Earthquake Classification with Deep Learning Across Different Subduction Zones: Chile, Japan, and Mexico

Chile, Japan, and Mexico are among the world’s most seismically active regions, each representing a distinct subduction setting. This work extends prior studies on earthquake classification and evaluates the effectiveness of sequence-based methods across these different subduction zones. We frame the task as classifying events into foreshock, mainshock, and aftershock classes. Our pipeline comprises three stages: first, spatio-temporal clustering to group related seismic events; second, labeling each event to identify the mainshock and assign foreshock/aftershock roles; and finally, sequence classification using deep learning models, including Long Short-Term Memory (LSTM), Transformer, and Spatio-Temporal Transformer (STTN) architectures. Our results across the three regions show that the approach is effective: mainshock detection is consistently strong, aftershock classification achieves intermediate performance, and foreshocks remain the most challenging class. Among the tested architectures, the Transformer model exhibits the most consistent and competitive performance across regions. These findings underscore the effectiveness of the approach across distinct subduction zones while highlighting persistent challenges in foreshock recognition and opportunities for further improvement.