Deep learning enhances earthquake monitoring capabilities by mining seismic
waveforms directly. However, current neural networks, trained within specific
areas, face challenges in generalizing to diverse regions. Here, we employ a
data recombination method to create generalized earthquakes occurring at any
location with arbitrary station distributions for neural network training. The
trained models can then be applied to various regions with different monitoring
setups for earthquake detection and parameter evaluation from continuous
seismic waveform streams. This allows real-time Earthquake Early Warning (EEW)
to be initiated at the very early stages of an occurring earthquake. When
applied to substantial earthquake sequences across Japan and California (US),
our models reliably report earthquake locations and magnitudes within 4 seconds
after the first triggered station, with mean errors of 2.6-6.3 km and
0.05-0.17, respectively. These generalized neural networks facilitate global
applications of real-time EEW, eliminating complex empirical configurations
typically required by traditional methods.

利用深度学习直接挖掘地震波形来增强地震监测能力，本研究使用数据重组方法训练具有普适性的神经网络，以便应用于不同地区的地震监测，实现实时的地震早期预警。通过应用于日本和美国加利福尼亚的地震序列，我们的模型可在触发的第一个台站后的 4 秒内可靠地报告地震位置和震级，平均误差分别为 2.6-6.3 公里和 0.05-0.17。这些普适的神经网络有助于全球实时地震早期预警的应用，消除了传统方法中通常需要复杂经验配置的要求。