": "Xs", "Y": "Ys", "Depth": "Zs", "MomMag": "magnitude", "Time(UTC)": "time"}, inplace=True)
+ #catalog_df_full.rename(columns={"mag": "magnitude"}, inplace=True)
+ catalog_df_full = catalog_df_full[catalog_df_full["magnitude"] > -5]
+ catalog_df_full.set_index("time", inplace=True)
+ catalog_df_full = catalog_df_full[catalog_df_full.index >= hydraulics.index.values[0]]
+ catalog_df_full = catalog_df_full[catalog_df_full.index <= forecast_end]
+ catalog_df_full = catalog_df_full.reset_index()
+
+ plotting_results(model,results,catalog_df_full)
+
+ return model
+
+
+if __name__ == "__main__":
+ result = main()
+
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..7fd26b9
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
\ No newline at end of file
diff --git a/run_model.py b/run_model.py
new file mode 100644
index 0000000..ab5f4f3
--- /dev/null
+++ b/run_model.py
@@ -0,0 +1,163 @@
+import logging
+import os
+from datetime import datetime
+
+import pandas as pd
+from hydws.parser import BoreholeHydraulics
+from seismostats.seismicity.catalog import Catalog
+from seismostats.utils.coordinates import CoordinateTransformer
+from shapely import Polygon
+from shapely.wkt import dumps
+
+from hm1d.ramsis import run_hm1d, run_hm1d_validated # noqa
+
+# Set up logger
+date_strftime_format = "%d-%m-%y %H:%M:%S"
+logging.basicConfig(
+ format='%(asctime)s %(levelname)s %(message)s',
+ datefmt=date_strftime_format,
+ level=logging.INFO)
+
+
+def main():
+ ############################################################
+ # HYDRAULICS
+ dirpath = os.path.dirname(os.path.abspath(__file__))
+ Data_Path = os.path.join(dirpath, "data/")
+ hydraulics = pd.read_hdf(Data_Path + 'TrainingData.h5')
+
+ hydraulics_plan = pd.read_hdf(Data_Path + 'Scenario.h5')
+
+ # define start and end of forecast
+ forecast_start = datetime.utcfromtimestamp(
+ hydraulics_plan.index.values[0].tolist() / 1e9)
+ forecast_end = datetime.utcfromtimestamp(
+ hydraulics_plan.index.values[-1].tolist() / 1e9)
+
+ # rename columns to match hydws
+ hydraulics = hydraulics.drop('time', axis=1).rename(
+ columns={'pressure': 'toppressure'})
+ hydraulics_plan.index.names = ['datetime']
+
+ # generate empty parser and load dataframe to empty section
+ parser = BoreholeHydraulics()
+ section_id = parser.section_from_dataframe(hydraulics)
+ hydjson = parser.to_json()
+
+ # replace hydraulics with scenario hydraulics
+ parser[section_id].hydraulics = hydraulics_plan
+ scenario_hydjson = parser.to_json()
+
+ ############################################################
+ # SEISMICITY
+
+ proj_string = 'epsg:2056'
+
+ # define coordinate transformer, local <-> swiss, external <-> WGS84
+ transformer = CoordinateTransformer(proj_string)
+ top_section_swiss = transformer.to_local_coords(
+ 8.47449792444771,
+ 46.5098187019071,
+ 1271.43402303251
+ )
+
+ # CoordinateTransformer with reference point in epsg:2056
+ transformer = CoordinateTransformer(proj_string, *top_section_swiss)
+
+ # Read seismics, transform to local coordinate system
+ catalog_df = pd.read_hdf(Data_Path + 'TrainingTarget.h5')
+ catalog_df['Xs'], catalog_df['Ys'], catalog_df['Zs'] = \
+ transformer.from_local_coords(catalog_df.Xs.values,
+ catalog_df.Ys.values,
+ catalog_df.Zs.values)
+
+ catalog_df = catalog_df.reset_index().rename(
+ columns={
+ 'Xs': 'longitude',
+ 'Ys': 'latitude',
+ 'Zs': 'depth',
+ 'mag': 'magnitude',
+ 'datetime': 'time'})
+
+ catalog = Catalog(catalog_df)
+ catalog['magnitude_type'] = 'Mw'
+
+ quakeml = catalog.to_quakeml()
+
+ bounding_polygon = Polygon([[-150, -150], [-150, 150], [150, 150],
+ [150, -150], [-150, -150]])
