seismicsuperresolution's Introduction

SeismicSuperResolution

This is a repository for the paper "Deep Learning for Simultaneous Seismic Image Super-Resolution and Denoising" (IEEE Transactions on Geoscience and Remote Sensing).

The frame and some code are from sanghyun-son/EDSR-PyTorch. src/loss/msssim.py was modified based on jorge-pessoa/pytorch-msssim.

Usage

SeismicSuperResolution/
      ├───── data/
      │        ├───── sx/     # high resolution data
      │        ├───── nx2/    # low resolution data
      │        └───── field/  # field data
      │                 ├───── kumano2_608x400.dat
      │                 ├───── lulia_592x400.dat
      │                 ├───── tp_352x240.dat
      │                 └───── ...
      ├───── experiment/
      │        ├───── alpha6/
      │        │        ├───── model/
      │        │        │         ├───── model_best.pt # in google drive
      │        │        │         └───── ...
      │        │        └───── ...
      │        └───── ...
      │ 
      └───── src/        
               └───── ...

Dataset

All the data used in this paper is avaliable in google drive https://drive.google.com/drive/folders/1DuMdclOdeXDgGBOhsHSlEdTB_LvhIH-X?usp=sharing. And the model experiment/alpha6/model/model_best.pt can also be obtained by above google drive link.

Code

All code is in the directory src.

Dependencies

python 3.6.9
pytorch 1.6.0
numpy 1.17.4
cudatoolkit 10.1.243
matplotlib 3.1.1

Citation

If you find this work useful in your research, please consider citing:

Plain Text

J. Li, X. Wu and Z. Hu, "Deep Learning for Simultaneous Seismic Image Super-Resolution and Denoising," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-11, 2022, Art no. 5901611, doi: 10.1109/TGRS.2021.3057857.

BibTex

@article{deep2022li,
   author={Li, Jintao and Wu, Xinming and Hu, Zhanxuan},
   journal={IEEE Transactions on Geoscience and Remote Sensing}, 
   title={Deep Learning for Simultaneous Seismic Image Super-Resolution and Denoising}, 
   year={2022},
   volume={60},
   number={5901611},
   pages={1-11},
   doi={10.1109/TGRS.2021.3057857}}

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