Random noise suppression based on sparse representation of multi-trace similarity group
Zhang Yan1,2, Ren Weijian2, Tang Guowei1
1. School of Computer and Information Technology, Northeast Petroleum University, Daqing, Heilongjiang 163318, China;
2. School of Electrical Engineering and Information, Northeast Petroleum University, Daqing, Heilongjiang 163318, China
Abstract：The single orthogonal transform could not adaptively adjust basis functions according to seismic data characteristics in noise suppression based on sparse representations,and the block-based over-complete learning dictionary methods usually ignores the similarities among blocks in random noise suppression. So we propose a novel denoising algorithm based on sparse representation model of multi-trace similarity group. As there is a strong waveform similarity between adjacent traces,we first construct multi-trace similarity groups,calculate the waveform similarity of these groups with the target seismic data block in a training window,and obtain the multi-trace highest similarity group. Then the adaptively learning algorithm of over-complete dictionary based on multi-trace similarity group is adopted to obtain a learning dictionary and sparse code. Finally,L1 norm minimization problem is solved with iterative threshold shrinkage algorithm. As the sparse degree of coding coefficients is gradually promoted,main seismic data characteristics are retained and random noise is suppressed. Comparing with the existing denoising algorithms,the proposed algorithm can yield higher peak signal-to-noise ratio (PSNR),and better preserve local event features in complex areas.
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