Algorithmic analysis towards time-domain extended source waveform inversion

TL;DR

This paper analyzes extended domain full waveform inversion (FWI), including wavefield reconstruction and extended source inversion, focusing on mathematical formulation, computational challenges, and potential solutions for practical seismic imaging applications.

Contribution

It provides a unified mathematical framework for WRI and ESI in the time domain, explores efficient solution methods, and identifies storage challenges with proposed strategies.

Findings

Symmetric positive definite Hessian enables conjugate gradient solutions.

Storage demand for time-domain wavefields is a major challenge.

Sparse frequency domain or time-domain data strategies can mitigate storage issues.

Abstract

Full waveform inversion (FWI) updates the subsurface model from an initial model by comparing observed and synthetic seismograms. Due to high nonlinearity, FWI is easy to be trapped into local minima. Extended domain FWI, including wavefield reconstruction inversion (WRI) and extended source waveform inversion (ESI) are attractive options to mitigate this issue. This paper makes an in-depth analysis for FWI in the extended domain, identifying key challenges and searching for potential remedies towards practical applications. WRI and ESI are formulated within the same mathematical framework using Lagrangian-based adjoint-state method with a special focus on time-domain formulation using extended sources, while putting connections between classical FWI, WRI and ESI: both WRI and ESI can be viewed as weighted versions of classic FWI. Due to symmetric positive definite Hessian, the conjugate gradient is explored to efficiently solve the normal equation in a matrix free manner, while both time and frequency domain wave equation solvers are feasible. This study finds that the most significant challenge comes from the huge storage demand to store time-domain wavefields through iterations. To resolve this challenge, two possible workaround strategies can be considered, i.e., by extracting sparse frequencial wavefields or by considering time-domain data instead of wavefields for reducing such challenge. We suggest that these options should be explored more intensively for tractable workflows.