Abstract
ABSTRACTThe 2019 Ridgecrest sequence provides the first opportunity to evaluate Uniform California Earthquake Rupture Forecast v.3 with epidemic-type aftershock sequences (UCERF3-ETAS) in a pseudoprospective sense. For comparison, we include a version of the model without explicit faults more closely mimicking traditional ETAS models (UCERF3-NoFaults). We evaluate the forecasts with new metrics developed within the Collaboratory for the Study of Earthquake Predictability (CSEP). The metrics consider synthetic catalogs simulated by the models rather than synoptic probability maps, thereby relaxing the Poisson assumption of previous CSEP tests. Our approach compares statistics from the synthetic catalogs directly against observations, providing a flexible approach that can account for dependencies and uncertainties encoded in the models. We find that, to the first order, both UCERF3-ETAS and UCERF3-NoFaults approximately capture the spatiotemporal evolution of the Ridgecrest sequence, adding to the growing body of evidence that ETAS models can be informative forecasting tools. However, we also find that both models mildly overpredict the seismicity rate, on average, aggregated over the evaluation period. More severe testing indicates the overpredictions occur too often for observations to be statistically indistinguishable from the model. Magnitude tests indicate that the models do not include enough variability in forecasted magnitude-number distributions to match the data. Spatial tests highlight discrepancies between the forecasts and observations, but the greatest differences between the two models appear when aftershocks occur on modeled UCERF3-ETAS faults. Therefore, any predictability associated with embedding earthquake triggering on the (modeled) fault network may only crystalize during the presumably rare sequences with aftershocks on these faults. Accounting for uncertainty in the model parameters could improve test results during future experiments.
Highlights
The former model is explained in detail by Field et al (2017b), so we summarize the important differences between U3ETAS and NoFaults here
Before we share the results of the quantitative evaluations of the forecasts, we show how differences between U3ETAS and NoFaults manifest in individual synthetic catalogs
We find that U3ETAS tends to show larger spatial test statistics, and quantile scores, when observed events occur along modeled U3ETAS faults
Summary
A fundamental question in seismology is: What is the probability of observing an earthquake within some predefined space-time-magnitude region? Earthquake forecasting models try to answer this question by incorporating ideas of varying complexity about the earthquake process, including both empirical statistical relations, such as the Omori-Utsu and Gutenberg-Richter relations (Gutenberg and Richter, 1944; Utsu, 1961), and physical modeling, such as Coulomb stress calculations (Oppenheimer et al, 1988; King et al, 1994; Stein, 1999; Woessner et al, 2011; Cattania et al, 2018). CSEP has been using likelihood-based consistency tests (Schorlemmer et al, 2007; Zechar et al., 2010; Rhoades et al, 2011; Werner et al, 2011) that are rooted in the concepts that 1) earthquakes occur in space-time-magnitude bins independently, 2) earthquakes follow the Poisson distribution in each bin, and 3) modelers provide the 'true' parameter of the distribution in each bin. CSEP required that modelers provide forecasts giving the expected number of earthquakes in discrete space-time-magnitude bins. This pragmatic simplification allows multiple types of models, including those without explicit likelihood functions, to participate in the experiments
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