Seismic Data Denoising Based on Sparse and Low-Rank Regularization

Shu Li, Xi Yang, Haonan Liu, Yuwei Cai and Zhenming Peng
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Shu Li: School of Information Science and Engineering, Jishou University, Jishou 416000, China
Xi Yang: School of Information Science and Engineering, Jishou University, Jishou 416000, China
Haonan Liu: School of Information Science and Engineering, Jishou University, Jishou 416000, China
Yuwei Cai: School of Information Science and Engineering, Jishou University, Jishou 416000, China
Zhenming Peng: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

Energies, 2020, vol. 13, issue 2, 1-16

Abstract: Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications. For the past ten years, there have mainly been two classes of methods for seismic denoising. One is based on the sparsity of seismic data. This kind of method can make use of the sparsity of seismic data in local area. The other is based on nonlocal self-similarity, and it can utilize the spatial information of seismic data. Sparsity and nonlocal self-similarity are important prior information. However, there is no seismic denoising method using both of them. To jointly use the sparsity and nonlocal self-similarity of seismic data, we propose a seismic denoising method using sparsity and low-rank regularization (called SD-SpaLR). Experimental results showed that the SD-SpaLR method has better performance than the conventional wavelet denoising and total variation denoising. This is because both the sparsity and the nonlocal self-similarity of seismic data are utilized in seismic denoising. This study is of significance for designing new seismic data analysis, processing and inversion methods.

Keywords: seismic denoising; sparse; low-rank; self-similarity; total variation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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