Elastic Least-Squares Gaussian Beam Imaging With Point Spread Functions

Abstract

Elastic least-squares migration (ELSM) has the potential to produce high-resolution images. It can be implemented in either data-domain or image-domain but is much faster in the image domain. A critical step of image-domain ELSM is the calculation of the Hessian. However, it is impractical to directly calculate the Hessian due to its high storage and costs. In this letter, the Hessian is efficiently constructed with elastic point spread functions (PSFs) calculated by a combination of multicomponent Gaussian beam Born modeling (of scattering points with elastic parameters perturbation) and elastic Gaussian beam migration. Based on this, we propose a fast image-domain ELSM method. A hyper-Laplacian priori regularization is used to produce sparse solutions. We evaluate the proposed method with the Marmousi2 model, and the results demonstrate the capability of the method to image complex structures with improved resolution relative to the initial migrated image.