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Earthquakes are major hazards to humans, buildings and infrastructure. Earlywarning methods aim to provide advance notice of incoming strong shaking toenable preventive action and mitigate seismic risk. Their usefulness depends onaccuracy, the relation between true, missed and false alerts, and timeliness,the time between a warning and the arrival of strong shaking. Currentapproaches suffer from apparent aleatoric uncertainties due to simplifiedmodelling or short warning times. Here we propose a novel early warning method,the deep-learning based transformer earthquake alerting model (TEAM), tomitigate these limitations. TEAM analyzes raw, strong motion waveforms of anarbitrary number of stations at arbitrary locations in real-time, making iteasily adaptable to changing seismic networks and warning targets. We evaluateTEAM on two regions with high seismic hazard, Japan and Italy, that arecomplementary in their seismicity. On both datasets TEAM outperforms existingearly warning methods considerably, offering accurate and timely warnings.Using domain adaptation, TEAM even provides reliable alerts for events largerthan any in the training data, a property of highest importance as records fromvery large events are rare in many regions.

The transformer earthquake alerting model: A new versatile approach to earthquake early warning