Piras, D;
Spurio Mancini, A;
Ferreira, AMG;
Joachimi, B;
Hobson, MP;
(2023)
Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism.
Geophysical Journal International
, 232
(2)
pp. 1219-1235.
10.1093/gji/ggac385.
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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 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 104 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.
| Type: | Article |
|---|---|
| Title: | Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/gji/ggac385 |
| Publisher version: | http://dx.doi.org/10.1093/gji/ggac385 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Physical Sciences, Geochemistry & Geophysics, Machine learning, Statistical methods, Induced seismicity, Waveform inversion, WAVE-FORM INVERSION, COSMOLOGICAL PARAMETER-ESTIMATION, SEISMIC SOURCE INVERSION, MOMENT TENSOR INVERSION, FREQUENCY-DOMAIN, EARTHQUAKE LOCATION, FOCAL MECHANISMS, VELOCITY MODEL, PART 1, PICKING |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Earth Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159763 |
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