GRAPES: Earthquake early warning by passing seismic vectors through the grapevine
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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.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | GRAPES: Earthquake early warning by passing seismic vectors through the grapevine |
| Series title | Geophysical Research Letters |
| DOI | 10.1029/2023GL107389 |
| Volume | 51 |
| Issue | 9 |
| Publication Date | May 08, 2024 |
| Year Published | 2025 |
| Language | English |
| Publisher | Seismological Society of America |
| Contributing office(s) | Earthquake Science Center |
| Description | e2023GL107389, 10 p. |
| Country | Japan |
| Other Geospatial | Shimane/HiroshimaPrefectures |