Universal neural networks for real-time earthquake early warning trained with generalized earthquakes

Deep learning enhances earthquake monitoring by directly mining seismic waveforms. Existing neural networks, however, struggle with generalization across diverse regions. This study uses a data recombination method to create generalized earthquakes for training, enabling universal application with varying monitoring setups for real-time Earthquake Early Warning (EEW). Applied to Japan and California datasets, models reliably report earthquake locations and magnitudes within 4 seconds of the initial P-wave arrival, with mean errors of 2.6–7.3 km and 0.05–0.32, respectively. This approach simplifies real-time EEW, eliminating complex empirical configurations of traditional methods.

Xiong Zhang, Miao Zhang