Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Abstract Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine-learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine-learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density-based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image-derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle-related labels compared to the camera-derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short-term average/long-term average method and k-means clustering. Our results suggest that seismic data, when analyzed with machine-learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.