## 3 Results  
The experiment was conducted on a high-performance computing system equipped with an NVIDIA GeForce RTX 4090 GPU, featuring 24 GB of video memory and 16,384 CUDA cores, alongside an Intel Core i9-14900K(F) processor with a clock speed of up to 6.0 GHz and 24 cores. After data filtering and preprocessing, the dataset was partitioned into training and test sets as follows:  
* Training set (Train): Data from January 2023 to February 2024, comprising 935 events (82%); \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{2}{c}{**Number of Filters/ Neurons**} & \multirow{2}{*}{**Kernel Size**} & \multirow{2}{*}{**Stride**} & \multirow{2}{*}{**Activation Function**} & \multirow{2}{*}{**Additionally**} \\  & & & & & & input data \\  & & & & & (dim. is \\ Conv1D-1 & 32 & 3 & 1 (by & ReLU & time- \\  & & & default) & ReLU & window \(\times\) \\  & & & & & fs, 1) \\ MaxPooling1D & & 2 & 2 & & \\ Conv1D-2 & 16 & 3 & 1 & ReLU & \\ MaxPooling1D & & 2 & 2 & & \\ Dropout & & & & & \\  & & & & & is 0.5 \\ \hline Conv1D-3 & 8 & 3 & 1 & ReLU & \\ MaxPooling1D & & 2 & 2 & & \\ Conv1D-4 & 4 & 3 & 1 & ReLU & \\ MaxPooling1D & & 2 & 2 & & \\ Dropout & & & & & \\  & & & & & \\ \hline Flatten & & & & & Converting \\ Dense-1 & 16 & & & ReLU & \\ Dense-2 & 1 & & & Sigmoid & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The architecture of the convolutional neural network includes four convolutional layers and two fully connected layers of perceptrons. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{1}{c}{**Value**} \\ \hline Optimizer & Adam \\ Learning rate & 0.001 (default) \\ Loss Function & Binary Crossentropy \\ Metric & Accuracy \\ L1/L2 regularization & None \\ Batch size & 128 \\ Number of epochs & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters of the model. Test set (Test): Data from March 2024 to December 2024, comprising 212 events (18%). The 82% to 18% split was selected to ensure a sufficient number of training samples while maintaining a substantial portion of data for testing, thus improving the reliability of the final evaluation results. The training set spans a complete annual cycle, allowing the model to account for seasonal variations in seismic activity. The test set was drawn from subsequent months, enabling an assessment of the model's temporal robustness and generalization ability. The number of training epochs is a crucial factor in model optimization, as an insufficient number of epochs may lead to underfitting, whereas an excessive number can result in overfitting. An early stopping mechanism was implemented to mitigate these risks, where training continued until the validation precision stabilized. The model performed best at 300 epochs, achieving minimal validation loss while maintaining optimal generalization to unseen data. The HHZ channel data were segmented into multiple time windows to determine the optimal signal duration for P-wave detection. The time window length was systematically varied from 20 s to 40 s, with a step size of 2 s.  
Figure 9 presents the relationship between the accuracy of the P-wave detection and the length of the time window. The blue dashed line represents the accuracy trend as a function of the time window size, while the blue markers indicate empirical values obtained from experiments. The red marker highlights the optimal time window length, which yields the highest detection accuracy. The optimal time window length identified in this study is 38 s. This value provides the best balance between signal completeness and P-wave classification precision, allowing the model to minimize detection errors effectively. Following the selection of the optimal time window, a receiver operating characteristic (ROC) curve (Figure 10) was generated to evaluate the performance of the binary classifier.