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

TL;DR

This paper introduces a machine learning-based emulator that accelerates Bayesian microseismic event location and source mechanism estimation, achieving high accuracy and robustness with minimal computational resources.

Contribution

It extends previous models by enabling fast, comprehensive Bayesian inference for any source mechanism using a trained emulator on pressure wave spectra.

Findings

Emulator allows rapid event location for any source mechanism.

The approach is computationally inexpensive, running in less than 1 hour on a laptop.

The method is robust to real field noise.

Abstract

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.