Bayesian ETAS modeling for the Pacific Northwest: Uncovering effects of tectonic regimes, regional differences, and swarms on aftershock parameters
The Pacific Northwest (PNW) of North America has high seismic hazard due to numerous earthquake sources under populated areas. It hosts several tectonic regimes and subregional seismic zones that are hypothesized to have different patterns of earthquake and aftershock occurrence. It is also predisposed to earthquake swarms, which can complicate the statistical modeling of these patterns. We present the first statistical seismicity model of the PNW catalog using the epidemic‐type aftershock sequence (ETAS) framework. We develop a Bayesian inference procedure that provides a stable estimation of both ETAS parameters and their uncertainties for different sets of PNW earthquakes, even those with very sparse catalogs. The Bayesian approach allows us to investigate how parameter estimates change between the intraslab and crustal tectonic regimes, the northern and southern PNW, and when swarms are included and excluded from the catalog. We also utilize our Bayesian framework to calculate parameter estimates under different prior beliefs about PNW seismicity, as well as to propagate catalog measurement errors into ETAS parameter estimates. We discuss the implications of parameter differences across the region for aftershock forecasting for the PNW.
Citation Information
Publication Year | 2025 |
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Title | Bayesian ETAS modeling for the Pacific Northwest: Uncovering effects of tectonic regimes, regional differences, and swarms on aftershock parameters |
DOI | 10.1785/0120240249 |
Authors | Max Schneider, Michael Barall, Peter Guttorp, Jeanne L. Hardebeck, Andrew J. Michael, Morgan T. Page, Nicholas van der Elst |
Publication Type | Article |
Publication Subtype | Journal Article |
Series Title | Bulletin of the Seismological Society of America |
Index ID | 70267725 |
Record Source | USGS Publications Warehouse |
USGS Organization | Earthquake Hazards Program; Earthquake Science Center |