| Summary: | In this paper, the time series classification frontier method MiniRocket was used to classify earthquakes, blasts, and background noise. From supervised to unsupervised classification, a comprehensive analysis was carried out, and finally, the supervised method achieved excellent results. The relatively simple model, MiniRocket, is only a one-dimensional convolutional neural network structure which has achieved the best comprehensive results, and its computational efficiency is far stronger than other supervised classification methods. Through our experimental results, we found that the MiniRocket model could well-extract the decisive features of the seismic sensing signal. In order to try to eliminate the tedious work of making data labels, we proposed a novel lightweight collaborative learning for seismic sensing signals (LCL-SSS) based on the method of feature extraction in MiniRocket combined with unsupervised classification. The new method gives new vitality to the unsupervised classification method that could not be used originally and opens up a new path for the unsupervised classification of seismic sensing signals.
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