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Article by Li H
Article by Han L
Article by Zhang L
Article by Jia S
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Separation of blended seismic acquisition with K-SVD dictionary in Radon domain
LI Hui, HAN Li-guo, ZHANG Liang, JIA Shuai
College of Geo-exploratiom Science and Technology, Jilin University, Changchun 130026, China
Abstract: The authors propose an approach that can separate blended seismic acquisition data using seismic sparse inversion. Meanwhile, the proposed approach updates the dictionary atoms via K-time iterative singular value decomposition (K-SVD) in the Radon domain:the separation of blended seismic data can be taken as sparse inversion for simultaneous sources, therefore, for common receiver point data, the reflectors of the effective signal are convergent in Radon domain; after that, the filters and block dictionary learning are applied to the blended data to sparsely represent the seismic data; finally, the updated dictionary is fixed and the sparse coefficients are calculated to achieve the separation. Through the synthetic and field data experiments, it is concluded that separation method of the proposed approach is more accurate than median filter and wavelets transform, and can be applied in the separation of blended field seismic data.
Keywords: separation of blended seismic acquisition   Radon domain   K-SVD   block dictionary learning  
�ո����� 2018-09-04 �޻����� 2018-12-12 ����淢������  
DOI: 10.3969/j.issn.1004-5589.2019.01.025
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