Juhel Kévin, Bletery Quentin, Licciardi Andrea, Vallée Martin, Hourcade Céline, Michel Théodore
Observatoire de la Côte d'Azur, Université Côte d'Azur, IRD, CNRS, Géoazur, Valbonne, France.
Laboratoire de Planétologie et Géosciences, CNRS UMR 6112, Nantes Université, Université d'Angers, Le Mans Université, Nantes, France.
Commun Earth Environ. 2024;5(1):561. doi: 10.1038/s43247-024-01725-9. Epub 2024 Oct 4.
Prompt ElastoGravity Signals are light-speed gravity-induced signals recorded before the arrival of seismic waves. They have raised interest for early warning applications but their weak amplitudes close to the background seismic noise have questioned their actual potential for operational use. A deep-learning model has recently demonstrated its ability to mitigate this noise limitation and to provide in near real-time the earthquake magnitude ( ). However, this approach was efficient only for large earthquakes ( ≥ 8.3) of known focal mechanism. Here we show unprecedented performance in full earthquake characterization using the dense broadband seismic network deployed in Alaska and Western Canada. Our deep-learning model provides accurate magnitude and focal mechanism estimates of ≥ 7.8 earthquakes, 2 minutes after origin time (hence the tsunamigenic potential). Our results represent a major step towards the routine use of prompt elastogravity signals in operational warning systems, and demonstrate its potential for tsunami warning in densely-instrumented areas.
瞬发弹性重力信号是在地震波到达之前记录到的以光速传播的重力感应信号。它们引起了人们对早期预警应用的兴趣，但在接近背景地震噪声时其微弱的振幅引发了对其实际应用潜力的质疑。最近，一个深度学习模型展示了其减轻这种噪声限制并近乎实时提供地震震级（ ）的能力。然而，这种方法仅对已知震源机制的大地震（ ≥ 8.3）有效。在此，我们利用部署在阿拉斯加和加拿大西部的密集宽带地震网络，在全面地震特征描述方面展现出前所未有的性能。我们的深度学习模型在震源时间后2分钟就能提供≥7.8级地震准确的震级和震源机制估计（因此具有海啸生成潜力）。我们的结果朝着在运行预警系统中常规使用瞬发弹性重力信号迈出了重要一步，并证明了其在仪器密集地区进行海啸预警的潜力。
