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Fully automatic random noise attenuation using empirical wavelet transform

WEI CHEN1,2 ,  HUI SONG1,2 ,  XIAOYU CHUAI3
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1 Key Laboratory of Exploration Technology for Oil and Gas Resources of the Ministry of Education, Yangtze University, Daxue Road 111, Caidian District, Wuhan 430100, P.R. China.,
2 Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Daxue Road 111, Caidian District, Wuhan 430100, P.R. China.,
3 Hebei Coal Research Institute, Xingtai 054000, P.R. China.,
JSE 2019, 28(2), 147–162;
Submitted: 9 June 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published: 9 June 2025
© 2025 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Chen, W., Song, H. and Chuai, X.Y., 2019. Fully automatic random noise attenuation using empirical wavelet transform. Journal of Seismic Exploration, 28: 147-162. Strong noise in seismic data seriously affects many steps in seismic data processing and imaging. While most traditional methods depend on carefully tuned input parameters by human, we are proposing an automatic noise attenuation algorithm to facilitate a fast preprocessing of massive prestack seismic data. In the proposed algorithm, the non-stationary seismic data is first adaptively decomposed into empirical components via empirical wavelet transform (EWT) according to the frequency contents in the data. Then, the first component is selected to represent the useful signals. This process can be implemented in a fully automatic way. We compare the decompositions from EWT and the empirical mode decomposition (EMD) and find that the EWT has a stronger capability in separating the useful signals and the random noise. We also test the proposed algorithm in both multi-channel synthetic and field data examples. The results demonstrate that the new adaptive method can obtain better denoising performance than the state-of-the-art methods.

Keywords
random noise suppression
empirical wavelet transform
seismic signal processing
automatic processing
intrinsic mode function
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