Zhang Xiong, Zhang Miao
Engineering Research Center for Seismic Disaster Prevention and Engineering Geological Disaster Detection of Jiangxi Province, East China University of Technology, Nanchang, Jiangxi China.
Shanghai Sheshan National Geophysical Observatory, Shanghai, China.
Commun Earth Environ. 2024;5(1):528. doi: 10.1038/s43247-024-01718-8. Epub 2024 Sep 27.
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 universally 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 most 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. The generalized neural networks facilitate global applications of real-time EEW, eliminating complex empirical configurations typically required by traditional methods.
深度学习通过直接挖掘地震波形来增强地震监测能力。然而，目前在特定区域训练的神经网络在推广到不同区域时面临挑战。在这里，我们采用一种数据重组方法来创建在任意位置发生的、具有任意台站分布的广义地震，用于神经网络训练。然后，经过训练的模型可以普遍应用于不同的监测设置，以便从连续的地震波形流中进行地震检测和参数评估。这使得在地震发生的早期阶段就能启动实时地震预警（EEW）。当应用于日本和美国加利福尼亚州的大量地震序列时，我们的模型在初至P波到达后的4秒内可靠地报告了大多数地震的位置和震级，平均误差分别为2.6 - 7.3千米和0.05 - 0.32。广义神经网络促进了实时地震预警的全球应用，消除了传统方法通常需要的复杂经验配置。
