查看文章

The Hankel matrix’s low-rank property derived from the noise-free seismic data describing a few linear events and has been successively leveraged in many low-rank seismic data de-noising approaches. In such rank reduction methods, the typical scheme is to determine the best low-rank estimation of the formulated Hankel matrix, and then obtain the de-noised data. However, if the noisy data has been rearranged for the low-rank approximation in a Hankel matrix, it is usually not precisely low-rank. In the presented research, we propose a multivariate generalization of the minimax-concave penalty (MCP) function inducing sparsity on seismic data in the time-space domain. Initially obtained sparse representation of data would be decomposed into semi low-rank and the sparse components with the best approximate of noisy measurement matrix would be defined. This would be performed through the low-rank …