Repository for the paper "Adapting the residual dense network for seismic data denoising and upscaling"

Usage

This is a supervised learning approach, consisting of 3 steps:

Step 1: Generating training data

You can generate training data in any way you prefer. We utilized the Python package Devito to generate the training data.

Step 2: Training

train.py is a framework for training process. Before using it, you need to make some modifications. First, you need to load your own training data in the train.py. Additionally, you can also adjust some hyperparameters.

Step 3: Testing

test.py is a framework for testing process. You need to load your own testing data in the test.py.

Description

• Random_noise.py/Coherent_noise.py/Missing_trace.py/Super_resolution.py files are the neural network modules for each task.

• The parameter.py file contains data address and hyperparameters.

Results:

Corrupted data	Ground truth	Curvelets

Ours-step1-denoise	Ours-step2-interpolation	Ours-step3-super resolution

Citation

BibTex

@article{wang2022adapting,
    title={Adapting the residual dense network for seismic data denoising and upscaling},
    author={Wang, Rongqian and Zhang, Ruixuan and Bao, Chenglong and Qiu, Lingyun and Yang, Dinghui},
    journal={Geophysics},
    volume={87},
    number={4},
    pages={V321--V340},
    year={2022},
    publisher={Society of Exploration Geophysicists}
}