Seismic Data Denoising With Correlation Feature Optimization Via S-Mean

Sun, Fengyuan; Liao, Guisheng; Lou, Yihuai; Xing, Jun

doi:10.1109/lgrs.2021.3117965

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…In recent years, numerous signal processing methods have emerged for the separation and suppression of seismic noise (Yuan et al, 2012;Li et al, 2014;Zhang et al, 2021;Ni et al, 2022;Sun et al, 2022;. These methods include singular spectrum analysis (Oropeza and Sacchi, 2011), empirical mode decomposition-based techniques (Bekara and Baan, 2009), wavelet transform (Yang et al, 2018), and curvelet transform (Qu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing with log-sum heuristic recover for seismic denoising

Sun,

Zhang,

Wang

et al. 2024

Front. Earth Sci.

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The compressed sensing (CS) method, commonly utilized for restructuring sparse signals, has been extensively used to attenuate the random noise in seismic data. An important basis of CS-based methods is the sparsity of sparse coefficients. In this method, the sparse coefficient vector is acquired by minimizing the l1 norm as a substitute for the l0 norm. Many efforts have been made to minimize the lp norm (0 < p < 1) to obtain a more desirable sparse coefficient representation. Despite the improved performance that is achieved by minimizing the lp norm with 0 < p < 1, the related sparse coefficient vector is still suboptimal since the parameter p is greater than 0 rather than infinitely approaching 0 p→0+. Therefore, the CS method with the limit p→0+ is proposed to enhance the sparse performance and thus generate better denoised results in this paper. Our proposed method is referred to as the CS-LHR method because the solving process for minimizing p→0+ is the log-sum heuristic recovery (LHR). Furthermore, to improve the computational efficiency, we incorporate the majorization-minimization (MM) algorithm in this CS-LHR method. Experimental results of synthetic and real seismic records demonstrate the remarkable performance of CS-LHR in random noise suppression.

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