deepprecs is a Python library aimed at providing the fundamental building blocks to solve Inverse Problems with
the aid of Deep Preconditioners: this library is implemented on top of Pytorch-Lightning (for network training) and
PyLops (for physical operators).
For more details refer to the accompanying paper: Deep Preconditioners and their application to seismic wavefield processing - Ravasi M. submitted to Frontiers in Earth Science.
This repository is organized as follows:
- 📂 deepprecs: python library containing routines for training and application of deep preconditioners to inverse problems;
- 📂 data: folder containing instructions on how to retrieve the data used in the examples;
- 📂 notebooks: set of jupyter notebooks applying deep preconditioners to a number of problems of increasing complexity;
- 📂 scripts: set of python scripts used to run seismic interpolation experiments in batch mode;
- 📂 config: set of configuration files to run scripts.
The following notebooks are provided:
- 📙
sinusoids/Sinusoid_reconstruction_pl.ipynb: notebook performing 1D interpolation with deep precondtioners; - 📙
syncline/Syncline_deghosting_pl.ipynb: notebook performing deghosting of a Syncline shot gather with deep preconditioners (including training); - 📙
syncline/Syncline_deghosting_inferenceonly.ipynb: notebook performing deghosting of a Syncline shot gather with deep precondtioners (only inference with pre-trained model); - 📙
marmousi/Marmousi_deghosting_pl.ipynb: notebook performing deghosting of a Marmousi shot gather with deep preconditioners (including training);
To ensure reproducibility of the results, we suggest using the environment.yml file when creating an environment.
Simply run:
./install_env.sh
It will take some time, if at the end you see the word Done! on your terminal you are ready to go. After that you can simply install your package:
pip install .
or in developer mode:
pip install -e .
Remember to always activate the environment by typing:
conda activate deepprecs
Disclaimer: All experiments have been carried on a Intel(R) Xeon(R) CPU @ 2.10GHz equipped with a single NVIDIA GEForce RTX 3090 GPU. Different environment configurations may be required for different combinations of workstation and GPU.
If you find this library useful for your work, please cite the following paper (ArXiv)
@article{ravasi2022,
title={Deep Preconditioners and their application to seismic wavefield processing},
author={Ravasi, Matteo},
journal={arXiv preprint arXiv:2207.09938},
year={2022}
}