Anti-aliasing 5D seismic data reconstruction based on Radon transform constrained tensor CANDECOM\PARAFAC decomposition

ABSTRACT Tensor decomposition is an efficient and accurate method for reconstructing 5D seismic data with irregular missing traces, using tensor rank-reduction techniques to estimate the data’s low-rank structure. Nevertheless, the reconstruction performance is limited for regularly sampled data that satisfy the low-rank assumption, particularly under the condition of strong spatial aliasing. The Radon transform constrained tensor CANDECOM\PARAFAC decomposition (RCPD) combines sparse Radon transform with low-rank estimation, which can reconstruct regularly missing traces to some extent. However, the reconstruction accuracy can be improved with further anti-aliasing mechanism considerations. As the RCPD method can obtain slope-related Radon coefficients during the low-rank estimation, the extracted low-frequency slope information is used to further constrain the RCPD algorithm for aliased data reconstruction. Synthetic data experiments confirm that the proposed anti-aliasing RCPD method can effectively reconstruct the seismic data with regularly missing traces to improve the lateral continuity with high accuracy. Field data applications further demonstrat the feasibility of the proposed method in providing high-quality regular and dense data for subsequent seismic inversion and imaging.