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GRAPES: Earthquake early warning by passing seismic vectors through the grapevine

Timothy Hugh Clements·Elizabeth S. Cochran·Annemarie S. Baltay·Sarah E. Minson·Clara Yoon
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Abstract

Estimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield-based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES’ internal activations, which we call “seismic vectors”, correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake.

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Geophysical Research LettersArticle
90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 32° to 36° latitude; 130° to 136° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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Timothy Hugh Clements, Elizabeth S. Cochran, Annemarie S. Baltay, Sarah E. Minson, Clara Yoon. 2024-05-08. GRAPES: Earthquake early warning by passing seismic vectors through the grapevine. https://doi.org/10.1029/2023gl107389

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