Target-oriented full-waveform inversion based on generalized R\'enyi entropy using patched Green's function techniques

TL;DR

This paper introduces a robust full-waveform inversion method based on Renyi entropy and patched Green's function techniques, demonstrating improved noise resilience in complex geophysical models.

Contribution

It presents a novel alpha-PGF-FWI framework that enhances robustness against noise in seismic data inversion using Renyi entropy and PGF methods.

Findings

Effective in noisy environments with Gaussian and non-Gaussian noise

Demonstrated on realistic P-wave velocity models from Angola and Brazil

Robustness increases as alpha approaches 2/3

Abstract

The estimation of physical parameters from data analysis is a crucial point for the description and modeling of many complex systems. Based on R\'enyi $\alpha$-Gaussian distribution and patched Green's function (PGF) techniques, we propose a robust framework for data inversion using a wave-equation based methodology named full-waveform inversion (FWI). We show the effectiveness of our proposal by considering two distinct realistic P-wave velocity models, in which the first one is inspired in the Kwanza Basin in Angola and the second in a region of great economic interest in the Brazilian pre-salt field. We call our proposal by the abbreviation $\alpha$-PGF-FWI. The results reveal that the $\alpha$-PGF-FWI is robust against additive Gaussian noise and non-Gaussian noise with outliers in the limit $\alpha \rightarrow 2/3$, being $\alpha$ the R\'enyi entropic index.