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Prospective Evaluation of Multiplicative Hybrid Earthquake Forecast Models for California

Session: Constructing and Testing Regional and Global Earthquake Forecasts II

Type: Oral

Date: 4/21/2021

Presentation Time: 10:45 AM Pacific

Description: 

The Regional Earthquake Likelihood Models (RELM) group designed a joint forecasting experiment, with associated models, data and tests to evaluate earthquake predictability in California. After five years of prospective evaluation, the RELM experiment found that the smoothed seismicity (HKJ) model by Helmstetter et al. (2007) was the most informative. The diversity of competing forecast hypotheses in RELM was suitable for combining multiple models that could provide more informative earthquake forecasts than HKJ. Thus, Rhoades et al. (2014) created multiplicative hybrid models that involve the HKJ model as a baseline and one or more conjugate models. According to retrospective analyses, some hybrid models showed significant information gains over the HKJ forecast. Here, we assess in a prospective setting the predictive skills of 16 hybrids and 6 original RELM forecasts, using a suite of tests of the Collaboratory for the Study of Earthquake Predictability (CSEP). The evaluation dataset contains 40 M≥4.95 events recorded within the California CSEP-testing region from 1 January 2011 to 31 December 2020, including the 2016 Mw 5.6, 5.6, and 5.5 Hawthorne earthquake swarm, and the Mw 6.4 foreshock and Mw 7.1 mainshock from the 2019 Ridgecrest sequence. We evaluate the consistency between the observed and the expected number, spatial, likelihood and magnitude distributions of earthquakes, and compare the performance of each forecast to that of HKJ. Our prospective test results show that none of the hybrid models are significantly more informative than the HKJ baseline forecast. These results appear to be mainly due to the occurrence of the 2016 Hawthorne and 2019 Ridgecrest earthquake sequences, as these clusters of seismicity are exceptionally unlikely in all models and are insufficiently captured by the Poisson distribution that the likelihood functions of tests assume. Currently, we are examining alternative likelihood functions to better understand the discrepancies between prospective and retrospective test results for multiplicative hybrid forecasts.

Presenting Author: Jose A. Bayona

Student Presenter: No

Authors