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Effect of tectonic setting on the fit and performance of a long-range earthquake forecasting model

机译：构造环境对远程地震预报模型拟合和性能的影响

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摘要

The Every Earthquake a Precursor According to Scale (EEPAS) long-range earthquake forecasting model has been shown to be informative in several seismically active regions, including New Zealand, California and Japan. In previous applications of the model, the tectonic setting of earthquakes has been ignored. Here we distinguish crustal, plate interface, and slab earthquakes and apply the model to earthquakes with magnitude M≥4 in the Japan region from 1926 onwards. The target magnitude range is M≥ 6; the fitting period is 1966-1995; and the testing period is 1996-2005. In forecasting major slab earthquakes, it is optimal to use only slab and interface events as precursors. In forecasting major interface events, it is optimal to use only interface events as precursors. In forecasting major crustal events, it is optimal to use only crustal events as precursors. For the smoothed-seismicity component of the EEPAS model, it is optimal to use slab and interface events for earthquakes in the slab, interface events only for earthquakes on the interface, and crustal and interface events for crustal earthquakes. The optimal model parameters indicate that the precursor areas for slab earthquakes are relatively small compared to those for earthquakes in other tectonic categories, and that the precursor times and precursory earthquake magnitudes for crustal earthquakes are relatively large. The optimal models fit the learning data sets better than the raw EEPAS model, with an average information gain per earthquake of about 0.4. The average information gain is similar in the testing period, although it is higher for crustal earthquakes and lower for slab and interface earthquakes than in the learning period. These results show that earthquake interactions are stronger between earthquakes of similar tectonic types and that distinguishing tectonic types improves forecasts by enhancing the depth resolution where tectonic categories of earthquakes are vertically separated. However, when depth resolution is ignored, the model formed by aggregating the optimal forecasts for each tectonic category performs no better than the raw EEPAS model.
机译：在按地震规模划分的各个地震（EEPAS）远程地震预测模型已被证明在包括新西兰，加利福尼亚和日本在内的几个地震活跃地区中具有参考价值。在该模型的先前应用中，地震的构造背景已被忽略。在这里，我们区分了地壳，板块界面和平板地震，并将该模型应用于1926年以来日本地区M≥4级的地震。目标幅度范围为M≥6;拟合期为1966-1995年;测试期为1996-2005。在预测大平板地震时，最好仅使用平板和界面事件作为前兆。在预测主要界面事件时，最好仅使用界面事件作为先驱。在预测重大地壳事件时，最好仅使用地壳事件作为前兆。对于EEPAS模型的平滑地震分量，最好对平板中的地震使用平板和界面事件，仅对界面上的地震使用界面事件，对于地壳地震使用地壳和界面事件。最优模型参数表明，与其他构造类别的地震相比，平板地震的前兆面积较小，地壳地震的前兆时间和前震震级较大。最佳模型比原始EEPAS模型更适合学习数据集，每次地震的平均信息增益约为0.4。测试期间的平均信息增益相似，尽管与学习期相比，地壳地震的平均信息增益更高，平板地震和界面地震的平均信息增益更低。这些结果表明，在类似构造类型的地震之间地震相互作用更强，并且通过增强垂直构造地震类别的深度分辨率，区分构造类型可以改善预报。但是，如果忽略深度分辨率，则通过汇总每个构造类别的最佳预测而形成的模型的性能不会比原始EEPAS模型好。

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