Pattern-of-Life Activity Recognition In Seismic Data

Pattern-of-life analysis models the observable activities associated with a particular entity or location over time. Automatically finding and separating these activities from noise and other background activity presents a technical challenge for a variety of data types and sources. This paper investigates a framework for finding and separating a variety of vehicle activities recorded using seismic sensors situated around a construction site. Our approach breaks the seismic waveform into segments, preprocesses them, and extracts features from each. We then apply feature scaling and dimensionality reduction algorithms before clustering and visualizing the data. Results suggest that the approach effectively separates the use of certain vehicle types and reveals interesting distributions in the data. Our reliance on unsupervised machine learning algorithms suggests that the approach can generalize to other data sources and monitoring contexts. We conclude by discussing limitations and future work.