Tracking and classifying objects with DAS data along railway

• Published in: Journal of applied geophysics Vol. 242; p. 105900
• Main Authors: Fredriksen, Simon Leander Berg, Mai, The Tien, Growe, Kevin, Eidsvik, Jo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2025
• Subjects: Classification; DBSCAN; Detection; Distributed acoustic sensing (DAS); JPDA; Target tracking; JPDA; DBSCAN; Target tracking; Detection; Distributed acoustic sensing (DAS); Classification
• ISSN: 0926-9851
• DOI: 10.1016/j.jappgeo.2025.105900

Using distributed acoustic sensing data from a day of field testing on a fiber-optic cable along a railroad track in Norway, we detect and track cars and trains moving along a segment of the cable where the road runs parallel to the railroad tracks. We develop a method for automatic detection of events using signal processing, thresholding and density-based clustering, and then put data picks into a Kalman filter variant known as joint probabilistic data association filter for multiple object tracking and classification. Statistical model parameters are specified using in-situ labeling data along with the fiber-optic signals. Running the algorithm over time, we automatically track about 100 cars and 20 trains per hour. The velocities of cars coming from a zone with higher speed limit tend to be larger (35 km/h) than that of cars going in the opposite direction (30 km/h). •Combining geophysics and machine learning techniques to detect DAS events.•State estimation to track and classify cars and trains along a fiber-optic cable.•Results on a distributed acoustic sensing data set from Trondheim, Norway.