Application of shifted-Laplace preconditioners for heterogenous Helmholtz equation- part 2: Full waveform inversion

TL;DR

This paper demonstrates that using shifted-Laplace preconditioners in frequency domain full waveform inversion improves convergence and model quality when reconstructing Earth's physical properties from seismic data.

Contribution

It introduces the application of shifted-Laplace preconditioners within FWI to enhance convergence and model accuracy in the frequency domain seismic inversion.

Findings

Improved convergence of FWI with preconditioners.

Enhanced quality of inverted models.

Effective handling of high-frequency data.

Abstract

Seismic waves bring information from the physical properties of the earth to the surface. Full waveform inversion (FWI) is a local optimization technique which tries to invert the recorded wave fields to the physical properties. An efficient forward-modelling engine along with local differential algorithm, to compute the gradient and Hessian operators, are two key ingredients of FWI approach. FWI can be done in time or frequency domain. Each method has its own pros and cons. Here, we only discuss frequency domain method with Krylov subspace solvers for time-harmonic wave equation. Nonlinearity of the problem requires good initial macro model of the physical properties and low frequency data. Macro models are built based on the kinematic information of the recorded wave fields. Another difficulty is the data modelling algorithm which is hard to solve especially for high wavenumbers (high frequencies). Without incorporation of high frequencies in the FWI algorithm we are not going to be able to update the macro models to high resolution ones. In the companion paper we showed that efficient forward modelling algorithms can be reached via proper preconditioners. Here, we will use the preconditioned data modelling engine in the context of local optimization method to solve for model parameters. The results show that we yield better convergence of the method and better quality of the inverted models after using the preconditioned FWI.