Enhanced source discrimination between tectonic earthquakes and quarry blasts via a hybrid CNN-GNN trained with multiple stations

Summary

Accurate seismic event discrimination has significant scientific and societal implications. While deep learning shows great promise for automatic feature extraction and classification, most existing models process seismograms from either a single station or a fixed number of stations. However, single-station approaches cannot exploit correlations between signals from different stations, while models that require a fixed number of stations lack the flexibility to adapt to networks with varying station counts. This study aims to enhance classification performance by utilizing seismograms from multiple stations while maintaining flexibility regarding the number of stations used. Each event is represented as a graph, where nodes correspond to stations with spectrogram features. To process these graph-structured data, we develop a hybrid model combining a convolutional neural network (CNN) and a graph neural network (GNN). The CNN first extracts features from individual spectrograms, and the GNN subsequently integrates these features across all nodes to perform the final classification. The model was trained on earthquakes and quarry explosions from Utah. We also compare our model’s performance with that of CNN classifiers, which employ post-processing strategies to derive the final event type. Results show that our model outperforms the CNN across all evaluation metrics. In summary, the model improves classification performance through effective multi-station aggregation while also providing flexibility regarding the number of input stations.