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).

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.