Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism. (arXiv:2101.04724v2 [physics.geo-ph] UPDATED)

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 …

arxiv bayesian event inference location machine physics posterior