Dual-Branch Adaptive Joint Decision-Making: Intelligent Identification of Seismic Events Supported by Single-Component Waveform Data from Multiple Global Regions

Abstract The classification of seismic event types is a fundamental prerequisite for reliable monitoring of seismic activity and for scientific assessment of earthquake hazards. However, efficiently and accurately distinguishing between natural earthquakes and artificial blast events based on seismic observation data from stations remains a key research topic with both challenges and value in the fields of seismology and signal processing. To address this issue, this article proposes a seismic event classification and prediction algorithm based on a deep learning network. The algorithm adopts a dual-branch joint decision-making strategy, taking single-channel waveform data with simple preprocessing as an input. During the model inference process, feature extraction and prediction are performed separately on the waveform data and its corresponding time–frequency matrix. In addition, a weight learning subnetwork is designed to adaptively adjust the weight proportion of the prediction results from the two branches in the overall decision-making, thereby ensuring the stability and accuracy of the classification and prediction results. Experimental test results show that the application process of the algorithm is simple, without relying on extensive manual parameter tuning, and it exhibits good generalization performance in seismic data sets from different observation instruments and different regions.