Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution

Abstract

The classical least-squares migration (LSM) translates seismic imaging into a data-fitting optimization problem to obtain high-resolution images. However, the classical LSM is highly dependent on the precision of seismic wavelet and velocity models, and thus it suffers from an unstable convergence and excessive computational costs. In this paper, we propose a new LSM method in the imaging domain. It selects a spatial-varying point spread function to approximate the accurate Hessian operator and uses a high-dimensional spatial deconvolution algorithm to replace the common-used iterative inversion. To keep a balance between the inversion precision and the computational efficiency, this method is implemented based on the strategy of regional division, and the point spread function is computed using only one-time demigration/migration and inverted individually in each region. Numerical experiments reveal the differences in the spatial variation of point spread functions and highlight the importance to use a space-varying deconvolution algorithm. A 3D field case in Northwest China can demonstrate the effectiveness of this method on improving spatial resolution and providing better characterizations for small-scale fracture and cave units of carbonate reservoirs.

Keywords

• least-squares migration
• imaging domain
• point spread function
• global space-varying deconvolution