Random noise suppression based on the improved total generalized variation with overlapping group sparsity
CHEN Yingpin1,2, PENG Zhenming1,3, LI Meihui1, YU Fei2
1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China; 2. School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, Fujian 363000, China; 3. Center for Information Geosciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
Abstract:The total generalized variation (TGV) denoising is a conventional way to suppress random noise in seismic data.This paper introduces the overlapping group sparsity into the TGV model and proposes an improved TGV denoising model.The proposed model explores the structural property of the first and second orders' image differential information.As a result,the proposed model performs better than the TGV model.We adopt the alternating direction method of multipliers (ADMM) to solve the proposed model.In the framework of ADMM,the multi-constrained problem is divided into several sub-problems that are easier to be solved.Furthermore,to improve the efficiency of the algorithm,we use the fast Fourier transform.Experiments verify that the proposed method outperforms than conventional TGV,especially for removing strong noise.
陈颖频, 彭真明, 李美惠, 喻飞. 基于交叠组稀疏广义全变分的地震信号随机噪声衰减[J]. 石油地球物理勘探, 2019, 54(1): 24-35,44.
CHEN Yingpin, PENG Zhenming, LI Meihui, YU Fei. Random noise suppression based on the improved total generalized variation with overlapping group sparsity. Oil Geophysical Prospecting, 2019, 54(1): 24-35,44.
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