论文标题
深度学习的实时地震早期警告：在2016年中央阿众，意大利地震序列
Real-time Earthquake Early Warning with Deep Learning: Application to the 2016 Central Apennines, Italy Earthquake Sequence
论文作者
论文摘要
需要地震预警系统才能在破坏的S波到达以减轻地震危害之前尽快报告地震位置和大幅度。深度学习技术为从完整的地震波形而不是地震阶段选择中提取地震源信息提供了潜力。我们开发了一种新颖的深度学习地震预警系统，该系统利用完全卷积网络同时检测地震并从连续的地震波形流中估算其源参数。该系统在一个站点接收地震信号后立即确定地震位置，并通过接收连续数据来改善解决方案。我们将该系统应用于意大利中部亚平宁山脉及其随后的序列的2016 MW 6.0地震。地震位置和大小可以在最早的p阶段后四秒钟可靠地确定，平均误差范围分别为6.8-3.7 km和0.31-0.23。
Earthquake early warning 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 earthquake early warning 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 one station receives earthquake signals and evolutionarily improves the solutions by receiving continuous data. We apply the system to the 2016 Mw 6.0 earthquake in Central Apennines, Italy and its subsequent sequence. Earthquake locations and magnitudes can be reliably determined as early as four seconds after the earliest P phase, with mean error ranges of 6.8-3.7 km and 0.31-0.23, respectively.
