# A Seismic Phase Recognition Algorithm Based on Time Convolution Networks
## 1 Introduction  
At present, artificial intelligence (AI) methods are widely used within the field of geophysical research and have achieved great results using deep neural networks. AI can be used to process massive amounts of seismic data by improving deep network generalization to provide more effective data for the inversion of the geological structures in existing seismic catalogs. Due to the improvements in seismic observation methods over recent years, the rapid and high-precision automatic detection of seismic data has become the focus of research. Over recent years, deep learning has also developed rapidly and more and more seismologists have begun to use deep neural networks to obtain information from seismic data, such as seismic phase identification, earthquake location detection, seismic phase mechanism solutions, aftershock searches, earthquake early warning systems and forecasts. [PERSON] et al. (2018) trained a 17-layer inception deep network model to detect the start times of near-seismic P and S wave phases [1] (P and S waves are two kinds of seismic waves: P waves are longitudinal waves, i.e., vertical shaking, and S waves are transverse waves, i.e., horizontal shaking: P waves are also faster than S waves). During the training process, the authors considered the addition of noise to the label data, the regularization of the output results from the convolutional layers, the dropout operation and trainingmethods based on noise stepping, which improved the generalization ability of their neural network. This method could directly output the start time of a seismic phase with better stability than the traditional method and it could adapt to seismic signals with different signal-to-noise ratios. [PERSON] et al. (2018) trained a convolutional neural network (CNN) classifier using more than 4.8 million seismograms from Southern California, USA, that were manually labeled with P wave start times and polarities and achieved the automated high-precision measurement of P wave start times and initial motion polarities [2]. Since the network structures of CNN convolution and filtering effectively learn features directly from data, thereby avoiding model construction and other tasks, deep neural networks can achieve great results in seismic phase detection research. [PERSON] et al. (2019) used U-Net and probability distribution methods to automatically detect earthquake phases in Southern California for the first time, which provided a novel way for image segmentation structures to detect earthquake phases [3] and achieved good results. On this basis, [PERSON] et al. (2019) redesigned a U-shaped network algorithm to create the Unet_cea structure. They used more than 80,000 seismic data points from earthquakes of different magnitudes and signal-to-noise ratios that were recorded during Wenchuan aftershocks and from the Metropolitan Seismic Network for training and testing. During their testing, the automatic identification and timely detection of seismic phases were realized and a high level of accuracy was also obtained [4]. The U-shaped network had a significantly higher hit rate and a lower root mean square error than the STA/LTA and kurtosis analysis automatic detection methods. With the development of deep neural networks, forming fusion structures using different structures has gradually become the new trend. [PERSON] et al. (2019) designed a CNN and recurrent convolutional network tandem structure to detect P and S waves in continuous seismic waveforms [5]. They used an eight-layer CNN to distinguish between seismic signals and noise data and then input a two-layer bidirectional recurrent neural network (RNN) structure to extract the start times of the P and S waves. Their results showed that this method could achieve an extremely small error rate and high accuracy. [PERSON] et al. (2020) designed an improved U-Net model that could use AI to capture competition and Hi-net data training, which achieved accurate time extraction from continuous waveforms [6]. [PERSON] et al.