DeepAI AI Chat
Log In Sign Up

Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events

01/12/2021
by   Davide Piras, et al.
UCL
5

Bayesian inference applied to microseismic activity monitoring allows for principled estimation of the coordinates of microseismic events from recorded seismograms, and their associated uncertainties. However, forward modelling of these microseismic events, 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 improve on previous work, which considered only sources with isotropic moment tensor. We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows for the complete and fast retrieval of the event coordinates for 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. This work lays the foundations for the efficient localisation and characterisation of any recorded seismogram, thus helping to quantify human impact on seismic activity and mitigate seismic hazard.

READ FULL TEXT

page 3

page 9

07/12/2022

Scalable Bayesian Inference for Detection and Deblending in Astronomical Images

We present a new probabilistic method for detecting, deblending, and cat...
12/12/2018

Surrogate-assisted Bayesian inversion for landscape and basin evolution models

The complex and computationally expensive features of the forward landsc...
11/01/2021

Swift sky localization of gravitational waves using deep learning seeded importance sampling

Fast, highly accurate, and reliable inference of the sky origin of gravi...
09/19/2022

Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference

Although the no-u-turn sampler (NUTS) is a widely adopted method for per...
07/08/2019

Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data

Datasets in engineering applications are often limited and contaminated,...
11/06/2017

Fast amortized inference of neural activity from calcium imaging data with variational autoencoders

Calcium imaging permits optical measurement of neural activity. Since in...
11/16/2022

Adapting to noise distribution shifts in flow-based gravitational-wave inference

Deep learning techniques for gravitational-wave parameter estimation hav...