PhaseLink: A Deep Learning Approach to Seismic Phase Association
PhaseLink: A Deep Learning Approach to Seismic Phase Association
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DOI：
10.1029/2018jb016674
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发表时间：
2019-01-01
影响因子：
3.9
通讯作者：
Heaton, Thomas H.
中科院分区：
文献类型：
--
作者：
Ross, Zachary E.;Yue, Yisong;Heaton, Thomas H.
地震相位关联是地震学中的一项基本任务，涉及将来自同一地震的不同传感器上的相位检测连接在一起。它被广泛用于在永久和临时地震网络上检测地震，并成为世界各地制作的大多数地震活动目录的基础。这项任务可能具有挑战性，因为源的数量是未知的，事件经常在时间上重叠，或者可能在网络的不同部分同时发生。我们提出了PhaseLink，这是一个基于深度学习最新进展的框架，用于无网格地震相位关联。我们的方法学习将共享一个共同起源的相位链接在一起，并且完全基于使用一维速度模型生成的P波和S波到达时间的数百万个合成序列进行训练。我们的方法是简单的，以实现任何构造制度，适合实时处理，并可以自然地将错误的到达时间选择。PhaseLink可以通过简单地将问题案例的示例添加到训练数据集来改进，而不是调整一组特定的超参数来提高性能。我们展示了PhaseLink在来自南加州的具有挑战性的序列和来自日本的合成序列上的最新性能，这些序列旨在测试该方法失败的点。对于所检查的数据集，PhaseLink可以精确地将相位与发生在原点时间中仅相隔约12秒的事件相关联。这种方法有望提高地震活动目录的分辨率，增加实时地震监测的稳定性，并简化大型地震数据集的自动化处理。
Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks and underlies most seismicity catalogs produced around the world. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. We present PhaseLink, a framework based on recent advances in deep learning for grid-free earthquake phase association. Our approach learns to link phases together that share a common origin and is trained entirely on millions of synthetic sequences of P and S wave arrival times generated using a 1-D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real-time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training data set. We demonstrate the state-of-the-art performance of PhaseLink on a challenging sequence from southern California and synthesized sequences from Japan designed to test the point at which the method fails. For the examined data sets, PhaseLink can precisely associate phases to events that occur only approximate to 12s apart in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real-time seismic monitoring, and streamline automated processing of large seismic data sets.