Reproducible material for PINNslope: seismic data interpolation and local slope estimation with physics informed neural networks - Brandolin F., Ravasi M., Alkhalifah T.

Project structure

This repository is organized as follows:

• 📂 pinnslope: python library containing routines for "PINNslope" seismic data interpolation and local slope estimation with physics informed neural networks;
• 📂 data: folder containing input data and results;
• 📂 notebooks: set of jupyter notebooks reproducing the experiments in the paper (see below for more details);
• 📂 asset: folder containing logo;

Notebooks

The following notebooks are provided:

• 📙 PINNslopePE.ipynb : notebook performing field seismic data interpolation and local slope estimation.
• 📙 PINNslope_synth.ipynb : notebook performing synthetic seismic data interpolation and local slope estimation
• 📙 LS_PWreg_Inversion.ipynb : notebook performing plane-wave regularized least-squares interpolation.
• 📙 plottingREALD.ipynb : notebook reproducing the figures in the paper (of the field data numerical examples).
• 📙 plottingSYNTH.ipynb : notebook reproducing the figures in the paper (of the synth data numerical examples).
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Getting started

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. Activate the environment by typing:

conda activate envpinnslope

After that you can simply install your package:

pip install .

or in developer mode:

pip install -e .

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.

Cite Us

Brandolin, F., Ravasi, M., & Alkhalifah, T. (2024). Pinnslope: Seismic data interpolation and local slope estimation with physics informed neural networks. GEOPHYSICS , 89 (4), V331-V345. DOI: 10.1190/geo2023-0323.1

Please use the following BibTeX entry to cite this work:

@article{doi:10.1190/geo2023-0323.1,
author = {Francesco Brandolin and Matteo Ravasi and Tariq Alkhalifah},
title = {PINNslope: Seismic data interpolation and local slope estimation with physics informed neural networks},
journal = {GEOPHYSICS},
volume = {89},
number = {4},
pages = {V331-V345},
year = {2024},
doi = {10.1190/geo2023-0323.1},
URL = { https://doi.org/10.1190/geo2023-0323.1},
eprint = {https://doi.org/10.1190/geo2023-0323.1},

}