Real‐Time Earthquake Early Warning With Deep Learning: Application to the 2016 M 6.0 Central Apennines, Italy Earthquake
Real‐Time Earthquake Early Warning With Deep Learning: Application to the 2016 M 6.0 Central Apennines, Italy Earthquake
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DOI：
10.1029/2020gl089394
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发表时间：
2020-06
影响因子：
5.2
通讯作者：
Xiong Zhang;Miao Zhang;X. Tian
中科院分区：
文献类型：
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作者：
Xiong Zhang;Miao Zhang;X. Tian
地震预警系统需要在破坏性的S波到达之前尽快报告地震位置和震级，以减轻地震灾害。深度学习技术为从完整的地震波形中提取震源信息提供了可能，而不是从地震相位选择中提取。我们开发了一种新的深度学习EEW系统，该系统利用完全卷积网络来同时检测地震并从连续的地震波形流中估计其震源参数。当极少数台站接收到地震信号时，该系统立即确定地震位置和震级，并通过接收连续数据来进化地改进解决方案。我们将该系统应用于2016年意大利亚平宁中部6.0级地震及其第一周余震。地震位置和震级最早可在最早P震相后4时被可靠地确定，平均误差范围分别为8.5~4.7公里和0.33~0.27。
Earthquake early warning (EEW) systems are required to report earthquake locations and magnitudes as quickly as possible before the damaging S wave arrival to mitigate seismic hazards. Deep learning techniques provide potential for extracting earthquake source information from full seismic waveforms instead of seismic phase picks. We developed a novel deep learning EEW system that utilizes fully convolutional networks to simultaneously detect earthquakes and estimate their source parameters from continuous seismic waveform streams. The system determines earthquake location and magnitude as soon as very few stations receive earthquake signals and evolutionarily improves the solutions by receiving continuous data. We apply the system to the 2016 M 6.0 Central Apennines, Italy Earthquake and its first‐week aftershocks. Earthquake locations and magnitudes can be reliably determined as early as 4 s after the earliest P phase, with mean error ranges of 8.5–4.7 km and 0.33–0.27, respectively.