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

Abstract

The estimation of physical parameters from data analysis is a crucial point for the description and modeling of many complex systems. Based on Rényi $\alpha$-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 $\alpha \to 2/3$, being $\alpha$ the Rényi entropic index.

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• Universality in the geometric dependence of Renyi entropy
• Maximum L$q$-likelihood estimation
• Electronic transport in disordered graphene antidot lattice devices
• Patched Green's function techniques for two dimensional systems: Electronic behaviour of bubbles and perforations in graphene
• Full Waveform Inversion of Solar Interior Flows

Cited by in corpus (1)

• Refraction FWI of a circular shot OBN acquisition in the Brazilian pre-salt region