Seismic instruments placed outside of spatially extensive hazard zones can be used to rapidly sense a range of mass movements. However, it remains challenging to automatically detect specific events of interest. Benford's law, which states that first non-zero-digit of given datasets follow a specific probability distribution, can provide a computationally cheap approach to identifying anomalies in large datasets and potentially be used for event detection. Here, we select raw seismic signals to derive the first-digit distribution. The seismic signals generated by debris flows, landslides, lahars, and glacier-lake-outburst floods follow Benford's law, while those generated by ambient noise, rockfalls, and bedload transports do not. Focusing on debris flows, our Benford's-law-based detector is comparable to an existing random forest method for the Illgraben, Switzerland, but requires only single station data and three non-dimensional parameters. We suggest this computationally cheap, novel technique offers an alternative for event recognition and potentially for real-time warnings.

A document by Małgorzata Chmiel. Click on the document to view its contents.

Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Hence, understanding and continuously monitoring mass movements is crucial to mitigate their risk. We propose to use Distributed Acoustic Sensing (DAS) to measure strain rate along a fiber-optic cable to characterize ground deformation induced by avalanches. We recorded 12 snow avalanches of various dimensions at the Vallée de la Sionne test site in Switzerland, utilizing existing fiber-optic infrastructure and a DAS interrogation unit during the winter 2020/2021. By training a Bayesian Gaussian Mixture Model, we automatically characterize and classify avalanche-induced ground deformations using physical properties extracted from the frequency-wavenumber and frequency-velocity domain of the DAS recordings. The resulting model can estimate the probability of avalanches in the DAS data and is able to differentiate between the avalanche-generated seismic near-field, the seismo-acoustic far-field and the mass movement propagating on top of the fiber. By analyzing the mass-movement propagation signals, we are able to identify group velocity packages within an avalanche that propagate faster than the phase velocity of the avalanche front, indicating complex internal structures. Importantly, we show that the seismo-acoustic far-field can be detected before the avalanche reaches the fiber-optic array, highlighting DAS as a potential research and early warning tool for hazardous mass movements.

We present a seismo-acoustic analysis of the debris-flow activity between 2017 and 2019 at the Illgraben catchment (Switzerland). To understand fluid dynamic processes involved in the seismo-acoustic energy generation by debris-flows, seismic and acoustic amplitudes (maximum root mean square amplitude, RMSA) and peak frequencies are compared with flow measurements (front velocity, maximum flow depth and density). Front velocity, maximum depth, peak discharge and peak mass flux show a positive correlation with both infrasonic and seismic maximum RMSA, suggesting that seismo-acoustic radiation is controlled by these flow parameters. Comparison between seismo-acoustic peak frequencies and flow parameters reveal that, unlike seismic signals, characterized by a constant peak frequency regardless of the magnitude of the flow, infrasound peak frequency decreases with increasing flow velocity, depth and discharge. Based on all collected evidence, we suggest that infrasound signals of debris-flows are generated by flow waves and water splashes that develop at the free-surface of the flow, whose dimension scales with flow magnitude. According to fluid dynamics, such surface oscillations are mostly generated wherever the flow encounters significant channel irregularities, such as topographic steps and planform steep bends, that therefore likely constitute preferential sources of infrasound. As for seismic waves, results are consistent with previous theoretical models and field observations, which attribute debris-flow seismicity to solid particle collisions, friction and fluid dynamic structures. Finally, the observed positive correlations between seismo-acoustic signal features and flow parameters highlight the potential to use infrasound and seismic measurements for debris-flow monitoring and risk management.