论文标题
使用修补的绿色功能技术基于广义的rényi熵的目标全波倒置
Target-oriented full-waveform inversion based on generalized Rényi entropy using patched Green's function techniques
论文作者
论文摘要
数据分析中物理参数的估计是许多复杂系统描述和建模的关键点。基于Rényi$α$ -Gaussian分布和修补的Green功能（PGF）技术，我们使用基于波浪方程的方法论为全波倒置（FWI）提出了一个可靠的数据倒置框架。我们通过考虑两种截然不同的p波速度模型来展示提案的有效性，其中第一个速度在安哥拉的宽扎盆地中受到启发，第二个是在巴西前萨尔特山脉领域具有巨大经济利益的第二个地区。我们通过缩写$α$ -PGF-FWI称我们的建议。结果表明，$α$ -PGF-FWI在限制$α\ rightarrow 2/3 $中，对添加剂高斯噪声和非高斯噪声具有强大的功能，为$α$rényientropic Index。
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.
