CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
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DOI：
10.1038/s41598-019-45748-1
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发表时间：
2019-07-16
影响因子：
4.6
通讯作者：
Beroza, Gregory C.
中科院分区：
文献类型：
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作者：
Mousavi, S. Mostafa;Zhu, Weicliang;Beroza, Gregory C.
地震信号探测是观测地震学的核心。一个好的检测算法应该对各种波形形状的小而弱的事件敏感，对背景噪声和非地震信号具有鲁棒性，对处理大数据量具有效率。在这里，我们介绍了Cnn-Rnn地震检测器（CRED），一种基于深度神经网络的检测器。CRED在残余结构中使用卷积层和双向长短期记忆单元的组合。它从各个台站记录的三分量数据中学习地震信号中主要相位的时频特性。我们使用北加州记录的50万张地震图来训练网络（其中25万张与构造地震有关，25万张被确定为噪音）。通过将该模型应用于一组半合成信号，证明了该模型对噪声水平和非地震信号的鲁棒性。我们还将该模型应用于阿肯色州中部一个月的连续数据记录，以证明其效率、通用性和敏感性。我们的模型能够探测到800多个小至-1.3 ML的微地震，这些微地震是在远离训练区域的水力压裂过程中引起的。我们将该模型的性能与STA/LTA、模板匹配和FAST算法进行了比较。我们的研究结果表明，CRED具有高效可靠的性能。该框架在降低检测阈值的同时最大限度地减少误报检测率。
Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks. CRED uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on individual stations. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. We also apply the model to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 800 microearthquakes as small as -1.3 ML induced during hydraulic fracturing far away than the training region. We compare the performance of the model with the STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise for lowering the detection threshold while minimizing false positive detection rates.