Fast large-scale gravity modeling using a high-order compact cascadic multigrid strategy
Shuanggui Hu, Kejia Pan, Zhengyong Ren, Jingtian Tang, Jinxuan Wang, Pengde Wang
Abstract
ABSTRACT A new high-order compact (HOC) cascadic multigrid (MG) strategy is developed for modeling large-scale complex gravity problems on nonuniform rectilinear grids. First, the governing 3D Poisson’s gravitational potential equation is discretized by a novel HOC difference scheme, which allows for Robin boundary conditions to further reduce the computational domain. Then, the resulting asymmetric linear system of equations is solved by a generalized extrapolation cascadic MG (gEXCMG) method augmented with a novel MG prolongation operator. The quartic interpolation and completed Richardson extrapolation assist this MG prolongation operator to produce a good initial guess for the biconjugate gradient stabilized smoother. Two synthetic and SEG/EAGE salt models are examined to verify the presented approach. Results indicate that our algorithm can efficiently and accurately produce the gravity signals for complex geologic models. Moreover, the comparisons with the state-of-the-art algebraic MG solvers demonstrate that our gEXCMG solver offers substantially better efficiency with significantly less memory consumption. Therefore, our newly developed method has the potential for serving as a forward engine in large-scale gravity inversions.