Improved depth imaging by constrained full-waveform inversion

TL;DR

This paper introduces a constrained full-waveform inversion method using total-variation regularization to improve depth imaging by producing sharper, more accurate subsurface models, especially at greater depths.

Contribution

It presents a novel constrained FWI formulation with a computationally efficient solution that enhances model sharpness and reflector positioning in seismic imaging.

Findings

Enhanced sharpness of inverted models

Improved reflector positioning at depth

Effective noise handling in synthetic datasets

Abstract

We propose a formulation of full-wavefield inversion (FWI) as a constrained optimization problem, and describe a computationally efficient technique for solving constrained full-wavefield inversion (CFWI). The technique is based on using a total-variation regularization method, with the regularization weighted in favor of constraining deeper subsurface model sections. The method helps to promote "edge-preserving" blocky model inversion where fitting the seismic data alone fails to adequately constrain the model. The method is demonstrated on synthetic datasets with added noise, and is shown to enhance the sharpness of the inverted model and correctly reposition mispositioned reflectors by better constraining the velocity model at depth.