Seismic phase identification using the convolutional neural networks based on sample enhancement

In order to quickly and efficiently identify earthquake events and pick up seismic phases from seismic data, this paper a small sample enhancement-based automatic phase picking method based on a convolutional neural network. A total of three months of data from the L0230 station in Linzhi, Tibet were used as a training set, and one-month continuous waveform data from the other six stations in this area were used as the test sets. Gaussian noise, random noise splicing, random selection of noise and random interception of seismic events were used to enhance the training set so as to improve the accuracy. The results show that the accuracy of the model on the test set was 80% before the samples were enhanced, and up to 97% after that, suggesting that the sample enhancement effectively improves the generalization perfor-mance and anti-interference ability of the model. Within the 0.5 s error range, the accuracy of the seismic phase automatic picking is higher than 81%, and within the 1.0 s error range, the accuracy is higher than 95%. Moreover, it is able to detect mislabeled and missing seismic phases in manually picking seismic phases by this method.