Integration of seismic and image data processing for rockfall monitoring and early warning along transportation networks

Panagiotis Partsinevelos (), George Kritikakis, Nikos Economou, Zach Agioutantis, Achilleas Tripolitsiotis, Stelios Mertikas and Antonis Vafidis
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Panagiotis Partsinevelos: University Campus
George Kritikakis: University Campus
Nikos Economou: University Campus
Zach Agioutantis: University of Kentucky
Achilleas Tripolitsiotis: Space Geomatica Ltd
Stelios Mertikas: University Campus
Antonis Vafidis: University Campus

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2016, vol. 83, issue 1, No 10, 133-153

Abstract: Abstract The occurrence of rockfall incidents on the transportation network may cause injuries, and even casualties, as well as severe damage to infrastructure such as dwellings, railways, road corridors, etc. Passive protective measures (i.e., rockfall barriers, wire nets, etc.) are mainly deployed by operators of ground transport networks to minimize the impact of detrimental effects on these networks. In conjunction with these passive measures, active rockfall monitoring should ideally include the magnitude of each rockfall, its initial and final position, and the triggering mechanism that might have caused its detachment from the slope. In this work, the operational principle of a low-cost rockfall monitoring and alerting system is being presented. The system integrates measurements from a multi-channel seismograph and commercial cameras as the primary equipment for event detection. A series of algorithms analyze these measurements independently in order to reduce alarms originated by surrounding noise and sources other than rockfall events. The detection methodology employs two different sets of algorithms: Time–frequency analyses of the rockfall event’s seismic signature are performed using moving window pattern recognition algorithms, whereas image processing techniques are utilized to deliver object detection and localization. Training and validation of the proposed approach was performed through field tests that involved manually induced rockfall events and recording of sources (i.e., passing car, walking people) that may cause a false alarm. These validation tests revealed that the seismic monitoring algorithms produce a 4.17 % false alarm rate with an accuracy of 93 %. Finally, the results of a 34-day operational monitoring period are presented and the ability of the imaging system to identify and exclude false alarms is discussed. The entire processing cycle is 10–15 s. Thus, it can be considered as a near real-time system for early warning of rockfall events.

Keywords: Rockfall monitoring; Seismic signature; Multi-temporal imagery datasets; STA/LTA (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11069-016-2462-2

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