# Research on Seismic Signal Analysis Based on Machine Learning
## 5 Discussion  
All the methods used in this paper underwent 30 epochs, and the average value of 30 iterations was taken as the basis for identifying the ability of the method. Finally, in terms of the robustness of the model, ResNet-18 and MiniRocket had the highest robustness. Except that the accuracy of the MiniRocket method was slightly lower than ResNet-18, it was better than ResNet-18 in recall, F1, and variance. Considering the classification accuracy and calculation time of the model, the MiniRocket model, as a neural network with both depth and width, had a better effect. It showed that the model can overcome the complexity of seismic waveform characteristics while calculating efficiently and has a good application prospect in the study of seismic waveform classification. The accuracy of the unsupervised classification method K-means was lower than 0.4, so it cannot be used in practical research, although it was the most efficient in label-independent methods. The main reason for the poor performance of K-means calculation is that the key features in different event waveforms were not fully extracted. The MiniRocket method can also be used to extract the characteristics of seismic waveforms and then combined with other clustering methods, such as K-means and cluster head. In order to directly extract the decisive features in waveforms, we tried the method of collaborative learning between MiniRocket and unsupervised classification. Firstly, we tried the MiniRocket + K-means method, which directly improved the accuracy to 0.7643, but the values of recall and F1 were relatively low. Since the simple application of K-means clustering leads to the decline in clustering performance, this paper also proposed the MiniRocket + cluster head method. This combined method greatly improved the accuracy of unsupervised classification. The accuracy of the MiniRocket + cluster head method was 84.5%, which was more than two times higher than that of the pure K-means method. Therefore, through this study, a new idea is given for the classification of different earthquake types, and the proposed unsupervised classification method also has a certain application prospect in the identification of unnatural earthquakes in other regions. The classification effect of collaborative learning was further improved, and the variance was minimal, indicating that the model has a certain robustness. Since MiniRocket has the function of feature extraction, this allowed us to visualize the distance in the feature space of different types of seismic events. For this purpose, we used the t-sne method to reduce the dimension of data to two dimensions for visualization. In Figure 5, there are three types of data ground truth labels; visualization results of the feature interval of the supervised MiniRocket method; visualization results of the feature interval of the MiniRocket + K-means method; visualization results of the feature interval of the MiniRocket + cluster head method. The characteristic distance of noise events is far from that of blast and seismic events, while the characteristic distance of blast and seismic events is very close, so it is difficult to distinguish. Although the MiniRocket + cluster head method has some disadvantages in noise identification, it has greater advantages over the MiniRocket + K-means method in distinguishing difficult blast and seismic events.