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

TL;DR

The paper introduces TEAM, a deep-learning transformer model for earthquake early warning that analyzes raw waveforms in real-time, outperforming existing methods in accuracy and timeliness across different seismic regions.

Contribution

The paper presents a novel transformer-based model for earthquake early warning that is adaptable, accurate, and capable of predicting larger-than-trained events using domain adaptation.

Findings

TEAM outperforms existing methods in Japan and Italy.

TEAM provides accurate, timely warnings in real-time.

Domain adaptation enables reliable alerts for larger events.

Abstract

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