Abstract

Chile is among the world's most seismically active countries, with an annual average of over 1,000 seismic events exceeding moment magnitude (Mw) 4.0. In the past 20 years, the country has experienced two major events surpassing 8.0Mw. While deep neural network models have been widely employed to detect patterns in seismic data, the classification of seismic events into foreshocks, mainshocks, and aftershocks remains a challenging task. This study proposes a hybrid approach for the classification of earthquakes in Chile. The methodology comprises three main steps: first, a spatio-temporal density-based clustering algorithm is applied to group seismic events based on their spatial and temporal similarities; second, the seismic events within each cluster are labeled as foreshocks, mainshocks, or aftershocks; and finally, deep neural networks, including Long Short-Term Memory (LSTM) and Transformer models, are employed to classify earthquakes. Features such as longitude, latitude, magnitude, depth, and distances between events are used as inputs. For aftershock classification, the LSTM model achieves the highest accuracy at 0.8. Meanwhile, for precursor event classification, the Transformer network outperforms the LSTM, achieving a recall of 0.6. Future work will focus on a more detailed exploration of the precursor class and the incorporation of additional seismic data from other countries to enhance the model's generalization.