Regularized least-squares reverse time migration with prior model
Li Zhenchun1, Li Chuang1, Huang Jianping1, Wang Rongrong2
1. School of Geosiences, China University of Petroleum (East China), Qingdao, Shandong 266580, China;
2. College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China
Abstract：Least-squares reverse time migration (LSRTM) converges slowly, and sometimes drops into local extremum because of the ill-posedness of the inversion problem. On the other hand, the influence of irregular geometry and the absorption of underground layers result in some blank areas of illumination, in which the structures cannot be imaged by LSRTM. To solve these problems, the paper presents the theory of regularized least-squares reverse time migration (RLSRTM) with prior model. The prior model is constructed from logging data and incorporated into LSRTM as the regularization constraint. And dynamic regularization parameters and preconditioned regularization-term gradients are proposed to ensure better constraint. Based on numerical tests on a sparse Marmousi model, the following observation are obtained: 1LSRTM can suppress the migration artifacts and compensate energy in deep part compared with reverse time migration (RTM), but the compensation to uneven illumination is limited and the structures in blank illumination areas cannot be imaged; 2RLSRTM with preconditioned regularization-term gradient can further compensate energy of structures with poor illumination, produce more clear images of the boundary of anticlines and other layers in deep part, and even recover some information in blank illumination areas; 3RLSRTM without preconditioned regularization-term gradient produces images with some blurry boundaries and false structures. Therefore, RLSRTM with preconditioned regularization-term gradient can accelerate the convergence, improve the resolution and amplitude preservation of the images, ensure the stability of the inversion, and has reduced sensitivity to low signal-to-noise ratio shot data compared with LSRTM and RLSRTM without preconditioned regularization-term gradient.
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