Shot-record extended model domain preconditioners for least-squares migration

Abstract

We have studied the preconditioned conjugate gradient (CG) algorithm in the context of shot-record extended model domain least-squares migration. The CG algorithm is a powerful iterative technique that can solve the least-squares migration problem efficiently; however, to see the merits of least-squares migration, one needs to apply the algorithm for several iterations. Generally speaking, the convergence rate of the CG algorithm depends on the condition number of the operator. Preconditioners are a family of operators that are easy to build and invert. Proper preconditioners can cluster the eigenvalues of the original operator; hence, they reduce the condition number of the operator that one wishes to invert. Accordingly, preconditioning the operator can, in theory, improve the convergence rate of the algorithm. In least-squares migration, the diagonal scaling of the Hessian and the approximated inverse of the Hessian are proven to work well as a preconditioner. We develop and apply two types of preconditioners for the shot-record extended model domain least-squares migration problem. The first preconditioner belongs to the diagonal scaling category, and a second preconditioner is a filter-based approach, which approximates the partial Hessian operators by local convolutional filters. The goal is to increase the convergence rate of the shot-record extended model domain least-squares migration using the reformulated cost function with a preconditioned operator. Experiments with a synthetic Sigsbee model and a real data example from the Gulf of Mexico, Mississippi Canyon data set, indicate that preconditioning the linear system of the equations improves the convergence rate of the algorithm.