In passive seismic and microseismic monitoring, identifying and characterizing events in a strong noisy
background is a challenging task. Most of the established methods for geophysical inversion are likely
to yield many false event detections. The most advanced of these schemes require thousands of
computationally demanding forward ...
In passive seismic and microseismic monitoring, identifying and characterizing events in a strong noisy
background is a challenging task. Most of the established methods for geophysical inversion are likely
to yield many false event detections. The most advanced of these schemes require thousands of
computationally demanding forward elastic-wave propagation simulations. Here we train and use an
ensemble of Gaussian process (GP) surrogate meta-models, or proxy emulators, to accelerate the
generation of accurate template seismograms from random microseismic event locations. In the
presence of multiple microseismic events occurring at different spatial locations with arbitrary
amplitude and origin time, and in the presence of noise, an inference algorithm needs to navigate an
objective function or likelihood landscape of highly complex shape, perhaps with multiple modes and
narrow curving degeneracies. This is a challenging computational task even for state-of-the-art
Bayesian sampling algorithms. In this paper, we propose a novel method for detecting multiple
microseismic events in a strong noise background using Bayesian inference, in particular, the
Multimodal Nested Sampling (MultiNest) algorithm. The method not only provides the posterior
samples for the 5D spatio-temporal-amplitude inference for the real microseismic events, by inverting
the seismic traces in multiple surface receivers, but also computes the Bayesian evidence or the marginal
likelihood that permits hypothesis testing for discriminating true vs. false event detection.
