基于广义Rényi熵的目标定向全波反演
Target-oriented full-waveform inversion based on generalized Rényi entropy using patched Green's function techniques
摘要：
从数据分析中估计物理参数是描述和建模许多复杂系统的关键点。基于Rényi $α$ -高斯分布和补丁格林函数（PGF）技术，我们提出了一个强大的数据反演框架，使用波动方程为基础的方法称为全波形反演（FWI）。我们通过考虑两个不同的现实P波速度模型，其中第一个是在安哥拉的宽扎盆地和第二个在巴西盐下油田的巨大经济利益的区域的启发，我们的建议的有效性。我们将我们的提案简称为$α$ -PGF-FWI。结果表明，$α$-PGF-FWI对加性高斯噪声和非高斯噪声具有鲁棒性，且异常值在极限$α\rightarrow 2/3$内，即$α$为Rényi熵指数。
英文摘要：
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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