Seismic Waveform Classification Based on Multi-classification Model Weighted Voting Method

Abstract

The earthquake event is caused by the activity of the interior earth, which has the potential and can create great damage. Thus, in big data era, it is necessary to fast, efficient and universal automatic recognition and classification of seismic waveform. A well-performed recognition and classification algorithm should be sensitive to small and weak events with various waveform shapes, robust to background noise and non-seismic signals, and effective for processing a large number of data in different areas. In this paper, we introduce the weighted voting method in ensemble learning, taking 50000 seismic signals monitored by the capital circle and its nearby stations as training and testing data, we learn the main time-frequency characteristics of seismic positions in the three-component seismic signal data through the sub-model, and then identify and classify them. Finally, the classification score of the sub-model is weighted to confirm the final event type. Compared with single machine learning models, our result shows improved recognition and classification performance and reduced uncertainty of ensembles.