Identification of Low‐Frequency Earthquakes on the San Andreas Fault With Deep Learning
Identification of Low‐Frequency Earthquakes on the San Andreas Fault With Deep Learning
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DOI：
10.1029/2021gl093157
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发表时间：
2021-07
影响因子：
5.2
通讯作者：
A. Thomas;A. Inbal;J. Searcy;D. Shelly;R. Bürgmann
中科院分区：
文献类型：
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作者：
A. Thomas;A. Inbal;J. Searcy;D. Shelly;R. Bürgmann
低频地震是缓慢断层滑动的一种地震表现。它们的突发、低幅度和独特的频率特征使得这些事件在连续的地震数据中很难检测到。在这里，我们使用Shelly(2017)，https://doi.org/10.1002/2017jb014047的目录作为训练数据，训练卷积神经网络来检测加利福尼亚州帕克菲尔德附近的低频地震。我们探讨了不同的模型大小和目标如何影响结果网络的性能。我们的首选网络具有85%的峰值精度，可以可靠地提取单台记录的低频地震S波的到达时间。我们使用Parkfield附近的永久和临时站点的数据演示了网络的能力，并显示它检测到不属于Shelly(2017)，https://doi.org/10.1002/2017jb014047目录的新LFE。总体而言，机器学习方法在识别额外的低频震源方面显示出巨大的前景。这项技术快速、可推广，而且不需要来源重复。
Low‐frequency earthquakes are a seismic manifestation of slow fault slip. Their emergent onsets, low amplitudes, and unique frequency characteristics make these events difficult to detect in continuous seismic data. Here, we train a convolutional neural network to detect low‐frequency earthquakes near Parkfield, CA using the catalog of Shelly (2017), https://doi.org/10.1002/2017jb014047 as training data. We explore how varying model size and targets influence the performance of the resulting network. Our preferred network has a peak accuracy of 85% and can reliably pick low‐frequency earthquake (LFE) S‐wave arrival times on single station records. We demonstrate the abilities of the network using data from permanent and temporary stations near Parkfield, and show that it detects new LFEs that are not part of the Shelly (2017), https://doi.org/10.1002/2017jb014047 catalog. Overall, machine‐learning approaches show great promise for identifying additional low‐frequency earthquake sources. The technique is fast, generalizable, and does not require sources to repeat.