Full Waveform Inversion and Lagrange Multipliers

TL;DR

This paper introduces a unified Lagrangian framework for full-waveform inversion, proposing multiplier-based algorithms with better physical interpretation and convergence, and analyzes their underlying mechanisms using inverse scattering methods.

Contribution

It presents a novel Lagrangian formulation for FWI, replacing wavefields with LS multipliers, and provides new physical insights into the role of Lagrange multipliers in algorithm performance.

Findings

Multiplier-based algorithms improve convergence and physical interpretability.

Lagrange multipliers enhance the linearization of FWI equations.

New understanding of the role of multipliers in inverse scattering methods.

Abstract

Full-waveform inversion (FWI) is an effective method for imaging subsurface properties using sparsely recorded data. It involves solving a wave propagation problem to estimate model parameters that accurately reproduce the data. Recent trends in FWI have led to the development of extended methodologies, among which source extension methods leveraging reconstructed wavefields to solve penalty or augmented Lagrangian (AL) formulations have emerged as robust algorithms, even for inaccurate initial models. Despite their demonstrated robustness, challenges remain, such as the lack of a clear physical interpretation, difficulty in comparison, and reliance on difficult-to-compute least squares (LS) wavefields. This paper is divided into two critical parts. In the first, a novel formulation of these methods is explored within a unified Lagrangian framework. This novel perspective permits the introduction of alternative algorithms that employ LS multipliers instead of wavefields. These multiplier-oriented variants appear as regularizations of the standard FWI, are adaptable to the time domain, offer tangible physical interpretations, and foster enhanced convergence efficiency. The second part of the paper delves into understanding the underlying mechanisms of these techniques. This is achieved by solving the FWI equations using iterative linearization and inverse scattering methods. The paper provides insight into the role and significance of Lagrange multipliers in enhancing the linearization of FWI equations. It explains how different methods estimate multipliers or make approximations to increase computing efficiency. Additionally, it presents a new physical understanding of the Lagrange multiplier used in the AL method, highlighting how important it is for improving algorithm performance when compared to penalty methods.