Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism

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 any source mechanism. Moreover, we show that our approach is computationally inexpensive, as it can be run in less than 1 hr 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 characterizing human-induced and natural earthquakes, and for enhanced quantitative seismic hazard assessments.

Automated summary

This paper applies Bayesian inference and machine learning techniques to accurately locate microseismic events and estimate associated uncertainties. By training a surrogate model on the power spectrum of recorded pressure wave, the forward modeling of microseismic events can be emulated and Bayesian inference can be accelerated. The approach is computationally inexpensive and robust to real noise, providing efficient and accurate determination of event location and moment tensor.