slimgroup / software.siahkoohi2019trnna

Neural network augmented wave-equation simulation by Siahkoohi, A., Louboutin, M., and Herrmann, F.J.

Neural network augmented wave-equation simulation

Codes for generating results in Siahkoohi, A., Louboutin, M. and Herrmann, F.J., 2019. Neural network augmented wave-equation simulation. arXiv preprint arXiv:1910.00925.

Prerequisites

This code has been tested on Deep Learning AMI (Amazon Linux) Version 11.0 on Amazon Web Services (AWS). We used g3.4xlarge instance. Also, we use GCC compiler version 7.3.0.

This software is based on Devito-3.2.0, ODL-0.7.0, and TensorFlow-1.10.0. Follow the steps below to install the necessary libraries:

cd $HOME
git clone https://github.com/alisiahkoohi/NN-augmented-wave-sim
git clone --branch v0.7.0 https://github.com/odlgroup/odl.git
git clone --branch v3.2.0 https://github.com/devitocodes/devito.git

cd $HOME/devito
conda env create -f environment.yml
source activate devito
pip install  -e .
export DEVITO_ARCH=gnu
export OMP_NUM_THREADS=16
export DEVITO_OPENMP=1

cd $HOME/odl
pip install --user -e .

pip install tensorflow-gpu==1.10.0
pip install h5py

Dataset

The Marmousi model we use is obtained from Devito Codes project and will be automatically downloaded and placed at ./vel-model/ directory upon running RunTraining.sh. See below for more details.

Script descriptions

RunTraining.sh: script for running training on AWS. It will make model/ and data/ directory in $HOME for storing training/testing data and saved neural net checkpoints and final results, respectively. Next, it will train a neural net for the experiment.

RunTraining_shared_weights.sh: script for running training on AWS for the case where networks share their weights. It will make model/ and data/ directory in $HOME for storing training/testing data and saved neural net checkpoints and final results, respectively. Next, it will train a neural net for the experiment.

RunTesting.sh: script for testing the trained neural net on AWS.

RunTesting.sh: script for testing the trained neural net on AWS for the case where networks share their weights.

src/main.py: constructs LearnedWaveSim class using given arguments in RunTraining.sh, defined in model.py and calls train function in the defined LearnedWaveSim class.

src/model.py: includes LearnedWaveSim class definition, which involves train and test functions.

src/main_shared_weights.py: constructs LearnedWaveSim class using given arguments in RunTraining.sh, defined in model.py and calls train function in the defined LearnedWaveSim class, for the case where networks share their weights.

src/model_shared_weights.py: includes LearnedWaveSim class definition, which involves train and test functions, for the case where networks share their weights.

show_prediction.py: Plotting the results.

Running the code

To perform training on AWS, run:

# Running in GPU

bash RunTraining.sh

To evaluated the pre-trained and transfer-trained neural net on test dataset run the following. It will automatically load the latest checkpoint saved for both neural nets.

# Running in GPU

bash RunTesting.sh

To generate and save figures shown in paper for gradient conditioning, run:

bash src/genFigures.sh

The saving directory can be changed by modifying savePath variable in src/genFigures.sh.

Citation

If you find this software useful in your research, please cite:

@unpublished {siahkoohi2019TRnna,
  title = {Neural network augmented wave-equation simulation},
  booktitle = {Tech. rep. TR-CSE-2019-1, Georgia Institute of Technology},
  year = {2019},
  month = {09},
  keywords = {deep learning, finite difference, wave equation},
  url = {https://arxiv.org/pdf/1910.00925.pdf},
  author = {Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}

Questions

Please contact [email protected] for further questions.

Acknowledgments

The authors thank Xiaowei Hu for his open-access repository on GitHub. Our software implementation built on this work.

Author

Ali Siahkoohi