StorSeismic: An approach to pre-train a neural network to store seismic data features

Last update: Dec 05, 2022

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Overview

StorSeismic: An approach to pre-train a neural network to store seismic data features

This repository contains codes and resources to reproduce experiments of StorSeismic in Harsuko and Alkhalifah, 2020.

Instruction

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

References

Harsuko, R., & Alkhalifah, T. (2022). StorSeismic: A new paradigm in deep learning for seismic processing. ArXiv, abs/2205.00222.

Citation

Citations are very welcomed. This work can be cited using:

@article{Harsuko2022StorSeismicAN,
  title={StorSeismic: A new paradigm in deep learning for seismic processing},
  author={Randy Harsuko and Tariq Alkhalifah},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.00222}
}

StorSeismic: An approach to pre-train a neural network to store seismic data features

Related tags

Overview

StorSeismic: An approach to pre-train a neural network to store seismic data features

Instruction

References

Citation

Owner

Seismic Wave Analysis Group

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