Regularized least-squares reverse time migration of simultaneous-source data with adaptive singular spectrum analysis

摘要

We propose the regularized least-squares reverse time migration method (RLSRTM) using the singular spectrum analysis (SSA) technique that imposes sparseness constraints on the inverted model. The difference spectrum theory of singular values is presented so that SSA denoising can be implemented adaptively to eliminate the migration artifacts introduced by simultaneous-source data. Similarly, we suggest that RLSRTM is also able to eliminate the migration artifacts caused by incomplete data and noisy data. With the numerical tests on Marmousi2 model, we validate the superior imaging quality and convergence of RLSRTM compared with LSRTM when dealing with simultaneous-source data, incomplete data and noisy data.