Werthmüller, D., R. Rochlitz, O. Castillo-Reyes, and L. Heagy, 2021, Towards an open-source landscape for 3-D CSEM modelling: Geophysical Journal International, 227(1), 644--659; DOI: 10.1093/gji/ggab238.
This repository contains the LaTeX source of the manuscript as well as the necessary codes to reproduce all the results and figures.
Repository structure
├── environment.yml # Python environment file
├── LICENSE # CC-BY-SA-4.0 License
├── README.md # this file
├── manuscript/ # LaTeX-files
│ └── figures/ # figures used in manuscript
├── model-{MODEL}/ # for MODEL in {block, marlim}
│ ├── README.md # info about MODEL
│ ├── {CODE}/ # for CODE in {custEM, emg3d, PETGEM, SimPEG}
│ └── results/ # results of the different codes
└── presentation/ # talk given at the AGU 2020
Each model-{MODEL}/-directory contains a README.md with more information
about the particular model.
- 2020-10-23: Submitted to Geophysical Journal International.
- 2021-02-15: Revision I submitted.
- 2021-04-15: Revision II submitted.
- 2021-06-08: Revision III submitted.
- 2021-06-16: Accepted.
The information in this repo is enough to reproduce all results and figures shown in the manuscript. Our results are available at 10.5281/zenodo.4535602.
The environment.yml file generates a
conda-environment
(see Anaconda Python Distribution)
with all the required dependencies. To create the environment simply run
conda env create -f environment.ymlThis will create a new conda environment called csem.
To activate the environment run
conda activate csemand to deactivate it run
conda deactivateTo use this environment in the Jupyter notebook, you have to register it first:
python -m ipykernel install --user --name csemThen, in the Jupyter notebook (or Jupyter lab), you can select it by going to
Kernel->Change kernel and select csem.
To completely remove the environment run
conda remove --name csem --allYou need at least Python 3.7 to run (at least some of) the codes.
Dependencies are a difficult topic, and no-one can guarantee that the scripts will still work a few years down to road with many new releases of all dependencies. We list here therefore the most important dependencies that were used when creating the results.
Note that all notebooks (data creation, results, and computation of emg3d and
SimPEG) have a scooby-report at the end, listing the most important
dependencies and their version.
The following is the emg3d-scooby report. You can get your own either in Python
via emg3d.Report() or in the terminal via emg3d --report.
--------------------------------------------------------------------------------
Date: Thu Jun 17 14:50:50 2021 CEST
OS : Linux
CPU(s) : 4
Machine : x86_64
Architecture : 64bit
RAM : 15.5 GB
Environment : Python
Python 3.8.0 | packaged by conda-forge | (default, Nov 22 2019, 19:11:38)
[GCC 7.3.0]
numpy : 1.19.5
scipy : 1.6.0
numba : 0.52.0
emg3d : 0.16.0
empymod : 2.0.4
xarray : 0.16.2
discretize : 0.6.2
h5py : 3.1.0
matplotlib : 3.3.3
IPython : 7.19.0
Intel(R) Math Kernel Library Version 2020.0.4 Product Build 20200917 for
Intel(R) 64 architecture applications
--------------------------------------------------------------------------------
custEM 1.0.0
fenics 2019.1
pygimli 1.1.0
tetgen 1.51
petgem 0.8
petsc 3.7
petsc4py 3.7
python 3.6.1
numpy 1.18.3
scipy 1.5.2
gmsh 4.5.4
The following an excerpt from the SimPEG-scooby report. You can get your own
either in Python via SimPEG.Report() or in the terminal via python -c 'import SimPEG; print(SimPEG.Report())'.
OS : Linux
CPU(s) : 8
Machine : x86_64
Architecture : 64bit
RAM : 413.4 GB
Environment : Python
Python 3.7.9 | packaged by conda-forge | (default, Dec 9 2020, 21:08:20)
[GCC 9.3.0]
SimPEG : 0.14.3
discretize : 0.6.2
pymatsolver : 0.1.2
vectormath : 0.2.2
properties : 0.6.1
numpy : 1.19.2
scipy : 1.2.1
matplotlib : 3.3.4