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Plug and Play Post-Stack Seismic Inversion with CNN-based Denoisers

Seismic_PnP 최신버젼 다운로드

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![LOGO](https://github.com/DIG-Kaust/Seismic_PnP/blob/main/PnP_logo.png)

Reproducible material for **Plug and Play Post-Stack Seismic Inversion with CNN-based Denoisers \
Romero J., Corrales M., Luiken N., and Ravasi M.** \
submitted to Second EAGE Subsurface Intelligence Workshop, 28-31 October 2022, Manama, Bahrain

## Project structure 
This repository is organized as follows:

- :open_file_folder: **data**: Marmousi impedance synthetic model.
- :open_file_folder: **models**: folder containing pre-trained models (DnCNN and DRUnet).
- :open_file_folder: **notebooks**: jupyter notebook reproducing the experiments in the paper.
- :open_file_folder: **pnpseismic**: package of the project (Deep denoisers architectures and PnP framework).

## Notebooks 
The following notebooks are provided:

- :orange_book: ``PnP_PD_Post-Stack_Seismic_Inversion_marmousi.ipynb``: notebook performing the comparison between model-based regularization and Plug and Play.

## Getting started :space_invader: :robot:
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 pnpseismic
```
**Disclaimer:** For computer time, this research used the resources of the Supercomputing Laboratory at KAUST in Thuwal, Saudi Arabia. All experiments have been carried on a Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz equipped with a single NVIDIA TESLA V100. Different environment 
configurations may be required for different combinations of workstation and GPU.

## Pre-trained Models
For more details of the Pre-trained Deep Denoisers used in this study, please refer to the following repositories: https://github.com/cszn/DnCNN and https://github.com/cszn/DPIR.

## Cite us

```
@article{eage:/content/papers/10.3997/2214-4609.2022616015,
   author = "Romero, J. and Corrales, M. and Luiken, N. and Ravasi, M.",
   title = "Plug and Play Post-Stack Seismic Inversion with CNN-Based Denoisers", 
   journal= "",
   year = "2022",
   volume = "2022",
   number = "1",
   pages = "1-5",
   doi = "https://doi.org/10.3997/2214-4609.2022616015",
   url = "https://www.earthdoc.org/content/papers/10.3997/2214-4609.2022616015",
   publisher = "European Association of Geoscientists &amp; Engineers",
   issn = "2214-4609",
   type = "",
  }
```

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