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

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.

arXiv comment13 pages main text, 16 pages appendix. This article has been accepted for publication in Geophysical Journal International Published by Oxford University Press on behalf of the Royal Astronomical Society

References in corpus (5)

• Reconciling modern machine learning practice and the bias-variance trade-off
• Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
• A Machine-Learning Approach for Earthquake Magnitude Estimation
• Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations
• Rapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network

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• Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning
• CREIME: A Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation
• A probabilistic view on rupture predictability: all earthquakes evolve similarly