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Open AccessArticle

Seismic Data Denoising Based on Sparse and Low-Rank Regularization

1
School of Information Science and Engineering, Jishou University, Jishou 416000, China
2
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
*
Author to whom correspondence should be addressed.
Energies 2020, 13(2), 372; https://doi.org/10.3390/en13020372
Received: 28 November 2019 / Revised: 30 December 2019 / Accepted: 8 January 2020 / Published: 13 January 2020
(This article belongs to the Section Geo-Energy)
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. View Full-Text
Keywords: seismic denoising; sparse; low-rank; self-similarity; total variation seismic denoising; sparse; low-rank; self-similarity; total variation
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Li, S.; Yang, X.; Liu, H.; Cai, Y.; Peng, Z. Seismic Data Denoising Based on Sparse and Low-Rank Regularization. Energies 2020, 13, 372.

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