https://github.com/lukasmosser/stochastic_seismic_waveform_inversion
Official Implementation of "Stochastic seismic waveform inversion using generative adversarial networks as a geological prior"
https://github.com/lukasmosser/stochastic_seismic_waveform_inversion
bayesian-inference generative-adversarial-network geophysics machine-learning
Last synced: 2 months ago
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Official Implementation of "Stochastic seismic waveform inversion using generative adversarial networks as a geological prior"
- Host: GitHub
- URL: https://github.com/lukasmosser/stochastic_seismic_waveform_inversion
- Owner: LukasMosser
- License: mit
- Created: 2019-05-28T13:45:58.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-09T16:54:41.000Z (over 5 years ago)
- Last Synced: 2025-03-29T00:23:10.574Z (3 months ago)
- Topics: bayesian-inference, generative-adversarial-network, geophysics, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 33.4 MB
- Stars: 49
- Watchers: 4
- Forks: 16
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
Authors: [Lukas Mosser](https://twitter.com/porestar), [Olivier Dubrule](https://www.imperial.ac.uk/people/o.dubrule), [Martin J. Blunt](https://www.imperial.ac.uk/people/m.blunt)
[Pytorch](https://pytorch.org) implementation of [Stochastic seismic waveform inversion using generative adversarial networks as a geological prior](https://arxiv.org/abs/1806.03720)
## Model Architecture
The model architecture consists of two parts:
- the generative adversarial network (implemented in [Pytorch](https://pytorch.org))
- the acoustic wave equation forward solver implemented in ([Devito](https://www.opesci.org/devito)).
The coupling between the two defines a fully differentiable computational graph.
## Movie representation of samples from the prior
## Movie representation of samples from the posterior (27 sources)
## Usage
To perform the inversion using the available pre-trained generator network use ``` apps/main_paper_version_revised.py ```
(Sorry for the long name, but older versions of the code were kept for reference purposes)
## Trained Models
Pre-trained models are available in the [checkpoints](checkpoints/) directory.
## Results and Data
The resulting datasets are available in this [Google Drive](https://drive.google.com/drive/folders/1xLkLwDxAGVmfz-o2DzImgr8fP0fQNHW4?usp=sharing)
Each run was made reproducible by setting the run-number = seed command-line argument.
Computations were performed on Imperial College CX1 supercomputing facilities.
Total duration: 12 hours wall-time on 32-core nodes ~ 50 nodes simultaneously.
## Figures from paper
The figures from the paper can be reproduced using ```notebooks/Paper_Figures.ipynb```.
All figures are located in ```results/figures```
## Devito Optimizations
The library used to represent the forward solver has a number of optimizations that allow it to parallelize across
cores and nodes using MPI.
We suggest the following environment variables be set to maximize for performance:
```
DEVITO_OPENMP="1";
DEVITO_DLE="advanced"
DEVITO_LOGGING="INFO"
DEVITO_ARCH="gcc"
```
An example bash script used to perform the numerical computations on Imperial's CX1 cluster can be found in ```scripts/cluster_run.sh```
## Citing
```
@article{mosser2018stochastic,
title={Stochastic seismic waveform inversion using generative adversarial networks as a geological prior},
author={Mosser, Lukas and Dubrule, Olivier and Blunt, Martin J},
journal={arXiv preprint arXiv:1806.03720},
year={2018}
}
```
## Acknowledgements
The author would like to acknolwedge the developers of the [Devito](https://www.opesci.org/devito/).
If you use their software, please acknowledge them in your references.
O. Dubrule would like to thank Total for seconding him as a visiting professor at Imperial College London.
## License
[MIT](LICENSE)