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

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

doi:10.5194/egusphere-2026-4460

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https://doi.org/10.5194/egusphere-2026-4460

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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.

Received: 24 Jul 2026 – Discussion started: 21 Aug 2026

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

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RC1:
'Comment on egusphere-2026-4460', Anonymous Referee #1, 01 Sep 2026 reply
The manuscript presents an interesting application of Distributed Acoustic Sensing (DAS) to rockfall-induced seismic events and anthropogenic events. The study applied processing and analysing approaches on both experimental and observed rockfall events. In general, I think this study has the potential for publication and is relevant to NHESS. However, several important aspects require further clarification, some key information needs to be added to the manuscript, and some interpretations require further discussion and explanation.
My comments are listed below:
Given the long distance of fibre (~98km), it would be useful to have additional information on the DAS acquisition. For example, I was wondering about the optical setup, and whether the optical amplification or repeaters were used. It would be also very useful to clarify if the signal quality varies with distance from the interrogator. For example, did the authors check the signal-to-noise ratio or sensitivity along different sections of the fibre, especially near the far end of the 98 km fibre? If this variability exists, could the detectability of rockfalls and other events be affected?

Lines 83-84: it is unclear to me how the tap tests were used to identify the sections with higher avalanche risk. What observable metrics were derived from the tap test and used for this assessment?

How were the geological effects from the near surface materials distinguished from the effects from fibre coupling and fibre orientation, etc.? How confident can we be that the main cause of the spatial variation in recorded amplitude is near-surface geological factors?

The subsection “data processing” is too short. A lot of methodological details are missing from this paragraph. Was the low-pass filter applied before the downsampling (for avoiding aliasing)? Why were the 5-70 Hz and 0.15-2 Hz frequency bands selected for the events of avalanches and traffic? Are these frequency bands based on spectral characteristics observed in the data or previous studies? What is the Butterworth filter order and did you apply the zero-phase filtering? Please better specify the preprocessing procedure and organize the section.

The hyperbolic-fitting workflow would need a more complete description. Why is the 5-50 Hz band used here but not the 5-70 Hz used in the general processing? It would be helpful to indicate how these processing parameters were selected.

It is not always clear which processing steps and criteria are applied to which types of events (e.g., controlled cases, natural rockfall, avalanches, or other detected signals). Some of this important information appears at different points throughout the text, which makes the overall processing workflow difficult to follow. I therefore suggest reorganizing the part of “Methods and signal classification”.

The reduced surface-waves amplitudes observed along the tunnel sections are attributed to the absence of surficial deposits beneath the fibre (around Line 140). However, DAS amplitudes can also be strongly influenced by fibre coupling, depth, orientation, and other installation conditions. Could the authors discuss these alternative factors and provide more explanation?

It is unclear for me how the predicted curves in Figure 7b and 7d were calculated and how were they compared with the observations quantitatively. For example, how was the amplitude scaling factor (A0) determined for the prediction curves? How was the best-fitting function identified (e.g., least-squares fitting)?

Lines 232-235: It may require more justification for the link between the unclear seismic phases in the f-k plot of the nature rockfall to the near-surface geology. In particular, natural rockfalls may represent more complex and spatially distributed sources than the controlled experiment through multiple impacts, rolling, etc. In addition, spatial variations in DAS coupling or sensitivity can also influence the f-k representation. Could the authors provide more evidence or explanation to support near-surface geology as the dominant cause?

The apparent velocities reported in the manuscript appear to be plausible, however, the procedure used to estimate them (and also their associated uncertainties) requires further clarification. In the manuscript, it is mentioned that the hyperbolas were manually overlaid on the data to determine the apparent velocities (~Line 263). However, how was the best-fitting velocity selected? What uncertainty is associated with these choices?

It is also mentioned in the manuscript that the difference between the fitted velocity and the manually estimated velocity for the controlled single rock-drop experiment (Table 1) reveals a limitation of the fitting procedure, despite the very low MAE. Could uncertainty estimates, pick-consistency criteria, or other quality metrics for the inferred velocities be provided to help strengthen the analysis?

Line 88: it may help if the author can provide the actual length of the Robel maintenance vehicle.

Figure 2: was the normalization or scaling applied when plotting? Please clarify the quantity plotted here.

Figure 10: it is difficult to interpret the figure in the current form and combination. It is difficult for me to understand why the events in panels (a) and (d) are presented differently from those in panels (b), (c), (e) and (f). Please mention the reason for using different representations, or simply presenting them in a more consistent way. Furthermore, additional information is needed to better understand the figure. Please clarify whether the quantity has been normalized, what is “c” in panels (b,c,e,f) and “d” in panels (a,d), what is represented by the colorbar at the right of (c) and (f), and what is “Value” 1 to 0.

A more detailed caption would improve the readability of the figure.

Several figure captions do not provide sufficient information to interpret the figures independently. I recommend that the authors revise the captions throughout the manuscript.

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Citation: https://doi.org/10.5194/egusphere-2026-4460-RC1

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

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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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