论文标题
使用Marchenko双重关注的目标面向目标最小二乘反向迁移:减少由倍增倍数造成的伪像
Target-oriented least-squares reverse-time migration using Marchenko double-focusing: reducing the artifacts caused by overburden multiples
论文作者
论文摘要
地球物理学家已广泛使用最小二乘反向时间迁移(LSRTM)来获得地下的高分辨率图像。但是,LSRTM需要类似于其他迁移方法的准确速度模型。否则,它会遭受深度估计错误和焦点图像的折磨。此外,LSRTM在计算上很昂贵,它可能会遭受多种反射。最近,已经提出了一种面向目标的LSRTM方法,该方法将波场集中在了感兴趣的目标之上。值得注意的是,这种方法有助于成像复杂的过上覆盖范围和海底领域。此外,这种方法可以通过将计算域限制为较小的区域来大大减轻问题的计算负担。然而,面向目标的LSRTM仍然需要一个精确的速度模型,以准确地聚焦波场并正确预测内部多重反射。近年来,Marchenko Redatuming已成为一种新型的数据驱动方法,可以在任何任意深度(包括所有倍数顺序)下预测Green的功能。该方法的唯一要求是覆盖量的平滑背景速度模型。此外,随着Marchenko双重关注,可以在目标上方的边界上制作虚拟来源和接收器,并绕过覆盖层。本文提出了一种针对目标的LSRTM的新算法,该算法将双重关注的数据符合引起关注目标的边界的建模数据。因此,我们面向目标的LSRTM算法正确地说明了与额叶相关的倍数的所有顺序,从而显着降低了由覆盖层的内部多重反射与常规LSRTM相比,目标图像中的内部多重反射引起的。
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.