Simultaneous-source acquisition has been recognized as an economic and efficient acquisition method, but the direct imaging of the simultaneous-source data produces migration artifacts because of the interference of adjacent sources. To overcome this problem, we propose the regularized least-squares reverse time migration method (RLSRTM) using the singular spectrum analysis technique that imposes sparseness constraints on the inverted model. Additionally, the difference spectrum theory of singular values is presented so that RLSRTM can be implemented adaptively to eliminate the migration artifacts. With numerical tests on a flat layer model and a Marmousi model, we validate the superior imaging quality, efficiency and convergence of RLSRTM compared with LSRTM when dealing with simultaneous-source data, incomplete data and noisy data.

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sparseness constraints interference of adjacent difference spectrum theory of singular values direct imaging of the simultaneoussource data flat layer model rlsrtm migration artifacts imaging quality efficiency acquisition lsrtm regularized leastsquares reverse time migration method the inverted model convergence

.Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis. 14 (1),61–74.

"Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis" 14

"Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis"