Fast least-squares migration with a deblurring filter

摘要

Least-squares migration LSM is a linearized waveforminversion for estimating a subsurface reflectivity model that,relative to conventional migration, improves spatial resolutionof migration images. The cost, however, is high becauseLSMtypically requires 10 or more iterations, which is at least20 times more than the CPU cost of conventional migration.To alleviate this expense, we offer a deblurring filter that canbe used in a regularization scheme or a preconditioningscheme to give acceptable LSM images with less than onethirdthe cost of the standard LSM method. Our results in applyingdeblurredLSMto synthetic data and field data supportthis claim. In particular, a Marmousi2 model test shows thatthe data residual for preconditioned deblurred LSM decreasesrapidly in the first iteration, which is equivalent to 10 ormore iterations of LSM. Empirical results suggest that regularizeddeblurred LSM after three iterations is equivalent toabout 10 iterations of LSM. Applying deblurred LSM to 2Dmarine data gives a higher-resolution image compared tothose from migration or LSM with three iterations. These resultssuggest that LSM combined with a deblurring filter allowsLSM to be a fast, practical tool for improved imaging ofcomplicated structures.