Unsupervised Seismic Random Noise Suppression Based on Local Similarity and Replacement Strategy

Unsupervised Seismic Random Noise Suppression Based on Local Similarity and Replacement Strategy

Improving the signal-to-noise ratio and suppressing random noise in seismic data is critical for high-precision processing. Although deep learning-based algorithms have gained popularity as denoising methods, they suffer from poor generalization ability, resulting in high training set construction c...

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Bibliographic Details
Main Authors: Jian Gao, Zhenchun Li, Min Zhang, Yixuan Gao, Wanyue Gao
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Seismic data
random noise
convolutional neural network
unsupervised learning
Online Access:https://ieeexplore.ieee.org/document/10115436/

Internet

https://ieeexplore.ieee.org/document/10115436/

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