FASER: Seismic Phase Identifier for Automated Monitoring
FASER: Seismic Phase Identifier for Automated Monitoring
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DOI：
10.1145/3447548.3467064
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
F. A. Chowdhury;M. A. Siddiquee;G. Baker;A. Mueen
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文献类型：
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作者：
F. A. Chowdhury;M. A. Siddiquee;G. Baker;A. Mueen
地震相位识别是根据地震仪记录的波形（即时间序列）对台站接收到的地震波类型进行分类。自动相位识别是大规模地震监测应用的一个组成部分，包括地震预警系统和地下爆炸监测。准确、快速、细粒度的相位识别有助于地震定位估计、了解地壳和地幔结构并进行预测建模等。然而，现有的操作系统利用多个附近站点进行精确识别，这增加了复杂性和人工干预，从而延迟了响应时间。此外，单站系统大多进行粗相识别。在本文中，我们重新审视了地震相位分类作为地震处理管道的一个组成部分。我们开发了一个机器学习模型FASER，它从信号检测器获取输入，并产生相位类型作为信号关联器的输出。该模型是卷积和长短期记忆网络的结合。我们的方法确定了更精细的波类型，包括地壳和地幔相。我们在真实数据集上进行了全面的实验，表明FASER优于现有的基线。我们评估了FASER在世界各地的源和站，以展示新源和站的一致性能。
Seismic phase identification classifies the type of seismic wave received at a station based on the waveform (i.e., time series) recorded by a seismometer. Automated phase identification is an integrated component of large scale seismic monitoring applications, including earthquake warning systems and underground explosion monitoring. Accurate, fast, and fine-grained phase identification is instrumental for earthquake location estimation, understanding Earth's crustal and mantle structure for predictive modeling, etc. However, existing operational systems utilize multiple nearby stations for precise identification, which delays response time with added complexity and manual interventions. Moreover, single-station systems mostly perform coarse phase identification. In this paper, we revisit the seismic phase classification as an integrated part of a seismic processing pipeline. We develop a machine-learned model FASER, that takes input from a signal detector and produces phase types as output for a signal associator. The model is a combination of convolutional and long short-term memory networks. Our method identifies finer wave types, including crustal and mantle phases. We conduct comprehensive experiments on real datasets to show that FASER outperforms existing baselines. We evaluate FASER holding out sources and stations across the world to demonstrate consistent performance for novel sources and stations.