Abstract Debris flows, characterised by their destructive potential and swift, unpredictable movement, pose significant hazards to mountainous regions and communities worldwide. Accurate and real-time monitoring of these flows is essential for risk mitigation and disaster prevention. In recent years, distributed acoustic sensing (DAS) technology, which uses fibre-optic sensors as a cost-effective and sensitive tool for detecting ground vibrations at several spatial locations, has emerged as a promising approach for continuous large-scale debris flow monitoring. This study illustrates a preliminary analysis of DAS applications for debris flow monitoring, focusing on synthetic data of debris flow signals. The proposed approach involves generating a synthetic database of signals that mimic the waveforms typically associated with debris flow events, followed by applying signal processing techniques to filter and analyse such waveforms. Initial results suggest that simple signal processing methods can help to discriminate between debris flow events and background noise, achieving a good detection accuracy even at fairly low signal-to-noise ratios. While further improvements of data processing algorithms are required to minimize detection errors, these findings indicate that DAS-based solutions have substantial potential for real-time applications and may serve as a scalable, minimally invasive tool for debris flow monitoring.
Poursalimi et al. (Tue,) studied this question.