Fast 3D Least-Squares RTM by Preconditioning with Non-Stationary Matching Filters
نویسنده
چکیده
I approximate the inverse Hessian (L′L)−1, where L is a Born modeling operator and L′ is the corresponding adjoint RTM operator, with non-stationary matching filters. These non-stationary filters can be seen as a low-rank approximation of the true inverse Hessian. For 3D least-squares imaging, I use these matching filters to precondition the inversion of prestack seismic data and observe a significant convergence speed-up.
منابع مشابه
Preconditioned 3D Least-Squares RTM with Non-Stationary Matching Filters
I approximate the inverse Hessian (L′L)−1, where L is a Born modeling operator and L′ is the corresponding adjoint RTM operator, with non-stationary matching filters. These non-stationary filters can be seen as a low-rank approximation of the true inverse Hessian. For 3D least-squares imaging, I use these matching filters to precondition the inversion of prestack seismic data and observe a sign...
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