No abstract

The classification of seismic events is crucial for monitoring underground nuclear explosions and regional unnatural seismic events. To classify tectonic earthquakes, explosions, and mining-induced earthquakes, we established 36- and 60-dimensional network-averaged datasets and single-station datasets through feature extraction and spectral amplitude analysis. Using different artificial intelligence (AI) methods, including the support vector machine (SVM), extreme gradient boosting (XGBoost), long short-term memory network (LSTM), residual neural network, and long short-term memory fully convolutional network (LSTM-FCN), we constructed two-class and three-class models, analyzed the change in the classification with epicentral distances, and evaluated the generalizability of different classifiers. The results showed that the accuracies of different AI models with the feature extraction dataset were higher than those achieved with the spectral amplitude dataset, indicating that the feature extraction method can more clearly highlight the differences between different types of seismic events. The accuracies with the network-averaged dataset were 5%–8% higher than that achieved using the single-station dataset. The earthquake and mining-induced earthquake classifiers constructed by different AI methods had the best performance, followed by the earthquake and explosion classifier, and the explosion and mining-induced earthquake classifier, with average accuracies of 97.4%–98.4%, 96.5%–97.6%, and 88.8%–90.6%, respectively. In the model generalization evaluation, the test accuracies and F1-Scores of the two-class models with the 36-dimensional network-averaged dataset exceeded 90%. Among the five AI methods, XGBoost and LSTM both performed well in classification of different datasets, indicating that these models have good application prospects for seismic event classifications.