Poster #168, Fault and Rupture Mechanics (FARM)

Multi-fault rupture plausibility inferences from a deterministic earthquake simulator

Kevin R. Milner, Bruce E. Shaw, & Thomas H. Jordan
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Poster Presentation

2020 SCEC Annual Meeting, Poster #168, SCEC Contribution #10630 VIEW PDF
Enumeration of the set of all plausible significant earthquakes (i.e., those likely to damage the built environment or result in casualties) in a region is a key step when building an earthquake rupture forecast (ERF) for probabilistic seismic hazard analysis (PSHA). The Third Uniform California Earthquake Rupture Forecast (UCERF3) was the first comprehensive ERF to include multi-fault ruptures, consistent with many examples in nature. The set of ruptures included in UCERF3 was determined through a set of binary filters which aimed to reduce the near-infinite fault section combinations to a tractable set of physically plausible ruptures. These plausibility criteria used hard cutoff values an...d were largely empirical, e.g., maximum jump distance (5 km) and maximum fault-to-fault azimuth change (60 degrees), and a physical constraint was included which assessed the Coulomb compatibility of fault section jumps (though it only evaluated ruptures at each jumping point, not their total physical consistency).

We test the UCERF3 multi-fault rupture plausibility criteria against physically-consistent synthetic seismicity catalogs generated with the Rate-State Earthquake Simulator (RSQSim) on the UCERF3 fault system. Previous studies have shown agreement between RSQSim and UCERF3 when carried through to PSHA calculations; here we examine individual ruptures in more detail. We find that RSQSim catalogs produce multi-fault ruptures at a similar rate to UCERF3, and most (>75% of M≥6.5 ruptures) pass all UCERF3 plausibility criteria. We also identify a number of plausible fault jumps which occur in RSQSim catalogs but are not allowed under the UCERF3 rules, and propose modifications to the plausibility criteria for the next UCERF model to incorporate inferences from RSQSim.