Random noise suppression on seismic data based on structured-clustering dictionary learning
Zhang Yan1,2, Ren Weijian2, Tang Guowei3
1. School of Computer and Information Technology, Northeast Petroleum University, Daqing, Heilongjiang 163318, China; 2. School of Electrical Engineering & Information, Northeast Petroleum University, Daqing, Heilongjiang 163318, China; 3. Modern Educational Technology Center, Northeast Petroleum University, Daqing, Heilongjiang 163318, China
Abstract:Since seismic waveforms vary greatly in different spatial positions,the sparse representation based on the global dictionary learning is not enough to provide optimal sparse representations of local seismic data features.Therefore we propose a random noise suppression based on sparse representations of structured-clustering dictionary learning.First the regularity and redundancy in the coefficients distribution are represented by the self similarity of seismic data block structures and the global dictionary sparse representation,the K-means method is used to cluster seismic data blocks.Then according to the structural features,the overcomplete dictionary is obtained by singular value decomposition (SVD) for a class of data blocks.So the seismic data blocks are recorded according to every clustering center,and the original seismic data are more sparsely represented and described.After that,the regularization model is established to update centroid and estimated values of seismic data.Finally,the dual variable iterative threshold algorithm is used to solve the optimization problem of the double L1 norm in the model,and the noise components are removed.Based on our experiments,the proposed algorithm can obtain higher peak signal to noise ratio,and better local seismic textures,which proves the effectiveness of the proposed algorithm in the random noise suppression.
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