The 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.

The Collaboratory for the Study of Earthquake Predictability (CSEP) is an open and global community whose mission is to accelerate earthquake predictability research through rigorous testing of probabilistic earthquake forecast models and prediction algorithms. pyCSEP supports this mission by providing open-source implementations of useful tools for evaluating earthquake forecasts. pyCSEP is a Python package that contains the following modules: (1) earthquake catalog access and processing, (2) representations of probabilistic earthquake forecasts, (3) statistical tests for evaluating earthquake forecasts, and (4) visualization routines and various other utilities. Most significantly, pyCSEP contains several statistical tests needed to evaluate earthquake forecasts, which can be forecasts expressed as expected earthquake rates in space–magnitude bins or specified as large sets of simulated catalogs (which includes candidate models for governmental operational earthquake forecasting). To showcase how pyCSEP can be used to evaluate earthquake forecasts, we have provided a reproducibility package that contains all the components required to re-create the figures published in this article. We recommend that interested readers work through the reproducibility package alongside this article. By providing useful tools to earthquake forecast modelers and facilitating an open-source software community, we hope to broaden the impact of the CSEP and further promote earthquake forecasting research.

Spontaneous rupture simulations along geometrically rough faults have been shown to produce realistic far‐field spectra and comparable fits with ground motion metrics such as spectral accelerations and peak motions from Ground Motion Prediction Equations (GMPEs), but they are too computationally demanding for use with physics‐based probabilistic seismic hazard analysis efforts. Here, we present our implementation of a kinematic rupture generator that matches the characteristics of, at least in a statistical sense, rough‐fault spontaneous rupture models. To this end, we analyze ~100 dynamic rupture simulations on strike‐slip faults with M_w ranging from 6.4 to 7.2. We find that our dynamic simulations follow empirical scaling relationships for strike‐slip events and provide source spectra comparable to a source model with ω⁻² decay. To define our kinematic source model, we use a regularized Yoffe function parameterized in terms of slip, peak‐time, rise‐time, and rupture initiation time. These parameters are defined through empirical relationships with random fields whose one‐ and two‐point statistics are derived from the dynamic rupture simulations. Our rupture generator reproduces Next Generation Attenuation (NGA) West2 GMPE medians and intraevent standard deviations of spectral accelerations with periods as short as 0.2 s for ensembles of ground motion simulations. Our rupture generator produces kinematic source models for M6.4–7.2 strike‐slip scenarios that can be used in broadband physics‐based probabilistic seismic hazard efforts or to supplement data in areas of limited observations for the development of future GMPEs.

We simulate 0–2.5 Hz deterministic wave propagation in 3D velocity models for the 2008 Chino Hills, CA, earthquake using a finite-fault source model and frequency-dependent anelastic attenuation. Small-scale heterogeneities are modeled as 3D random fields defined using an elliptically anisotropic von Kármán autocorrelation function with its parameters constrained using Los Angeles basin borehole data. We superimpose the heterogeneity models on a leading deterministic Community Velocity Model (CVM) of southern California. We find that models of velocity and density perturbations can have significant effects on the wavefield at frequencies as low as 0.5 Hz, with ensemble median values of various ground motion metrics varying up to ± 50 per cent compared to those computed using the deterministic CVM only. In addition, we show that frequency-independent values of the shear-wave quality factor (Qs₀) parameterized as Qs₀ = 150Vs (Vs in km/s) provides the best agreement with data when assuming the published moment magnitude (M_w) of 5.4 (M₀ = 1.6 × 10¹⁷ Nm) for the finite-fault source model. This model for Qs₀ trades off with Qs₀ = 100Vs assuming M_w = 5.5 (M₀ = 2.2 × 10¹⁷ Nm), which represents an upper bound of the M_w estimates for this event. We find the addition of small-scale heterogeneities provides limited overall improvement to the misfit between simulations and data for the considered ground motion metrics, because the primary sources of misfit originate from the deterministic CVM and/or the finite-fault source description.

The Collaboratory for the Study of Earthquake Predictability (CSEP) is an international effort to evaluate earthquake forecast models prospectively. In CSEP, one way to express earthquake forecasts is through a grid-based format: the expected number of earthquake occurrences within 0.1^○ × 0.1^○ spatial cells. The spatial distribution of seismicity is thereby evaluated using the Spatial test (S-test). The high-resolution grid combined with sparse and inhomogeneous earthquake distributions leads to a huge number of cells causing disparity in the number of cells and the number of earthquakes to evaluate the forecasts, thereby affecting the statistical power of the S-test. In order to explore this issue, we conducted a global earthquake forecast experiment, in which we computed the power of the S-test to reject a spatially non-informative uniform forecast model. The S-test loses its power to reject the non-informative model when the spatial resolution is so high that every earthquake of the observed catalog tends to get a separate cell. Upon analyzing the statistical power of the S-test, we found, as expected, that the statistical power of the S-test depends upon the number of earthquakes available for testing, e. g. with the conventional high-resolution grid for the global region, we would need more than 32000 earthquakes in the observed catalog for powerful testing, which would require approximately 300 years to record M ≥ 5.95. The other factor affecting the power is more interesting and new; it is related to the spatial grid representation of the forecast model. Aggregating forecasts on multi-resolution grids can significantly increase the statistical power of the S-test. Using the recently introduced Quadtree to generate data-based multi-resolution grids, we show that the S-test reaches its maximum power in this case already for as few as 8 earthquakes in the test period. Thus we recommend for future CSEP experiments the use of Quadtree-based multi-resolution grids, where available data determine the resolution.