The Predictive Skills of Elastic Coulomb Rate-and-State Aftershock Forecasts during the 2019 Ridgecrest, California, Earthquake Sequence

Abstract

Operational earthquake forecasting protocols commonly use statistical models for their recognized ease of implementation and robustness in describing the short-term spatiotemporal patterns of triggered seismicity. However, recent advances on physics-based aftershock forecasting reveal comparable performance to the standard statistical counterparts with significantly improved predictive skills when fault and stress-field heterogeneities are considered. Here, we perform a pseudoprospective forecasting experiment during the first month of the 2019 Ridgecrest (California) earthquake sequence. We develop seven Coulomb rate-and-state models that couple static stress-change estimates with continuum mechanics expressed by the rate-and-state friction laws. Our model parameterization supports a gradually increasing complexity; we start from a preliminary model implementation with simplified slip distributions and spatially homogeneous receiver faults to reach an enhanced one featuring optimized fault constitutive parameters, finite-fault slip models, secondary triggering effects, and spatially heterogenous planes informed by pre-existing ruptures. The data-rich environment of southern California allows us to test whether incorporating data collected in near-real time during an unfolding earthquake sequence boosts our predictive power. We assess the absolute and relative performance of the forecasts by means of statistical tests used within the Collaboratory for the Study of Earthquake Predictability and compare their skills against a standard benchmark epidemic-type aftershock sequence (ETAS) model for the short (24 hr after the two Ridgecrest mainshocks) and intermediate terms (one month). Stress-based forecasts expect heightened rates along the whole near-fault region and increased expected seismicity rates in central Garlock fault. Our comparative model evaluation not only supports that faulting heterogeneities coupled with secondary triggering effects are the most critical success components behind physics-based forecasts, but also underlines the importance of model updates incorporating near-real-time available aftershock data reaching better performance than standard ETAS. We explore the physical basis behind our results by investigating the localized shut down of pre-existing normal faults in the Ridgecrest near-source area.

Spatial Clustering of Aftershocks Impacts the Performance of Physics‐Based Earthquake Forecasting Models

“…While the background conditions and their impacts on triggering may be incompletely known and impractical to incorporate into modeling, the performance of physical models can be improved through the incorporation of ETAS-like features in spatial forecasts. Mancini et al ( , 2020 have shown, for example, that modeling secondary triggering from aftershocks in CRS models can improve their forecast performance relative to CRS models with only the mainshock triggering and can achieve performance comparable to ETAS.…”

“…Some CRS models have already been developed that include spatial variations in the modeled receiver fault orientation, which improves their performance (e.g. Mancini et al., 2019, 2020).…”

“…Mancini et al. (2019, 2020) have shown, for example, that modeling secondary triggering from aftershocks in CRS models can improve their forecast performance relative to CRS models with only the mainshock triggering and can achieve performance comparable to ETAS.…”

“…While the background conditions and their impacts on triggering may be incompletely known and impractical to incorporate into modeling, the performance of physical models can be improved through the incorporation of ETAS-like features in spatial forecasts. Mancini et al ( , 2020…”

“…Hainzl et al., 2009; Woessner et al., 2011) or the spatial variability in the modeled receiver fault orientations (e.g. Cattania et al., 2014, 2018; Mancini et al., 2019, 2020). These models allow for the possibility of aftershocks in more locations and reduce or eliminate the modeled stress shadows.…”