We use a large instrumental dataset from the 2019 Ridgecrest earthquake sequence (Rekoske et al., 2019, 2020) to examine repeatable source‐, path‐, and site‐specific ground motions. A mixed‐effects analysis is used to partition total residuals relative to the Boore et al. (2014; hereafter, BSSA14) ground‐motion model. We calculate the Arias intensity stress drop for the earthquakes and find strong correlation with our event terms, indicating that they are consistent with source processes. We look for physically meaningful trends in the partitioned residuals and test the ability of BSSA14 to capture the behavior we observe in the data.We find that BSSA14 is a good match to the median observations for M>4⁠. However, we find bias for individual events, especially those with small magnitude and hypocentral depth≥7 km⁠, for which peak ground acceleration is underpredicted by a factor of 2.5. Although the site amplification term captures the median site response when all sites are considered together, it does not capture variations at individual stations across a range of site conditions. We find strong basin amplification in the Los Angeles, Ventura, and San Gabriel basins. We find weak amplification in the San Bernardino basin, which is contrary to simulation‐based findings showing a channeling effect from an event with a north–south azimuth. This and an additional set of ground motions from earthquakes southwest of Los Angeles suggest that there is an azimuth‐dependent southern California basin response related to the orientation of regional structures when ground motion from waves traveling south–north are compared with those in the east–west direction. These findings exhibit the power of large, spatially dense ground‐motion datasets and make clear that nonergodic models are a way to reduce bias and uncertainty in ground‐motion estimation for applications like the U.S. Geological Survey National Seismic Hazard Model and the ShakeAlert earthquake early warning System.

中文翻译：

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2019年M 7.1里奇克里斯特地震序列的可重复源，路径和场地影响

我们使用了2019年Ridgecrest地震序列中的大型仪器数据集（Rekoske等人，2019，2020），研究了可重复的源，路径和特定地点的地震动。相对于Boore等人，使用混合效应分析来划分总残差。（2014年；此后，BSSA14）地面运动模型。我们计算了地震的Arias强度应力降，并发现与我们的事件项密切相关，表明它们与震源过程一致。我们在分区残差中寻找有意义的物理趋势，并测试BSSA14捕获数据中观察到的行为的能力。我们发现BSSA14与M>4⁠的中值观察值非常匹配。但是，我们发现个别事件有偏见，尤其是小震级和震中深度≥7km⁠的事件，其峰值地面加速度被低估了2.5倍。尽管将所有站点同时考虑时，站点放大项可以捕获中位站点响应，但它不能捕获整个站点条件范围内单个站点的变化。我们在洛杉矶，维特纳和圣加布里埃尔盆地发现了强大的盆地放大作用。我们在圣贝纳迪诺盆地发现了微弱的放大作用，这与基于模拟的发现相反，该发现显示了南北方位角事件带来的通道效应。洛杉矶西南地震的这一和另外一组地震动表明，将南北向传播的地震动与东向传播的地震动进行比较时，存在与方位角有关的南加州盆地响应，与区域结构的方向有关。西方向。