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Full-waveform earthquake source inversion using simulation-based inference

AA Saoulis, D Piras, AS Mancini, B Joachimi… - arXiv preprint arXiv …, 2024 - arxiv.org
Astrophysics paper physics.geo-ph Suggest

… 3 SIMULATION-BASED INFERENCE Simulation-based inference has emerged across a variety of the physical sciences as a way of incorporating complex, nonGaussian …

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@article{2410.23238v2,
Author        = {A. A. Saoulis and D. Piras and A. Spurio Mancini and B. Joachimi and A. M. G. Ferreira},
Title         = {Full-waveform earthquake source inversion using simulation-based
  inference},
Eprint        = {2410.23238v2},
DOI           = {10.1093/gji/ggaf112},
ArchivePrefix = {arXiv},
PrimaryClass  = {physics.geo-ph},
Abstract      = {This paper presents a novel framework for full-waveform seismic source
inversion using simulation-based inference (SBI). Traditional probabilistic
approaches often rely on simplifying assumptions about data errors, which we
show can lead to inaccurate uncertainty quantification. SBI addresses this
limitation by building an empirical probabilistic model of the data errors
using machine learning models, known as neural density estimators, which can
then be integrated into the Bayesian inference framework. We apply the SBI
framework to point-source moment tensor inversions as well as joint moment
tensor and time-location inversions. We construct a range of synthetic examples
to explore the quality of the SBI solutions, as well as to compare the SBI
results with standard Gaussian likelihood-based Bayesian inversions. We then
demonstrate that under real seismic noise, common Gaussian likelihood
assumptions for treating full-waveform data yield overconfident posterior
distributions that underestimate the moment tensor component uncertainties by
up to a factor of 3. We contrast this with SBI, which produces well-calibrated
posteriors that generally agree with the true seismic source parameters, and
offers an order-of-magnitude reduction in the number of simulations required to
perform inference compared to standard Monte Carlo techniques. Finally, we
apply our methodology to a pair of moderate magnitude earthquakes in the North
Atlantic. We utilise seismic waveforms recorded by the recent UPFLOW ocean
bottom seismometer array as well as by regional land stations in the Azores,
comparing full moment tensor and source-time location posteriors between SBI
and a Gaussian likelihood approach. We find that our adaptation of SBI can be
directly applied to real earthquake sources to efficiently produce high quality
posterior distributions that significantly improve upon Gaussian likelihood
approaches.},
Year          = {2024},
Month         = {Oct},
Note          = {Geophysical Journal International 241.3 (2025): 1740-1761},
Url           = {http://arxiv.org/abs/2410.23238v2},
File          = {2410.23238v2.pdf}
}

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