1887

Abstract

Summary

Least-squares migration (LSM) generates better quality migration images than conventional migration by removing migration artifacts, balancing reflector amplitudes, and improving the spatial resolution. Accurate consideration of the inverse Hessian is of great importance in the context of LSM, because the convergence rate would be accelerated to some extent. In this study, we adapt the truncated Gauss-Newton method to LSM, in which the Gauss-Newton normal equation is iteratively solved with a matrix-free conjugate gradient algorithm. The Hessian-vector product is calculated based on a demigration/migration procedure. The numerical example proves the feasibility of our method.

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/content/papers/10.3997/2214-4609.201412782
2015-06-01
2024-04-20
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