Preprints
https://doi.org/10.5194/egusphere-2026-4460
https://doi.org/10.5194/egusphere-2026-4460
21 Aug 2026
 | 21 Aug 2026
Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

Detecting and discriminating rockfall signals from anthropogenic noise using Distributed Acoustic Sensing along a Norwegian railway

Robin André Rørstadbotnen, Kasper Hunnestad, Khanh Truoung, Martin Landrø, Jo Eidsvik, Aadne Ingvald Austigar Pettersen, and Rune Brannfjell

Abstract. In mountainous regions, railway lines frequently traverse terrain with elevated rockfall and avalanche risk. In recent years, several such events have struck railways, resulting in casualties and significant infrastructure damage. Continuous monitoring along railway corridors is therefore essential for early warning and preventive measures. Given that fibre-optic telecommunications cables are already installed along many railways, Distributed Acoustic Sensing (DAS) represents an operationally attractive solution for continuous, large-scale rockfall monitoring. Avalanches and rockfalls release seismic energy as they propagate, which can be recorded by seismic sensors installed along the railway. Here, we use a fibre-optic cable running parallel to a railway in Norway to acquire DAS data and characterise the seismic signatures of rockfall events. The DAS system also recorded a range of anthropogenic signals, including those from road vehicles and trains, and the frequency distribution are found to be distinctly different from rockfalls. Rockfall signals are characterised by higher dominant frequencies and apparent propagation velocities than anthropogenic noise sources. Furthermore, moving vehicles generate persistent low-frequency energy that is absent in rockfall signals, providing an additional basis for discrimination. In addition to natural rockfall events, we carried out controlled rockfall experiments in which rocks of various sizes were dropped along the railway. The smallest rockfall detected had a mass of approximately 50 kg and was observable over approximately 400 m of fibre, whereas the largest boulder could be observed over several kilometres. Combined, these characteristics indicate that rockfalls can be effectively discriminated from anthropogenic signals, forming a promising foundation for the development of automated detection algorithms using cost-efficient DAS system.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Robin André Rørstadbotnen, Kasper Hunnestad, Khanh Truoung, Martin Landrø, Jo Eidsvik, Aadne Ingvald Austigar Pettersen, and Rune Brannfjell

Status: open (until 02 Oct 2026)

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Robin André Rørstadbotnen, Kasper Hunnestad, Khanh Truoung, Martin Landrø, Jo Eidsvik, Aadne Ingvald Austigar Pettersen, and Rune Brannfjell
Robin André Rørstadbotnen, Kasper Hunnestad, Khanh Truoung, Martin Landrø, Jo Eidsvik, Aadne Ingvald Austigar Pettersen, and Rune Brannfjell
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Latest update: 21 Aug 2026
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Short summary
Railway lines in mountainous regions face serious risks from rockfalls, which can derail trains and damage infrastructure. We used fibre cables along a Norwegian railway to detect rockfalls using Distributed Acoustic Sensing, a technology turning cables into thousands of sensors. Comparing rockfall signals with those from vehicles and trains revealed differences in frequency and wave speed. Rocks as small as 50 kilograms were detected, laying the groundwork for future automated warning systems.
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