HypoSVI：基于Stein变分推理和物理信息神经网络的震源反演
HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks
摘要：
本文介绍了一种基于Stein变分推理的概率震源反演方法。我们的方法使用了一个可微的前向模型的形式的物理通知神经网络，我们训练，以解决Eikonal方程。这允许通过针对核化的Stein差异迭代地优化粒子的集合来快速近似后验。我们表明，该方法是装备精良，以处理高度多峰后验分布，这是常见的震源逆问题。一套实验进行检查的各种超参数的影响。一旦训练，该方法是有效的任何地震网络的几何形状内的研究领域，而不需要建立旅行时间表。我们证明了计算需求随着差分次数的增加而有效地扩展，使其成为分布式声学传感等大N传感技术的理想选择。在这份手稿中概述的技术有相当大的影响，不仅仅是射线追踪程序，与工作流程适用于其他领域的计算昂贵的反演程序，如全波形反演。
英文摘要：
We introduce a scheme for probabilistic hypocenter inversion with Stein variational inference. Our approach uses a differentiable forward model in the form of a physics informed neural network, which we train to solve the Eikonal equation. This allows for rapid approximation of the posterior by iteratively optimizing a collection of particles against a kernelized Stein discrepancy. We show that the method is well-equipped to handle highly multimodal posterior distributions, which are common in hypocentral inverse problems. A suite of experiments is performed to examine the influence of the various hyperparameters. Once trained, the method is valid for any seismic network geometry within the study area without the need to build travel time tables. We show that the computational demands scale efficiently with the number of differential times, making it ideal for large-N sensing technologies like Distributed Acoustic Sensing. The techniques outlined in this manuscript have considerable implications beyond just ray-tracing procedures, with the work flow applicable to other fields with computationally expensive inversion procedures such as full waveform inversion.
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