Recognizing earthquakes as foreshocks in real time would provide a valuable forecasting capability. In a recent study, Gulia and Wiemer (2019) proposed a traffic‐light system that relies on abrupt changes in b‐values relative to background values. The approach utilizes high‐resolution earthquake catalogs to monitor localized regions around the largest events and distinguish foreshock sequences (reduced b‐values) from aftershock sequences (increased b‐values). The recent well‐recorded earthquake foreshock sequences in Ridgecrest, California, and Maria Antonia, Puerto Rico, provide an opportunity to test the procedure. For Ridgecrest, our b‐value time series indicates an elevated risk of a larger impending earthquake during the Mw 6.4 foreshock sequence and provides an ambiguous identification of the onset of the Mw 7.1 aftershock sequence. However, the exact result depends strongly on expert judgment. Monte Carlo sampling across a range of reasonable decisions most often results in ambiguous warning levels. In the case of the Puerto Rico sequence, we record significant drops in b‐value prior to and following the largest event (⁠Mw 6.4) in the sequence. The b‐value has still not returned to background levels (12 February 2020). The Ridgecrest sequence roughly conforms to expectations; the Puerto Rico sequence will only do so if a larger event occurs in the future with an ensuing b‐value increase. Any real‐time implementation of this approach will require dense instrumentation, consistent (versioned) low completeness catalogs, well‐calibrated maps of regionalized background b‐values, systematic real‐time catalog production, and robust decision making about the event source volumes to analyze.

中文翻译：

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Gulia和Wiemer的两个前躯序列（2019）

实时将地震识别为前震将提供有价值的预测能力。在最近的研究中，Gulia和Wiemer（2019）提出了一种交通灯系统，该系统依赖于b值相对于背景值的突然变化。该方法利用高分辨率地震目录来监视最大事件周围的局部区域，并区分余震序列（降低的b值）和余震序列（增加的b值）。最近在加利福尼亚的里奇克雷斯特和波多黎各的玛丽亚·安托尼亚，记录良好的地震前震序列为测试该过程提供了机会。对于Ridgecrest，我们的b值时间序列表明，在Mw 6.4前震序列中发生大地震的风险更高，并且对Mw 7.1余震序列的开始提供了模糊的标识。然而，确切的结果在很大程度上取决于专家的判断。跨一系列合理决策的蒙特卡洛采样通常会导致含糊的警告级别。在波多黎各序列中，我们记录了序列中最大事件（Mw 6.4）前后b值的显着下降。b值仍未恢复到背景水平（2020年2月12日）。Ridgecrest序列大致符合预期；波多黎各序列只有在将来发生更大的事件并随之增加b值时才会这样做。此方法的任何实时实施都需要密集的仪器，一致的（版本化的）低完整性目录，经过良好校准的区域背景b值图，系统的实时目录生成以及对事件源量进行分析的可靠决策。 。