Discrimination of earthquake and quarry blast based on multi-input convolutional neural network

The discrimination of natural earthquakes and quarry blasts is an important part of earthquake monitoring and early warning. In recent years, the fast-developing deep learning algorithm with its powerful data feature extraction and image recognition capabilities can quickly and accurately constrain the classifications of seismic events. The multi-input convolutional neural network is used to construct the automatic classification network of natural earthquake and quarry blasts. The input layer includes the waveform data of multiple stations and the spectrogram data of a single station, so that the multi-input convolutional neural network can learn the waveform, spectrogram, and polarity characteristics of the event at the same time. Earthquakes and quarry blasts recorded in Utah, the United States in 2012 are used to construct the training data set, and the known natural earthquakes and quarry blasts from 2013 to 2016 in the area are utilized to test the trained network model. The results show that the multi-input convolutional neural network has high recognition accuracy, and the discrimination accuracy is as high as 97%.