This repository contains codes and resources to reproduce experiments of StorSeismic in Harsuko and Alkhalifah, 2020.
We use RAdam as the default optimizer. To install this, use:
pip install git+https://github.com/LiyuanLucasLiu/RAdam
| No | Notebook name | Description |
|---|---|---|
| 1 | nb0_1_data_prep_pretrain.ipynb | Create pre-training data |
| 2 | nb0_2_data_prep_finetune.ipynb | Create fine-tuning data |
| 3 | nb1_pretraining.ipynb | Pre-training of StorSeismic |
| 4 | nb2_1_finetuning_denoising.ipynb | Example of fine-tuning task: denoising |
| 5 | nb2_2_finetuning_velpred.ipynb | Example of fine-tuning task: velocity estimation |
Harsuko, R., & Alkhalifah, T. A. (2022). StorSeismic: A new paradigm in deep learning for seismic processing. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15.
Citations are very welcomed. This work can be cited using:
@article{harsuko2022storseismic,
title={StorSeismic: A new paradigm in deep learning for seismic processing},
author={Harsuko, Randy and Alkhalifah, Tariq A},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={60},
pages={1--15},
year={2022},
publisher={IEEE}
}