Generalized Seismic Phase Detection with Deep Learning
Generalized Seismic Phase Detection with Deep Learning
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DOI：
10.1785/0120180080
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发表时间：
2018-10-01
影响因子：
3
通讯作者：
Heaton, Thomas H.
中科院分区：
文献类型：
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作者：
Ross, Zachary E.;Meier, Men-Andrin;Heaton, Thomas H.
为了最佳地监测地震发生过程，地震学家们从大约世纪前仪器地震网络开始就一直在寻求降低探测灵敏度。最近，已经可以搜索连续波形档案以寻找先前记录的事件的副本（即，模板匹配），这使得检测到的地震数量至少增加了一个数量级，并大大提高了我们对地质结构的看法。然而，以这种方式产生的地震目录有很大的偏差，因为它们对没有模板可用的事件完全视而不见，例如在以前平静的地区或非常大的震级事件。在这里，我们表明，通过深度学习，我们可以在不牺牲检测灵敏度的情况下克服这些偏差。我们在南加州地震网络的大量手工标记数据档案上训练了一个卷积神经网络（ConvNet），以检测地震体波相位。我们证明了ConvNet在检测相位方面非常敏感和鲁棒，即使被高背景噪声掩盖，并且当ConvNet应用于训练集中未表示的新数据（特别是非常大的事件）时。这种广义的相位检测框架将显着改善地震监测和目录，形成了广泛的基础和应用地震学研究的基础。
To optimally monitor earthquake-generating processes, seismologists have sought to lower detection sensitivities ever since instrumental seismic networks were started about a century ago. Recently, it has become possible to search continuous waveform archives for replicas of previously recorded events (i.e., template matching), which has led to at least an order of magnitude increase in the number of detected earthquakes and greatly sharpened our view of geological structures. Earthquake catalogs produced in this fashion, however, are heavily biased in that they are completely blind to events for which no templates are available, such as in previously quiet regions or for very large-magnitude events. Here, we show that with deep learning, we can overcome such biases without sacrificing detection sensitivity. We trained a convolutional neural network (ConvNet) on the vast hand-labeled data archives of the Southern California Seismic Network to detect seismic body-wave phases. We show that the ConvNet is extremely sensitive and robust in detecting phases even when masked by high background noise and when the ConvNet is applied to new data that are not represented in the training set (in particular, very large-magnitude events). This generalized phase detection framework will significantly improve earthquake monitoring and catalogs, which form the underlying basis for a wide range of basic and applied seismological research.