BibTeX

@article{2404.16590v2,
Author        = {Samuel Stockman and Daniel J. Lawson and Maximilian J. Werner},
Title         = {SB-ETAS: using simulation based inference for scalable, likelihood-free
  inference for the ETAS model of earthquake occurrences},
Eprint        = {2404.16590v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.AP},
Abstract      = {Performing Bayesian inference for the Epidemic-Type Aftershock Sequence
(ETAS) model of earthquakes typically requires MCMC sampling using the
likelihood function or estimating the latent branching structure. These tasks
have computational complexity $O(n^2)$ with the number of earthquakes and
therefore do not scale well with new enhanced catalogs, which can now contain
an order of $10^6$ events. On the other hand, simulation from the ETAS model
can be done more quickly $O(n \log n )$. We present SB-ETAS: simulation-based
inference for the ETAS model. This is an approximate Bayesian method which uses
Sequential Neural Posterior Estimation (SNPE), a machine learning based
algorithm for learning posterior distributions from simulations. SB-ETAS can
successfully approximate ETAS posterior distributions on shorter catalogues
where it is computationally feasible to compare with MCMC sampling.
Furthermore, the scaling of SB-ETAS makes it feasible to fit to very large
earthquake catalogs, such as one for Southern California dating back to 1932.
SB-ETAS can find Bayesian estimates of ETAS parameters for this catalog in less
than 10 hours on a standard laptop, which would have taken over 2 weeks using
MCMC. Looking beyond the standard ETAS model, this simulation based framework
would allow earthquake modellers to define and infer parameters for much more
complex models that have intractable likelihood functions.},
Year          = {2024},
Month         = {Apr},
Url           = {http://arxiv.org/abs/2404.16590v2},
File          = {2404.16590v2.pdf}
}