+ bounding_polygon = transformer.polygon_from_local_coords(bounding_polygon)
+
+ extent = {
+ 'bounding_polygon': dumps(bounding_polygon),
+ 'altitude_min': -150 + top_section_swiss[2],
+ 'altitude_max': 150 + top_section_swiss[2],
+ }
+
+ data = {
+ 'forecast_start': forecast_start,
+ 'forecast_end': forecast_end,
+ 'injection_well': [hydjson],
+ 'injection_plan': scenario_hydjson,
+ 'geometry': extent, # always in WGS84
+ 'seismic_catalog': quakeml,
+ 'model_parameters': {
+ 'M_decimals': 2,
+ 'dM': 0.05,
+ 'Mc': None,
+ 'LASSO_m': 10,
+ 'LASSO_penalty': 1,
+ 'LASSO_Nmin_fit': 5,
+ 'Nsim': 150,
+ 'Delta_tau': 600,
+ 'Pmodel': 'FlowModels0-1',
+ 'well_section_publicid': str(section_id),
+ 'reference_point': top_section_swiss, # always in WGS84
+ 'local_proj_string': proj_string,
+ 'epoch_duration': 600,
+ 'n_phases': 8
+ }
+ }
+
+ # # # initialize the model
+
+ #model_output = run_ml1(data) # unvalidated input
+ model_output = run_ml1_validated(data) # validated input
+
+ ############################################################
+ # results are a list of ForecastGRRateGrids, they can for example
+ # be accessed like this:
+ print('\n\n'
+ f'100 Simulations for timestep from {model_output[-1].starttime} '
+ f'until {model_output[-1].endtime}\n',
+ model_output[-1])
+
+ # it is easy to add the datetime index to the dataframe:
+ print('\n\n'
+ 'Same dataframe with the datetime index added:\n',
+ model_output[-1].add_time_index())
+
+ # as well as concatenating all the simulations into one dataframe:
+ full_df = pd.concat([x.add_time_index() for x in model_output])
+ print('\n\n'
+ 'All simulations concatenated into one dataframe:\n',
+ full_df)
+
+ ############################################################
+ # you have also available here the:
+
+ # input hydraulics (history) as a DataFrame
+ print(hydraulics)
+
+ # input injection plan as a DataFrame
+ print(hydraulics_plan)
+
+ # input seismicity as a seismostats Catalog
+ print(catalog)
+
+ return model_output
+
+
+if __name__ == "__main__":
+ result = main()
+
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..5aece8a
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,57 @@
+[metadata]
+name = ml1
+author =
+ Victor Clasen Repolles
+ Rinaldi Antonio
+ Dimitrios Karvounis
+ Federico Ciardo
+author_email =
+ victor.clasen@sed.ethz.ch
+ antoniopio.rinaldi@sed.ethz.ch
+version = 0.1.0
+description =
+ !D Hydromechanical models for Induced Seismicity Forecasts
+long_description = file: README.md, LICENSE
+license = AGPL License
+classifiers =
+ Programming Language :: Python :: 3
+
+[options]
+packages = find:
+install_requires =
+ h5py
+ hydws-client @ git+https://git@gitlab.seismo.ethz.ch/indu/hydws-client.git
+ nbformat
+ numpy
+ scipy
+ pandas
+ pyarrow
+ pydantic
+ ramsis-model[ramsis-final] @ git+https://gitlab.seismo.ethz.ch/indu/ramsis-model.git
+ scikit-learn
+ seismostats @ git+https://github.com/swiss-seismological-service/SeismoStats.git
+ tables
+ numba
+
+python_requires = >= 3.10
+
+[options.extras_require]
+dev =
+ pytest
+ pytest-cov
+ autopep8
+ flake8
+
+[flake8]
+select = E,F,W
+max_line_length = 79
+ignore = E402, W503
+exclude =
+ .git,
+ .tox,
+ .eggs,
+ .config,
+ *.egg-info,
+ __pycache__,
+ .pytest_cache,
+ env,
diff --git a/tox.ini b/tox.ini
new file mode 100644
index 0000000..07b4426
--- /dev/null
+++ b/tox.ini
@@ -0,0 +1,17 @@
+[tox]
+envlist = py310-tests,flake8
+minversion = 4.0
+
+[testenv]
+usedevelop = true
+download = true
+extras =
+ tests
+ py310: py310
+deps =
+ cython
+
+[testenv:flake8]
+skip_install = true
+deps = flake8
+commands = flake8 .
--
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