Using Deep Learning for Flexible and Scalable Earthquake Forecasting
Using Deep Learning for Flexible and Scalable Earthquake Forecasting
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DOI：
10.1029/2023gl103909
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发表时间：
2023-08
影响因子：
5.2
通讯作者：
K. Dascher‐Cousineau;Oleksandr Shchur;E. Brodsky;Stephan Günnemann
中科院分区：
文献类型：
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作者：
K. Dascher‐Cousineau;Oleksandr Shchur;E. Brodsky;Stephan Günnemann
地震学正在见证地震目录的多样性和规模的爆炸性增长。这一社区努力的一个关键动机是，更多的数据应该转化为更好的地震预报。这种改进尚待观察。在这里，我们介绍了Recurrent Earthquake foreCAST（RECAST），这是一种基于神经时间点过程最新发展的深度学习模型。该模型能够获得更大的地震观测量和多样性，克服了传统方法的理论和计算限制。我们对一个时间流行病型余震序列模型进行了基准测试。对合成数据的测试表明，对于一个中等大小的数据集，RECAST可以直接从编目数据中准确地模拟类似地震的点过程。对南加州地震目录的测试表明，当训练集足够长（>104个事件）时，与我们的基准相比，拟合和预测精度有所提高。RECAST中的基本组件在不牺牲性能的情况下为地震预报增加了灵活性和可扩展性。
Seismology is witnessing explosive growth in the diversity and scale of earthquake catalogs. A key motivation for this community effort is that more data should translate into better earthquake forecasts. Such improvements are yet to be seen. Here, we introduce the Recurrent Earthquake foreCAST (RECAST), a deep‐learning model based on recent developments in neural temporal point processes. The model enables access to a greater volume and diversity of earthquake observations, overcoming the theoretical and computational limitations of traditional approaches. We benchmark against a temporal Epidemic Type Aftershock Sequence model. Tests on synthetic data suggest that with a modest‐sized data set, RECAST accurately models earthquake‐like point processes directly from cataloged data. Tests on earthquake catalogs in Southern California indicate improved fit and forecast accuracy compared to our benchmark when the training set is sufficiently long (>104 events). The basic components in RECAST add flexibility and scalability for earthquake forecasting without sacrificing performance.