Detection and discrimination of seismic events have important implications. Precise detection of earthquakes may help prevent collateral damage and even save lives. On the other hand, the ability to identify explosions reliably not only helps prevent false alarms but also is crucial for monitoring nuclear experiments. In this work, we present a method for automatic discrimination of seismic events. A neural network is trained to fuse information from multiple seismic channels into a correlated space. Then, we augment the minority class to avoid class imbalance. Finally, a tree-based classifier is used to estimate the nature of the suspected event. We apply the proposed approach to 1609 events collected in Israel and Jordan. Our framework demonstrates improved precision and recall scores.
- data augmentation
- deep canonical correlation analysis (DCCA)
- seismic discrimination