# Research on Seismic Phase Recognition Method Based on Bi-LSTM Network
## 3 Preparation for the Experiments
### Model Evaluation Metric Selection  
The seismic phase identification indicators mainly include root mean square error (_RMSE_), accuracy (_A_), and misdetection rate (_M_). These indicators are used to evaluate the performance of the seismic phase automatic recognition model. RMSE is used to measure the deviation between the predicted and actual values of a model, and is an important indicator for measuring the accuracy of model predictions. In the automatic identification of seismic phases, _RMSE_ can be used to evaluate the accuracy of the model in identifying seismic phases. \(A\) refers to the proportion of seismic phases correctly identified by a model to the total number of identified phases. High accuracy means that the model can accurately identify most seismic phases, which is crucial for the analysis of seismic data. \(M\) represents the proportion of seismic phases that the model failed to identify to the total number of identified phases. A low misdetection rate means that the model can identify as many seismic phases as possible to avoid missing important information.  
During the seismic phase recognition experiment, in order to objectively evaluate the performance of the automatic phase recognition model based on the Bi-LSTM network and ensure the reliability and validity of the experimental results, this paper calculates the _RMSE_, \(A\), and \(M\) of different methods. These three metrics are used as the basis for evaluating the effect of the phase recognition model and whether it meets the practical use standard, and the calculation formulas of the three evaluation indexes are shown in Equations (3)-(5).  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left[(T_{i}-t_{i})^{2}\right]} \tag{3}\]  
\[A=\frac{T_{e}}{(T_{e}+F_{e})} \tag{4}\]  
Figure 2: Data example of STEAD dataset.  
\[M=\frac{F_{n}}{(T_{e}+F_{n})} \tag{5}\]  
_RMSE_ is the root mean square error between the model-recognized phase arrival time \(T_{i}\) and the manually labeled reference phase arrival time \(t_{i}\), which can indicate the precision of the model's automatic phase recognition, where \(n\) denotes the number of seismic waveform data in the dataset and \(i\) denotes the seismic waveform data's sequence number; \(A\) is the accuracy rate of the model's phase recognition, \(T_{e}\) is the number of times the model correctly detects the phase's arrival time, while \(F_{e}\) is the number of times the model wrongly detects the phase's arrival time, and the threshold values for correct recognition of seismic phase are \(\pm 0.5\) s for P-wave arrival and \(\pm 0.5\) s for S-wave arrival; more than \(\pm 0.5\) s will result in incorrect recognition. \(A\) is the ratio of the number of phases correctly recognized by the model within the error range to the total number of phases recognized by the model, which reflects the accuracy of the model's phase recognition. \(M\) is the misdetection rate of model seismic phase recognition, and \(F_{n}\) is the number of misdetected phases. The misdetection rate M reflects the completeness of model seismic phase recognition for the manually labeled reference phases and indicates the effectiveness of model seismic phase recognition.