Multipliers waveform inversion

TL;DR

This paper introduces a multipliers-based waveform inversion method that enhances full-waveform inversion (FWI) by improving convergence, robustness, and compatibility with existing FWI tools, especially when starting from inaccurate models.

Contribution

It formulates FWI as a constrained optimization problem solved via the augmented Lagrangian method, leading to a recursive algorithm that improves convergence and robustness.

Findings

Converges without low-frequency data from inaccurate initial models.

Requires minimal changes to existing FWI engines.

Demonstrates improved convergence and robustness in numerical experiments.

Abstract

The full-waveform inversion (FWI) addresses the computation and characterization of subsurface model parameters by matching predicted data to observed seismograms in the frame of nonlinear optimization. We formulate FWI as a nonlinearly constrained optimization problem, for which a regularization term is minimized subject to the nonlinear data matching constraint. Unlike FWI which is based on the penalty function, the method of multipliers solves the resulting optimization problems by using the augmented Lagrangian function; and leads to a two-step recursive algorithm. The primal step requires solving an unconstrained minimization problem like the traditional FWI with a difference that the data are replaced by the Lagrange multipliers. The dual step involves an update of the Lagrange multipliers. The overall performance of the algorithm is improved considering that this multiplier method does not require an exact solution of these primal-dual subproblems. In fact, convergence is attained when only one step of a gradient-based method is taken on both subproblems. The proposed algorithm greatly improves the overall performance of FWI such as convergence from inaccurate starting models and robustness with respect to the determination of the step length. Furthermore, it can be performed by the existing FWI engines with minimal change. We only have to replace the observed data at each iteration with the multipliers, thus all the nice properties of the traditional FWI algorithms are kept. Numerical experiments confirm that the multipliers waveform inversion can converge to a solution of the inverse problem in the absence of low-frequency data from an inaccurate initial model even with a constant step size.