OKSP：一种用于哥斯达黎加地震勘探的新型深度学习自动事件检测管道
OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for Seismic Monitoringin Costa Rica
摘要：
小震级地震是最丰富的，但最难以鲁棒和良好地定位，因为它们的低振幅和高频率通常被不均匀的噪声源掩盖。它们突出了关于地震周期中断层系统的应力状态和时空行为的关键信息，因此，其完整表征对于改善地震危险性评估至关重要。随着计算能力的提高，现代深度学习算法正在利用不断增长的地震数据库，使科学家能够提高地震目录的完整性，系统地检测较小震级的地震，并减少主要由人为干预引入的错误。在这项工作中，我们介绍了OKSP，一种新的自动地震检测管道在哥斯达黎加的地震监测。使用哥斯达黎加高技术中心的Kabre超级计算机，我们将OKSP应用于2019年6月26日发生在哥斯达黎加-巴拿马边境的Puerto Armuelles M6.5地震的前一天和后5天，发现了哥斯达黎加火山和地震观测站先前未识别的1100次地震。从这些事件中，总共有23次震级低于1.0的地震发生在主震前一天到几个小时，揭示了破裂启动和地震相互作用导致这一生产性地震层序的发生。我们的观察结果表明，在研究期间，该模型是100%详尽和82%的精确度，导致F1得分为0.90。这是哥斯达黎加首次尝试使用深度学习方法自动检测地震，并表明在不久的将来，地震监测程序将完全由人工智能算法执行。
英文摘要：
Small magnitude earthquakes are the most abundant but the most difficult to locate robustly and well due to their low amplitudes and high frequencies usually obscured by heterogeneous noise sources. They highlight crucial information about the stress state and the spatio-temporal behavior of fault systems during the earthquake cycle, therefore, its full characterization is then crucial for improving earthquake hazard assessment. Modern DL algorithms along with the increasing computational power are exploiting the continuously growing seismological databases, allowing scientists to improve the completeness for earthquake catalogs, systematically detecting smaller magnitude earthquakes and reducing the errors introduced mainly by human intervention. In this work, we introduce OKSP, a novel automatic earthquake detection pipeline for seismic monitoring in Costa Rica. Using Kabre supercomputer from the Costa Rica High Technology Center, we applied OKSP to the day before and the first 5 days following the Puerto Armuelles, M6.5, earthquake that occurred on 26 June, 2019, along the Costa Rica-Panama border and found 1100 more earthquakes previously unidentified by the Volcanological and Seismological Observatory of Costa Rica. From these events, a total of 23 earthquakes with magnitudes below 1.0 occurred a day to hours prior to the mainshock, shedding light about the rupture initiation and earthquake interaction leading to the occurrence of this productive seismic sequence. Our observations show that for the study period, the model was 100% exhaustive and 82% precise, resulting in an F1 score of 0.90. This effort represents the very first attempt for automatically detecting earthquakes in Costa Rica using deep learning methods and demonstrates that, in the near future, earthquake monitoring routines will be carried out entirely by AI algorithms.
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