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• W4386308278 abstract "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." @default.
• W4386308278 created "2023-09-01" @default.
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• W4386308278 date "2023-08-31" @default.
• W4386308278 modified "2023-10-04" @default.
• W4386308278 title "Using Deep Learning for Flexible and Scalable Earthquake Forecasting" @default.
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• W4386308278 doi "https://doi.org/10.1029/2023gl103909" @default.
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