Application of shifted-Laplace preconditioners for heterogenous Helmholtz equation- part 1: Data modelling

TL;DR

This paper explores the use of shifted-Laplace preconditioners to enhance the efficiency of solving the inhomogeneous Helmholtz equation in geophysical data modelling, demonstrating improved convergence of iterative methods.

Contribution

It introduces the application of shifted-Laplace operators as preconditioners within GMRES to improve Helmholtz equation solutions in data modelling.

Findings

Preconditioning accelerates GMRES convergence.

Shifted-Laplace preconditioners improve solution efficiency.

Numerical results validate the effectiveness of the approach.

Abstract

In several geophysical applications, such as full waveform inversion and data modelling, we are facing the solution of inhomogeneous Helmholtz equation. The difficulties of solving the Helmholtz equa- tion are two fold. Firstly, in the case of large scale problems we cannot calculate the inverse of the Helmholtz operator directly. Hence, iterative algorithms should be implemented. Secondly, the Helmholtz operator is non-unitary and non-diagonalizable which in turn deteriorates the performances of the iterative algorithms (especially for high wavenumbers). To overcome this issue, we need to im- plement proper preconditioners for a Krylov subspace method to solve the problem efficiently. In this paper we incorporated shifted-Laplace operators to precondition the system of equations and then generalized minimal residual (GMRES) method used to solve the problem iteratively. The numerical results show the performance of the preconditioning operator in improving the convergence rate of the GMRES algorithm for data modelling case. In the companion paper we discussed the application of preconditioned data modelling algorithm in the context of frequency domain full waveform inversion. However, the analysis of the degree of suitability of the preconditioners in the solution of Helmholtz equation is an ongoing field of study.