Preconditioned prestack plane-wave least squares reverse time migration with singular spectrum constraint
Li Chuang1, Huang Jian-Ping1, Li Zhen-Chun1, and Wang Rong-Rong2
1. School of Geosciences, China University of Petroleum, Qingdao 266580, China.
2. Hisense (Shandong) Refrigerator Co. Ltd, Hisense, Qingdao 266580, Shandong, China.
Abstract：
Least squares migration can eliminate the artifacts introduced by the direct imaging of irregular seismic data but is computationally costly and of slow convergence. In order to suppress the migration noise, we propose the preconditioned prestack plane-wave least squares reverse time migration (PLSRTM) method with singular spectrum constraint. Singular spectrum analysis (SSA) is used in the preconditioning of the take-off angle-domain common-image gathers (TADCIGs). In addition, we adopt randomized singular value decomposition (RSVD) to calculate the singular values. RSVD reduces the computational cost of SSA by replacing the singular value decomposition (SVD) of one large matrix with the SVD of two small matrices. We incorporate a regularization term into the preconditioned PLSRTM method that penalizes misfits between the migration images from the plane waves with adjacent angles to reduce the migration noise because the stacking of the migration results cannot effectively suppress the migration noise when the migration velocity contains errors. The regularization imposes smoothness constraints on the TADCIGs that favor differential semblance optimization constraints. Numerical analysis of synthetic data using the Marmousi model suggests that the proposed method can efficiently suppress the artifacts introduced by plane-wave gathers or irregular seismic data and improve the imaging quality of PLSRTM. Furthermore, it produces better images with less noise and more continuous structures even for inaccurate migration velocities.
. Preconditioned prestack plane-wave least squares reverse time migration with singular spectrum constraint[J]. APPLIED GEOPHYSICS, 2017, 14(1): 73-86.
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2017, Vol. 14 
