# Research on Seismic Signal Analysis Based on Machine Learning
## 4 Results  
In this paper, we proposed a novel model \"LCL-SSS\" which uses supervised and unsupervised machine learning methods to deal with the automatic classification of noise, natural earthquake events, and blast events. To facilitate the comparison of the computational efficiency of each model, all our experiments were performed in a Titan rtx gpu environment with 30 iterations. From the training results (Table 2), the overall effect of supervised learning was much better than that of the unsupervised method(Figure 3). Different training models have certain differences in recognition effect and calculation time. With the increase in training time, the generalization ability of different models to data roughly converged. From the average value of 30 times, ResNet-18 had the highest accuracy rate, reaching 0.9560, and the MiniRocket method had the second accuracy rate of 0.9499. In addition, recall and F1 indexes were the highest in MiniRocket, which were 0.9496 and 0.9493, respectively. With excellent classification accuracy, MiniRocket took the least time to calculate among all supervised models. With 30 iterations of all supervised models, the calculation speed of the MiniRocket method was 2.5-3.7 times that of the other three methods. Without relying on the label, the accuracy of using the K-means method directly was only 0.3953. It is noteworthy that the performance of our MiniRocket with unsupervised clustering methods was comparable to the supervised methods and surpassed the vanilla K-means method by a large margin, suggesting the effectiveness of our collaborative learning. The collaborative learning methods MiniRocket + K-means and MiniRocket + cluster head proposed in this paper achieved accuracy rates of 0.7643 and 0.8458, respectively. From the calculation efficiency of the unsupervised method, the calculation time of LCL-SSS method was 246 s, and the calculation time was too long. In order to check the stability of various methods, we calculated the classification stability of various methods. The specific method was to calculate the difference between the three indexes of each iteration and their respective median values. The standard deviation of these differences (referred to as deviation in this paper) was used to measure the stability of the method. The final results are shown in Figure 4 and Table 3. Among the supervised classification methods, MiniRocket achieved the best results in all three indicators. The accuracy variances of the ResNet-18 and the MiniRocket models were 0.0592 and 0.0207, respectively. In addition, the deviation of the MiniRocket method in recall and F1 was much smaller than ResNet-18, only about 1/6 of it. In unsupervised methods, K-means had the smallest classification deviation, but its accuracy was too low. The method proposed in this paper achieved 0.0097, 0.0181, and 0.0187, respectively, in the three indicators, ranking the second in all methods, second only to k-means. The above results show that the three methods based on MiniRocket achieved good stability. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Category** & **Accuracy** & **Recall** & **F1** & **Label Dependency** & **Calculation Time (s)** \\ \hline Inception10 & Supervised & 0.9441 & 0.9487 & 0.9457 & Yes & 155 \\ VGG16 & Supervised & 0.9214 & 0.9149 & 0.9039 & Yes & 228 \\ ResNet-18 & Supervised & **0.9560** & 0.9425 & 0.9377 & Yes & 211 \\ MiniRocket & Supervised & 0.9499 & **0.9496** & **0.9493** & Yes & **62** \\ K-means & Unsupervised & 0.3953 & 0.4108 & 0.3908 & **No** & 43 \\ MiniRocket & Collaborative & & & & & \\ + K-means & learning & 0.7643 & 0.6791 & 0.6591 & **No** & **100** \\ LCL-SSS & Collaborative & & & & & \\ (ours) & learning & **0.8458** & **0.8140** & **0.8143** & **No** & 246 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of different methods. Figure 4: Deviation of each index of each iteration from the median value. (**a–c**) represent the accuracy, recall, and F1 values for different models in each epoch.