基于机器学习的相速度面波多模态反演的不确定性量化

A. Yablokov, A. Serdyukov
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引用次数: 0

摘要

本文研究了多通道表面波分析方法反问题求解的不确定性量化——基于频率依赖的相速度曲线反演。不确定性估计方法是基于蒙特卡罗采样策略和多层全连接人工神经网络,通过相速度表面波的值来近似剪切波速与层厚的非线性关系。数据中的频率相关噪声和逆算子的误差被投影到反问题解中。以综合数据处理为例,比较了单峰和多峰反演的结果。实验结果表明,利用机器学习方法可以快速准确地估计重建速度模型参数的后验概率密度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncertainty quantification of phase velocity surface waves multy-modal inversion using machine learning
The paper is devoted to uncertainty quantification of the inverse problem solution of the multichannel analysis of surface waves method - the inversion of the curves of the phase velocity via frequency dependence. The uncertainty estimation approach is based on the Monte Carlo sampling strategy and a multilayer fully connected artificial neural network to approximate nonlinear dependence of shear wave velocity and layers thickness via values of phase velocity surface waves. Frequency-dependent noise in the data and errors of the inverse operator are projected onto the inverse problem solution. The results of unimodal and multimodal inversion are compared on the example of synthetic data processing. The experimental results show that using of machine learning approaches makes it possible to quickly and accurately estimate the posterior probability density of the reconstructed velocity model parameters.
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