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.

Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi-supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million m3 on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for ground-truth labeling. The proposed algorithm is capable of distinguishing between rock-slope failures and background noise, including road and train traffic, with a detection precision of over 90%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal-to-noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

Machine learning can improve the accuracy of identifying mass movements in seismic signals and extend early warning times. However, we lack a profound understanding of the effective seismic features and the limitations of different machine learning models, especially for debris-flow warning. Here, we investigate the importance of seismic features for the binary debris flow classification tasks using two ensemble models: Random Forest (RF) and eXtreme Gradient Boosting (XGB) models. We find that an established approach to training machine learning models for debris flow classification task based on more than seventy seismic signal features may be affected by redundant input information. These seismic features are derived from physical and statistical knowledge of impact sources and are grouped into waveform, spectrum, spectrogram, and network sets. Our results show that only six selected seismic features can perform similarly for the binary debris flow classification task compared to published benchmark results trained with seventy features. Considering models that aim to capture patterns in sequential data rather than focusing on information only in one given window as ensemble models, using the Long Short-Term Memory (LSTM) algorithm does not improve the performance of binary debris flow classification tasks over RF and XGB. As a debris flow alarm task, the LSTM model predicts debris flow initiation more consistently and generates fewer false warnings. Our proposed framework simplifies seismic signal-driven early warning for debris flows and provides an appropriate workflow for identifying other mass movements.

One major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Events of interest potentially stay unnoticed and remain unanalyzed, particularly because conventional sensors cannot monitor an entire glacier. However, with Distributed Acoustic Sensing (DAS), we can observe seismicity over multiple kilometers. DAS systems turn common fiber-optic cables into seismic arrays that measure strain rate data, enabling researchers to acquire seismic data in hard-to-access areas with high spatial and temporal resolution. We deployed a DAS system on Rhonegletscher, Switzerland, using a 9 km long fiber-optic cable that covered the entire glacier, from its accumulation to its ablation zone, recording seismicity for one month. The highly active and dynamic cryospheric environment, in combination with poor coupling, resulted in DAS data characterized by a low Signal-to-Noise Ratio (SNR) compared to classical point sensors. Our objective is to effectively denoise this dataset. We use a self-supervised J-invariant U-net autoencoder capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest (e.g., stick-slip or crevasse signals). The method shows enhanced inter-channel coherence, increased SNR, and significantly improved visibility of the icequakes. Further, we compare different training data types varying in recording position, wavefield component, and waveform diversity. Our approach has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.

Debris flows represent a major hazard in mountain environments. Recently, the use of seismo-acoustic signals to study and monitor debris flows has gained attention. Debris flows, indeed, generate both seismic and infrasonic waves, whose source mechanisms are not yet completely clear. We present the seismo-acoustic analysis of a debris-flow event at Illgraben (Switzerland). The array processing of infrasonic data reveals that coherent infrasound is preferentially radiated at channel irregularities, acting as predominant acoustic sources because of the intense flow turbulence. The high cross-correlation observed between the infrasonic and seismic signals during the event suggests that a significant component of the seismic signal is produced by the turbulent flow and correlates with infrasound. Our work defines additional constraints on the seismo-acoustic radiation by debris flows and highlights how infrasonic and seismic signals can be used to detect and track events in near real time.

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.