antoine@sep.stanford.edu

## ABSTRACTObtaining true-amplitude migrated images remains a challenging problem. One possible solution to address it is iterative inversion. However, inversion is an expensive process that can be rather difficult and expensive to apply, especially with 3-D data. In this paper, I propose computing an image that is close to the least-squares inverse image by approximating the Hessian, thus avoiding the need for iterative inversion. The Hessian is approximated with non-stationary matching filters. These filters are estimated from two images: one is the migration result () and the second is the migration result of the remodeled data computed from . Tests on the Marmousi dataset show that this filtering approach gives results similar to iterative least-squares inversion at a lower cost. In addition, because no regularization was used in the inversion process, the filtering method produces an image with fewer artifacts. Applying this method in the angle domain yields similar conclusions. |

7/8/2003