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}}