[an error occurred while processing this directive] ������� 2018, 37(2) 627-635 DOI:   10.3969/j.issn.1004-5589.2018.02.030  ISSN: 1004-5589 CN: 22-1111/P

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PubMed
Article by Cheng S
Article by Han L
Article by Yu J
Article by Zhang F
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Seismic data denoising based on improved K-SVD dictionary learning method
CHENG Shi-jun1, HAN Li-guo1, YU Jiang-long2, ZHANG Feng-jiao1
1. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China;
2. PetroChina Xinjiang Oilfield Branch Exploration and Development Research Institute, Kelamayi 834000, Xingjiang, China
Abstract: In order to achieve better seismic data denoising technology, a new algorithm is introduced in this paper:fast iterative shrinkage-thresholding algorithm(FISTA). The K-SVD dictionary is iteratively updated by FISTA and K-singular value decomposition (K-SVD). The updated K-SVD dictionary sparsely represents the seismic data and removes the smaller sparse coefficients, which suppresses the random noise in the data. Comparison among simulation data, Marmousi model seismic data and actual seismic data indicates that FISTA algorithm can improve signal-to-noise ratio of seismic data and protect reflection signal more effectively than orthogonal matching pursuit (OMP) algorithm.
Keywords: K-SVD dictionary   fast iterative shrinkage-thresholding algorithm   orthogonal matching pursuit algorithm   sparse representation   random noise  
�ո����� 2017-11-13 �޻����� 2018-03-06 ����淢������  
DOI: 10.3969/j.issn.1004-5589.2018.02.030
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