Convolutional and Long Short-term Memory Neural Network for Earthquake Detection
Abstract— Earthquake detection is a foundation and critical work link in the study of seismology. Traditional detection algorithms have the disadvantages of low detection sensitivity, low computational efficiency, and poor general applicability. In this paper, we develop a novel earthquake detection method based on convolutional neural network (CNN) and long short-term memory network (LSTM). It extracts the high-level features in the seismic signal and learns the time-frequency characteristics of the main phase by recording the three component data on a single station. We train the network using 4932 waveform windows (2236 positive windows and 2696 negative windows) recorded in Wenchuan, China, with a window size of 30s. Using the trained model to test a continuous waveform for one day, compared to the long-short window energy ratio method (STA/LTA), all manually selected seismic events were successfully detected. To explore the impact of different window sizes and LSTM layers on the detection results, we use a larger dataset (derived from Oklahoma, USA) for network training. The test results show that our method not only has a good generalization ability for cataloging events, but also detects micro-seismic events that are not included in the catalog. The detection accuracy of cataloging events reaches 100%. Our results indicate that this method has fast, efficient and scalable superior performance in earthquake detection.
Index Terms— earthquake detection, CNN, LSTM.
Xuefan Xu, Yingxue Wang, Yifeng Liu
China Academy of Electronics and Information Technology, CHINA
Lian Zou, Xuefan Xu
School of Electronic Information, Wuhan University, CHINA
Cite: Xuefan Xu, Yingxue Wang, Lian Zou, Yifeng Liu, "Convolutional and Long Short-term Memory Neural Network for Earthquake Detection," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, pp. 378-386, Hong Kong, 15-17 June, 2019.