# Research on Seismic Phase Recognition Method Based on Bi-LSTM Network
## 1 Introduction
In low signal-to-noise ratio situations, extreme points are not obvious, resulting in large recognition errors and low accuracy. The fractal theory was first proposed by [PERSON] in a paper published in the journal Science in 1967 [13], and later gradually improved and written into a new theory: fractal geometry. Fractal geometry breaks the traditional geometry that only uses integers to describe the dimensions of images and curves. In fractal theory, integers or decimals can be used to represent the fractal dimension of images or curves, describing their irregularity and variation patterns. [PERSON] and [PERSON] [14] first applied fractal theory to the automatic identification of seismic phases. The principle is that the fractal dimension values of noise signals and seismic signals remain unchanged before and after the arrival of an earthquake, and only change at the moment of the arrival of the seismic phase. The arrival of the seismic phase is the starting point of the fractal dimension value change. [PERSON] et al. [15] applied the principle of segmentation when calculating fractal dimension values, which accelerated the calculation speed and made the calculation results more stable. [PERSON] et al. [16] proposed the length fractal dimension method after simplifying the Huasdorff and divider methods, which improved computational efficiency and recognition accuracy. Compared with other seismic phase automatic recognition methods, the fractal dimension method can perform seismic phase recognition well under low signal-to-noise ratio conditions, with higher accuracy than other algorithms. However, the problem is that the calculation process of fractal dimension is relatively complex. Since the 1860s, the polarization analysis method has gradually been applied to automatic identification of seismic phases. The difference between the polarization analysis method and STA/LTA, AIC, and other methods is that it simultaneously processes and calculates the data of three seismic wave phases, while other methods are only based on a single component. In recent years, experts and scholars at home and abroad have been conducting in-depth research on the application of polarization analysis in seismic phase automatic identification systems [17; 18; 19]. Scholars have found that in the identification of S-wave seismic phases, the polarization analysis method has much better accuracy than STA/LTA and AIC methods, and the identification effect is better. However, the polarization analysis method requires solving eigenvalues and eigenvectors, which is significantly more computationally intensive compared to other methods. Its disadvantages are more obvious when fast calculations are needed. In recent years, with the continuous development of deep learning technology, considering the characteristics of temporal correlation of seismic signals, some experts and scholars have tried to apply the RNN (recurrent neural network) as a deep learning method for the automatic recognition of seismic phases. In 2018, [PERSON] and [PERSON] [20] applied the LSTM network to the automatic recognition of seismic phases, and the LSTM model achieved good results when the judgment threshold was set at 1 s. Compared with LSTM network, the Bi-LSTM network, as a variant of LSTM, is more suitable for processing temporal data; it has been widely used in other temporal signal recognition fields, and its recognition effect is obviously better than other networks. In 2019, [PERSON] et al. [21] used Bi-LSTM to recognize and classify heart sound signals of congenital heart disease, and the results showed that the Bi-LSTM network had good recognition effect, and the correct recognition rate of heart sound signals reached 84.2%. In 2019, [PERSON] and [PERSON] et al. [22] applied the Bi-LSTM network to the pattern recognition of quality control charts, and the experimental results showed that the Bi-LSTM network had higher precision.