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.

中文翻译：

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2019年加利福尼亚里奇克雷斯特地震序列期间的弹性库仑速率和状态余震预测的预测技能

业务地震预报协议通常使用统计模型，因为它们公认易于实施且具有鲁棒性，可用于描述触发地震活动的短期时空模式。但是，基于物理的余震预报的最新进展表明，考虑到断层和应力场的非均质性，其性能可与标准统计对应物相媲美，并具有显着提高的预测技能。在这里，我们在2019年里奇克莱斯特（加利福尼亚）地震序列的第一个月中执行了伪预期的预测实验。我们开发了七个库仑速率和状态模型，将静态应力变化估计值与速率和状态摩擦定律表示的连续力学耦合在一起。我们的模型参数化支持逐渐增加的复杂度；我们从简化滑模分布和空间均匀的接收器故障的初步模型实施开始，发展到具有优化的故障本构参数，有限断层滑模模型，二次触发效应以及由先前破裂引起的空间异构平面的增强模型。加利福尼亚南部的数据丰富的环境使我们能够测试在不断发生的地震序列中合并近实时收集的数据是否能增强我们的预测能力。我们通过在地震可预测性研究合作机构内使用的统计测试评估预报的绝对和相对性能，并在短期（地震发生后24小时）内将其技能与标准基准流行病余震序列（ETAS）模型进行比较。两次Ridgecrest主震）和中期（一个月）。基于应力的预测预计整个断层带附近地区的地震波速将增加，而加洛克地区中部的地震活动率将提高。我们的比较模型评估不仅支持断层异质性和二次触发效应是基于物理的预测背后最关键的成功要素，但同时也强调了模型更新的重要性，该模型包含近实时的余震数据，其性能要比标准ETAS更好。通过调查Ridgecrest近源区先前存在的正常断层的局部关闭，我们探索了结果背后的物理基础。