微地震事件定位和震源机制的快速机器学习辅助贝叶斯后验推理
Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism
摘要：
应用贝叶斯推断进行微地震活动监测，可以从记录的地震图中准确定位微地震事件，并估计相关的不确定性。然而，这些微震事件的正演建模，这是必要的进行贝叶斯源反演，可以是昂贵的计算资源。一个可行的解决方案是基于机器学习技术训练代理模型，以模拟前向模型，从而加速贝叶斯推断。在本文中，我们大大提高了以前的工作，只考虑各向同性矩张量的源。我们训练机器学习算法的功率谱的压力波记录，并表明，训练的仿真器允许完整和快速的事件位置的$\textit{any}$源机制。此外，我们表明，我们的方法是计算成本低，因为它可以在不到1小时的商业笔记本电脑上运行，同时产生准确的结果，使用不到$10^4$训练地震图。我们还演示了如何训练仿真器可以用来识别源机制，通过贝叶斯证据的估计。最后，我们证明了我们的方法是鲁棒的实际噪声测量现场数据。这项工作奠定了基础，为有效，准确的未来联合确定事件的位置和矩张量，以及相关的不确定性，这是最终的关键，准确地描述人为和自然地震，并加强定量地震危险性评估。
英文摘要：
Bayesian inference applied to microseismic activity monitoring allows the accurate location of microseismic events from recorded seismograms and the estimation of the associated uncertainties. However, the forward modelling of these microseismic events, which is necessary to perform Bayesian source inversion, can be prohibitively expensive in terms of computational resources. A viable solution is to train a surrogate model based on machine learning techniques, to emulate the forward model and thus accelerate Bayesian inference. In this paper, we substantially enhance previous work, which considered only sources with isotropic moment tensors. We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows complete and fast event locations for $\textit{any}$ source mechanism. Moreover, we show that our approach is computationally inexpensive, as it can be run in less than 1 hour on a commercial laptop, while yielding accurate results using less than $10^4$ training seismograms. We additionally demonstrate how the trained emulators can be used to identify the source mechanism through the estimation of the Bayesian evidence. Finally, we demonstrate that our approach is robust to real noise as measured in field data. This work lays the foundations for efficient, accurate future joint determinations of event location and moment tensor, and associated uncertainties, which are ultimately key for accurately characterising human-induced and natural earthquakes, and for enhanced quantitative seismic hazard assessments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
