Target-enclosing inversion using an interferometric objective function

TL;DR

This paper introduces a novel target-enclosing full-waveform inversion method that uses an interferometric objective function based on data from a closed boundary, improving target recovery accuracy and computational efficiency.

Contribution

It proposes a new local FWI framework that minimizes wavefield mismatch via an interferometric objective, requiring only kinematic subsurface knowledge and no prior target model.

Findings

Superiority in target recovery quality.

Reduced computational cost.

Effective with synthetic data.

Abstract

Full waveform inversion is a high-resolution subsurface imaging technique, in which full seismic waveforms are used to infer subsurface physical properties. We present a novel, target-enclosing, full-waveform inversion framework based on an interferometric objective function. This objective function exploits the equivalence between the convolution and correlation representation formulas, using data from a closed boundary around the target area of interest. Because such equivalence is violated when the knowledge of the enclosed medium is incorrect, we propose to minimize the mismatch between the wavefields independently reconstructed by the two representation formulas. The proposed method requires only kinematic knowledge of the subsurface model, specifically the overburden for redatuming, and does not require prior knowledge of the model below the target area. In this sense it is truly local: sensitive only to the medium parameters within the chosen target, with no assumptions about the medium or scattering regime outside the target. We present the theoretical framework and derive the gradient of the new objective function via the adjoint-state method and apply it to a synthetic example with exactly redatumed wavefields. A comparison with FWI of surface data and target-oriented FWI based on the convolution representation theorem only shows the superiority of our method both in terms of the quality of target recovery and reduction in computational cost.