# Research on Seismic Phase Recognition Method Based on Bi-LSTM Network
## 1 Introduction  
As part of the basic work of seismic signal processing, seismic phase recognition plays an important role in the field of seismology. In the early research process of seismology, seismic phase recognition basically needs to be completed by manual recognition, and all kinds of phase data are calculated by combining with a travel time table. Manual recognition requires a long period of professional knowledge and skills training and relies on personal experience to perform recognition, and cannot be promoted on a large scale. Meanwhile, the speed of artificial recognition cannot meet the needs of real-time processing of an earthquake rapid report. With the gradual improvement of seismic stations and networks, the total number of seismic stations has reached 1972. The rapid increase in seismic waveform data makes it difficult to continue the manual recognition work, and the efficiency is gradually reduced. To address the problem of the slow speed and low precision of the current manual recognition methods, scholars at home and abroad began to study the automatic recognition methods of seismic phases in the 1960s, and have achieved a series of scientific research results. At present, the automatic recognition methods of seismic phases mainly include two categories: the traditional automatic phase recognition method and the deep learning method emerging in recent years due to big data technology. Among the traditional methods for automatic recognition of seismic phase, the most commonly used methods are STA/LTA (short-term average/long-term average) [1, 2] and AIC (Akaike information criterion) [3, 4], as well as fractal dimension method, polarization analysis method, and artificial neural network, among others. At the same time, with the advancement of technology in recent years, deep learning technology has developed rapidly. Experts and scholars have begun to try to apply deep learning technology to seismic phase automatic recognition, and deep learning methods for seismic phase automatic recognition have gradually become a research hotspot. In 1976, [PERSON] [3] proposed the application of STA/LTA to pick up the arrival time of earthquake events. This method calculates the ratio of the average short-term windowsignal (STA) to the average long-term window signal (LTA) at each point in the signal. The judgment principle is that at a point where the seismic wave arrives, this ratio will increase sharply. Therefore, a threshold is set for judgment, and the judgment criterion is the comparison between the ratio at that point and the threshold. When the ratio exceeds the set value, this point is the point where the seismic wave arrives. The STA/LTA seismic phase automatic recognition method has been widely used in real-time seismic phase recognition due to its advantages of simple principle and fast calculation speed. However, due to the issue of feature function and threshold selection, different feature functions and thresholds have a significant impact on the final recognition accuracy. At the same time, the random noise and high-frequency pulses generated during the seismic recording process can only estimate the approximate range of signal arrival in low signal-to-noise ratio situations, and sometimes there may even be misjudgments in signal pickup. Some experts and scholars have proposed improved methods based on this approach to address these issues, achieving an increase in picking accuracy [5; 6; 7; 8]. At present, STA/LTA is commonly used to roughly determine the picking range, and other high-precision methods are used to pick up the exact time. The Akaike information criterion (AIC) [4] was first proposed by Japanese statistician [PERSON]. Based on the basic concept of entropy, it is a standard used to measure the accuracy of statistical model fitting. The AIC method has been widely used in seismic phase automatic recognition systems [9; 10; 11; 12], but it is greatly affected by the signal-to-noise ratio of seismic signals.