A block-acoustic preconditioner for the elastic Helmholtz equation

TL;DR

This paper introduces a novel block-preconditioner for the elastic Helmholtz equation that reduces the problem to acoustic Helmholtz equations, enabling efficient and scalable solutions for complex wave propagation in heterogeneous media.

Contribution

The paper develops a block-triangular preconditioner based on acoustic Helmholtz operators, with proven convergence conditions and demonstrated scalability and efficiency over existing monolithic multigrid methods.

Findings

Achieves lower computational cost than monolithic multigrid methods.

Demonstrates scalability with respect to Poisson ratio and grid size.

Enables fast solutions for wave problems in 2D and 3D heterogeneous media.

Abstract

We present a novel block-preconditioner for the elastic Helmholtz equation, based on a reduction to acoustic Helmholtz equations. Both versions of the Helmholtz equations are challenging numerically. The elastic Helmholtz equation is larger, as a system of PDEs, and harder to solve due to its more complicated physics. It was recently suggested that the elastic Helmholtz equation can be reformulated as a generalized saddle-point system, opening the door to the current development. Utilizing the approximate commutativity of the underlying differential operators, we suggest a block-triangular preconditioner whose diagonal blocks are acoustic Helmholtz operators. Thus, we enable the solution of the elastic version using virtually any existing solver for the acoustic version as a black-box. We prove a sufficient condition for the convergence of our method, that sheds light on the long questioned role of the commutator in the convergence of approximate commutator preconditioners. We show scalability of our preconditioner with respect to the Poisson ratio and with respect to the grid size. We compare our approach, combined with multigrid solve of each block, to a recent monolithic multigrid method for the elastic Helmholtz equation. The block-acoustic multigrid achieves a lower computational cost for various heterogeneous media, and a significantly lower memory consumption, compared to the monolithic approach. It results in a fast solution method for wave propagation problems in challenging heterogeneous media in 2D and 3D.