Random noise attenuation of common offset gathers by f-x low-rank matrix approximation with nonconvex regularization

SHI Zhan-Zhan; PANG Su; WANG Yuan-Jun; CHI Yue-Long; ZHOU Qiang

doi:10.11720/wtyht.2022.1533

2022 Vol. 46, No. 6

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Article navigation > Geophysical and Geochemical Exploration > 2022 > 46(6): 1444-1453

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SHI Zhan-Zhan, PANG Su, WANG Yuan-Jun, CHI Yue-Long, ZHOU Qiang. 2022. Random noise attenuation of common offset gathers by f-x low-rank matrix approximation with nonconvex regularization. Geophysical and Geochemical Exploration, 46(6): 1444-1453. doi: 10.11720/wtyht.2022.1533

Citation:

SHI Zhan-Zhan, PANG Su, WANG Yuan-Jun, CHI Yue-Long, ZHOU Qiang. 2022. Random noise attenuation of common offset gathers by f-x low-rank matrix approximation with nonconvex regularization. Geophysical and Geochemical Exploration, 46(6): 1444-1453. doi: 10.11720/wtyht.2022.1533

Random noise attenuation of common offset gathers by f-x low-rank matrix approximation with nonconvex regularization

1. School of Artificial Intelligence, Leshan Normal University, Leshan 614000, China；
2. The Engineering and Technical College, Chengdu University of Technology, Leshan 614000, China；
3. College of Geophysics, Chengdu University of Technology, Chengdu 610059, China；
4. School of Land and Resources, China West Normal University, Nanchong 637002, China

Received Date: 24 September 2021

Revised Date: 20 December 2022

Publish Date: 03 January 2023

Abstract

Abstract

Random noise attenuation played an important role in the seismic data processing. The low-rank estimation of the seismic signal in the time-frequency domain is essentially a trace-by-trace process, which cannot exploit the channel-to-channel coherence of the signal. We propose a novel random noise attenuation based on f-x low-rank matrix approximation with nonconvex regularization. Firstly, the noisy seismic data is transformed into the f-x domain by Fourier transform. Then, the time-frequency method is employed to decompose each discrete frequency slice. Finally, we estimate the sparse low-rank matrix from the obtained noisy matrix. This method enables the denoising of non-stationary signals by exploiting the spectral differences between signal and noise. Compared with the common shot and mid-point gathers, the common offset gather is characterized by flat events, which basically satisfies the assumption of linear events for f-x domain denoising, and it is suggested that the proposed algorithm should be applied to the common offset gather. Synthetic and real data sets demonstrate the performance of our proposed method in random noise suppression and preserving more useful energy.
- f-x domain /
- time-frequency /
- low-rank matrix approximation /
- common offset gather /
- random noise

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