Least-Squares Full-Wavefield Reverse Time Migration Using a Modeling Engine With Vector Reflectivity

Conventional least-squares reverse time migration (LSRTM) generally involves a de-migration operator based on the first-order scattering approximation (Born modeling), which can only simulate the seismograms containing the primary reflected wave. When the input observed seismograms contain "redundant information " (especially multiples), crosstalk may occur in the imaging results. Therefore, we develop a least-squares full-wavefield reverse time migration (LSFWM), which is implemented based on a two-way modeling engine with vector reflectivity and the corresponding adjoint sensitive kernel. This modeling engine is modified from the variable density acoustic wave equation and can simulate the subsurface wavefield containing the primaries and multiples only by giving the accurate or estimated subsurface reflectivity and velocity. Theoretically, this LSFWM approach can eliminate the influence of "redundant information " on imaging and provide higher-quality imaging results compared to conventional LSRTM. In addition, since the modeling engine is based on vector reflectivity, the imaging results produced by the LSFWM are also vectorized, which can give more information about the subsurface structures, especially steep structures. And the imaging results produced by the LSFWM can accurately depict the subsurface reflectivity. These are helpful to obtain the information on subsurface structure and physical properties more clearly.