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

The estimation of physical parameters from data analysis is a crucial point for the description and modeling of many complex systems. Based on Rényi $α$-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 $α$-PGF-FWI. The results reveal that the $α$-PGF-FWI is robust against additive Gaussian noise and non-Gaussian noise with outliers in the limit $α\rightarrow 2/3$, being $α$ the Rényi entropic index.

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