Random noises suppression based on overlapping group sparsity, non-convex Lp-pseudo-norm regula-rization and higher-order total variation
LIANG Shanglin1, HU Tianyue1, CUI Dong2, SUI Jingkun1,2
1. School of Earth and Space Sciences, Peking University, Beijing 100871, China; 2. Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Abstract：Suffering serious staircase effects, the higher-order total variation model provides unsatisfactory denoising results. In this paper, we introduce a technique of overlapping group sparsity, and utilize the non-convex Lp-pseudo-norm for preserving weak signals. Our new model can fully exploit local similarity of signals instead of unrelated individual data. To solve the multi-constrained problem, we adopt the alternating direction method of multipliers to divide the whole problem into four sub-problems. The iteratively re-weighted alternating direction L1-norm and majorization minimization algorithm are added into the algorithm to improve the efficiency and accuracy. Applied to synthetic and field data, the improved method not only reduced staircase effects and attenuated random noises, but also effectively preserved weak signals.
梁上林, 胡天跃, 崔栋, 隋京坤. 基于交叠组稀疏非凸Lp伪范数高阶全变分的地震随机噪声压制[J]. 石油地球物理勘探, 2021, 56(1): 69-76.
LIANG Shanglin, HU Tianyue, CUI Dong, SUI Jingkun. Random noises suppression based on overlapping group sparsity, non-convex Lp-pseudo-norm regula-rization and higher-order total variation. Oil Geophysical Prospecting, 2021, 56(1): 69-76.
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