Machine learning-based classification of seismic events: a case study of seismic events in Jilin Province, NE China

Introduction The rapid and accurate identification of natural and non-natural seismic events is crucial for compiling comprehensive earthquake catalogs and assessing regional seismic risk. Methods This study utilized waveform data from seismic events (1.5 ≤ ML ≤ 3.5) recorded in Jilin Province between 2013 and 2024. Multiple physical features were extracted from the waveforms, and Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Backpropagation Neural Network (BPNN) algorithms were employed to classify artificial blasts, mining collapses, and tectonic earthquakes. Results The study shows that all three models achieved over 94% accuracy in regional tests, with the SVM model performing best. In cross-regional validation, SVM maintained superior generalization capability, achieving an accuracy of 84%. Feature importance analysis confirmed that P-/S-wave spectrum amplitude and maximum P/S amplitude ratio served as the most critical features for event discrimination. Discussion This study validates the effectiveness of the selected features and machine learning methods for identifying low-magnitude multi-type seismic events; however, for events with strong regional characteristics, further development of cross-regional datasets or region-specific models is necessary to enhance classification accuracy.