Making the most out of least-squares migration

Migration Deconvolution versus Least Squares Migration

Both migration deconvolution (MD) and least squares migration (LSM) are capable of improving the resolution and suppress acquisition footprints in migrated images. In this report, I investigate the relative performance of these two methods in enhancing migration image quality, suppressing artifacts and computational efficiency. Both MD and LSM were implemented on synthetic data generated from p...

Migration deconvolution vs. least squares migration

Both migration deconvolution (MD) and least squares migration (LSM) are capable of improving the resolution and suppress acquisition footprints in migrated images. In this paper, we investigate the relative performance of these two technologies in enhancing migration image quality, suppressing artifacts and computational efficiency. Both MD and LSM were tested on SEG/EAGE overthrust models. The...

Making the most out of the least: new insights into congenital diaphragmatic hernia.

Sparse regularization for least-squares AVP migration

This paper presents least-squares wave equation AVP (Amplitude versus Ray Parameter) migration with non-quadratic regularization. We pose migration as an inverse problem and propose a cost function that makes use of a priori information about the AVP common image gather. In particular, we introduce two regularization goals: smoothness along the ray parameter direction and sparseness in the dept...

Block row recursive least squares migration

Recursive estimates of large systems of equations in the context of least squares fitting is a common practice in different fields of study. For example, recursive adaptive filtering is extensively used in signal processing and control applications. The necessity of solving least squares problem via recursive algorithms comes from the need of fast real-time signal processing strategies. Computa...