Target-oriented least-squares reverse-time migration using Marchenko double-focusing: reducing the artifacts caused by overburden multiples

TL;DR

This paper introduces a novel target-oriented LSRTM method utilizing Marchenko double-focusing to effectively reduce artifacts caused by overburden multiples, enhancing subsurface imaging accuracy with less computational effort.

Contribution

It presents a new algorithm that combines target-oriented LSRTM with Marchenko double-focusing to account for all overburden multiples using only a smooth velocity model.

Findings

Reduces artifacts from overburden multiples in target images.

Requires only a smooth background velocity model for overburden.

Improves imaging accuracy in complex overburden and subsalt regions.

Abstract

Geophysicists have widely used Least-squares reverse-time migration (LSRTM) to obtain high-resolution images of the subsurface. However, LSRTM needs an accurate velocity model similar to other migration methods. Otherwise, it suffers from depth estimation errors and out of focus images. Moreover, LSRTM is computationally expensive and it can suffer from multiple reflections. Recently, a target-oriented approach to LSRTM has been proposed, which focuses the wavefield above the target of interest. Remarkably, this approach can be helpful for imaging below complex overburdens and subsalt domains. Moreover, this approach can significantly reduce the computational burden of the problem by limiting the computational domain to a smaller area. Nevertheless, target-oriented LSRTM still needs an accurate velocity model of the overburden to focus the wavefield accurately and predict internal multiple reflections correctly. In recent years, Marchenko redatuming has emerged as a novel data-driven method that can predict Green's functions at any arbitrary depth, including all orders of multiples. The only requirement for this method is a smooth background velocity model of the overburden. Moreover, with Marchenko double-focusing, one can make virtual sources and receivers at a boundary above the target and bypass the overburden. This paper proposes a new algorithm for target-oriented LSRTM, which fits the double-focused data with modeled data at a boundary above the target of interest. Consequently, our target-oriented LSRTM algorithm correctly accounts for all orders of overburden-related multiples, resulting in a significant reduction of the artifacts caused by overburden internal multiple reflections in the target image compared to conventional LSRTM.