# Research on Seismic Signal Analysis Based on Machine Learning
## 6 Conclusions  
Through qualitative analysis of signal and data waveform characteristics, the event type can be directly determined. Combined with long-term experience accumulation in daily practical work, blast and natural earthquakes can be identified simply and efficiently to a certain extent. However, earthquake monitoring is long-term and real-time. It is a waste of a lot of manpower to rely on human experience to continuously judge the earthquake category. Moreover, there are certain differences in the personal experience level of earthquake analysis experts, resulting in different discrimination results caused by personnel changes in earthquake classification. If you want to find an efficient recognition criterion that can be directly applied to computer automatic recognition, it must be based on the quantitative determination of the characteristics of the event type (establishing the effective threshold of various criteria), and because the waveform record is a comprehensive reflection of a series of influences such as the source type, propagation path, and recording instruments and equipment, the determination of the final event type should be a comprehensive classification problem with multiple characteristics. All the information on the characteristics of the earthquake source is recorded in the seismic sensing signal waveform. The depth characteristics of natural earthquakes, explosions, and other waveforms were extracted by using the depth learning model, and a neural network model that can identify non-natural seismic events was established. When applied to the fields of earthquake monitoring, earthquake  
Figure 5: Visualization of feature extraction of three classification models based on MiniRocket. disaster prevention, and public services, it can further improve work efficiency, be used to respond to social concerns in time, and reduce disaster losses. In order to explore the effect of different depth learning models in the classification of earthquake, blast, and background noise, this paper used a variety of neural network structures to establish a classifier to identify the types of natural earthquake, earthquake, blast, and background noise. These classifiers were tested by using a single seismic waveform as a unit to comprehensively evaluate the actual effect of a convolutional neural network in the identification of seismic event types so as to provide a reference for the automatic identification of seismic event types. The final results are as follows:  
1. Among the supervised learning methods used in seismic classification, this paper attempted to use MiniRocket, a one-dimensional convolution model. It is relatively simple, does not need a complex and deep network structure, and can also achieve classification results close to or even surpassing those of the other three mainstream classification methods, with the highest computational efficiency. 2. The feature extraction of seismic waves was carried out through MiniRocket, and then the t-sne visualization method was used to compare the feature distances of three types of data: earthquake, blast, and background noise. It was found that the feature distances of earthquake and blast blend with each other and are difficult to distinguish. 3. In supervised learning, it is inevitable to make labels manually, which is heavy work, while unsupervised learning can classify sample data without prior information, that is, label making is not required. Our LCL-SSS combined two unsupervised classification methods, K-means and cluster head, and finally achieved an accuracy of nearly 80%. The method proposed in this paper provides a feasible reference scheme for the automatic classification of earthquake types and points out a new classification for the classification of seismic events in future seismic big data. Once the unsupervised method is established, the application of all algorithms in practice is very simple. Compared with the supervised method, there is no need to make labels, so the calculation cost is very low.