Deconvolutional double-difference misfit measurements and the application for full-waveform inversion

TL;DR

This paper introduces a novel deconvolutional double-difference misfit function for full-waveform inversion, effectively mitigating wavelet inaccuracy and improving convergence from rough initial models.

Contribution

It proposes a new misfit measurement using deconvolution, enhancing robustness of full-waveform inversion against wavelet inaccuracies.

Findings

Inversion with the new misfit is resilient to wavelet inaccuracy.

The method converges to plausible local minima from rough initial models.

Numerical examples validate the robustness and effectiveness of the approach.

Abstract

It is challenging for full-waveform inversion to determine geologically informative models from field data. An inaccurate wavelet can make it more complicated. We develop a novel misfit function, entitled deconvolutional double-difference misfit measurement to cancel the influence of wavelet inaccuracy on inversion results. Unlike the popular double-difference misfit measurement in which the first difference is evaluated by cross-correlation, the proposed one employs deconvolution to do this step. Numerical examples demonstrate that full-waveform inversion with the new misfit function is resilient to the wavelet inaccuracy. It can also converge to plausible local minima even from rough initial models.