Randomized source sketching for full waveform inversion

TL;DR

This paper introduces a randomized source sketching technique for seismic full waveform inversion that significantly reduces computational costs by decreasing the number of PDE solves while maintaining convergence through regularization.

Contribution

It proposes a unified randomized sketching framework for multisource PDE problems, improving efficiency in seismic FWI with minimal accuracy loss.

Findings

Reduces computational cost by at least an order of magnitude.

Maintains convergence speed with sparsity-promoting regularization.

Effective in large-scale 2D seismic benchmarks.

Abstract

Partial differential equation (PDE) constrained optimization problems such as seismic full waveform inversion (FWI) frequently arise in the geoscience and related fields. For such problems, many observations are usually gathered by multiple sources, which form the right-hand-sides of the PDE constraint. Solving the inverse problem with such massive data sets is computationally demanding, in particular when dealing with large number of model parameters. This paper proposes a novel randomized source sketching method for the efficient resolution of multisource PDE constrained optimization problems. We first formulate the different source-encoding strategies used in seismic imaging into a unified framework based on a randomized sketching. To this end, the source dimension of the problem is projected in a smaller domain by a suitably defined projection matrix that gathers the physical sources in super-sources through a weighted summation. This reduction in the number of physical sources decreases significantly the number of PDE solves while suitable sparsity-promoting regularization can efficiently mitigate the footprint of the cross-talk noise to maintain the convergence speed of the algorithm. We implement the randomized sketching method in an extended search-space formulation of frequency-domain FWI, which relies on the alternating direction method of multipliers (ADMM). Numerical examples carried out with a series of well documented 2D benchmarks demonstrate that the randomized sketching algorithm reduces the cost of large-scale problems by at least one order of magnitude compared to the original deterministic algorithm.