Residual Learning of Cycle-GAN for Seismic Data Denoising

Residual Learning of Cycle-GAN for Seismic Data Denoising

Random noise attenuation has always been an indispensable step in the seismic exploration workflow. The quality of the results directly affects the results of subsequent inversion and migration imaging. This paper proposes a cycle-GAN denoising framework based on the data augmentation strategy. We i...

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Bibliographic Details
Main Authors: Wenda Li, Jian Wang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Geophysical data
geophysics computing
generative adversarial networks
noise reduction
residual learning
Online Access:https://ieeexplore.ieee.org/document/9316158/

Internet

https://ieeexplore.ieee.org/document/9316158/

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