In this paper, the characteristics of seismic wave data are extracted mainly through windowed Fourier transform and power spectrum sample entropy. A support vector machine classification model and random forest regression model are respectively established to classify seismic events and predict the grade of the earthquake. The results showed that for earthquake discrimination, the accuracy of the test set reached 82.7%; For grade prediction, the MAE reached 0.58. Therefore, classification support vector machine and regression random forest can be used for earthquake identification and grade prediction. Additionally, and the power spectrum sample entropy of the waveform signal can be used to measure the characteristics of the waveform. The earthquake discrimination and magnitude prediction methods based on Fourier power spectrum sample entropy and machine learning algorithms have potential applications in the field of earthquake monitoring and early warning. These research results provide a feasible technical tool for earthquake-related decision-making and help improve the prediction and response capability of earthquake hazards. However, further research and validation are still needed to further improve and optimize the performance and stability of the method.
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