水准点(测量)
可扩展性
地震预报
计算机科学
余震
数据集
地震模拟
集合(抽象数据类型)
比例(比率)
人工神经网络
地震学
灵活性(工程)
数据挖掘
机器学习
人工智能
地质学
地理
地图学
数据库
统计
程序设计语言
数学
大地测量学
作者
Kelian Dascher‐Cousineau,Oleksandr Shchur,Emily E. Brodsky,Stephan Günnemann
摘要
Abstract 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 (>10 4 events). The basic components in RECAST add flexibility and scalability for earthquake forecasting without sacrificing performance.
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