Waveform classification and seismic recognition by convolution neural network

The development of efficient, high-precision, and universal automatic waveform pick-up algorithm is more and more important in the background of earthquake big data. The main challenge comes from how to adapt to the classification of different types of seismic events in different regions. In this paper, according to the seismic event-noise classification problem, a convolutional neural network method was used to train the dataset based on 13839 Wenchuan earthquake aftershocks and 8900 new Wenchuan aftershock events were used as the test data set. The training and detection accuracy rates were both over 95%. In the detection of continuous waveforms, the CNN method is superior to the traditional methods of STA/LTA and Fbpicker in precision and recall rate, and can find a large number of manually selected microseismic events that are easily missed. Finally, we use the trained optimal model to identify 8-day continuous waveform data from 441 stations nationwide. CNN detects 7016 waveforms, then we pick up 1380 pairs of P and S arrival times using an automatic picking algorithm, finally the pick-ups were successfully associated with 540 earthquake catalog events. The overall recognition accuracy of events above magnitude 1 was 54% and 80% above magnitude 2, while in some areas such as Sichuan and Xinjiang the detection rate is higher. It is shown that CNN neural network has broad application prospects in the real-time earthquake detection and location.