Debris flows are among the most destructive geohazards in alpine regions. Within minutes, hundreds of thousands of cubic meters of water, sediments, and rocks may discharge in an uncontrolled way at velocities exceeding 5 m/s. Seismic monitoring offers perspectives for detection and warning, and thus for protecting human lives and infrastructure. Distributed Acoustic Sensing (DAS) is a new alternative to conventional seismic sensors and can be applied to pre-existing telecommunication fibers repurposed as seismic sensors. With the high sensitivity to ground displacement and the distributed nature of DAS measurements, this approach allows detection and location of debris flows kilometers upstream of affected regions, thus maximizing warning times.Between September and August 2022, we interrogated a 450-meter-long telecommunication fiber in the municipality of Susten, located on Illgraben’s debris cone in Switzerland’s Rhône valley. Illgraben is among Europe’s most active debris flow catchments, producing 2-10 debris flows per year (Badoux et al., 2009). One event was recorded on 8 September 2022, with first signals registered by the DAS system 20 minutes before the debris flow reached the village of Susten. At that time, the debris flow was still located 4 km upstream in the Illgraben catchment, demonstrating the early warning capabilities of DAS.In a second DAS investigation between 2024 and 2026, a 2-kilometer-long fiberoptic cable was trenched along the Illgraben channel, only tens of meters away from the torrent bed. Such near-torrent observations illuminate the interaction of the debris flow material with the torrent bed and enable us to better understand the seismogenesis of debris flows. The strongest signals are observed at the boulder-rich debris flow front. Using DAS, such moving sources can be tracked along the torrent, and their velocity can be estimated. During later flow stages, the bulk composition changes, and only fine-grained sediments are transported. During these flow stages, large boulders generate the strongest seismic signals. Their ground impacts can be located with the DAS system, elucidating boulder transport within debris flows and their contribution to the hazard potential.The 2-kilometer-long fiber also resolved surge fronts and roll waves within several debris flows. Such unsteady flow features increase peak discharge and dynamic complexity, which contributes much to the hazard potential (Aaron et al., 2025). Our along-torrent DAS measurements capture the evolution of debris flow surges and roll waves. This provides unprecedented insights into their formation and propagation, which is essential to more accurate predictions of the destructive potential of surging debris flows.Badoux, A., et al. A debris-flow alarm system for the Alpine Illgraben catchment: design and performance. Nat Hazards 49, (2009). https://doi.org/10.1007/s11069-008-9303-xAaron, J., et al. Detailed observations reveal the genesis and dynamics of destructive debris-flow surges. Commun Earth Environ 6, (2025). https://doi.org/10.1038/s43247-025-02488-7

Monitoring glacier dynamics is essential for understanding climate change impacts, safeguarding water resources, and protecting communities from related hazard. Distributed acoustic sensing (DAS) provides a unique opportunity to observe these dynamic environments with high spatial and temporal resolution. However, creating comprehensive seismic event catalogs from DAS data requires the development of efficient, automated tools. In this study, we analyzed 17.6 TB of DAS data collected over 31 d from a 9 km fiber-optic cable deployed on Rhonegletscher, Switzerland. The cable recorded strain-rate data along 2225 channels, spanning both snow-covered and bare-ice regions. We developed a robust preprocessing pipeline to address challenges posed by noise, coupling inconsistencies, and large data volumes. Our feature extraction is based on covariance matrix analysis, which allowed us to characterize wavefield properties using the first eigenvalue, coherency function, and eigenvalue variance. We compared unsupervised and supervised approaches, evaluating their relative effectiveness in detecting cryoseismic events across noisy DAS datasets. Although unsupervised methods provided valuable insights into inherent data patterns, their performance was hindered by dataset imbalance and noise. In contrast, supervised methods trained on manually labeled data demonstrated higher classification accuracy and reliability, with random forest emerging as the top performer, achieving 81% cross-validation accuracy, 93% test accuracy using a random split, and 73% test accuracy when evaluated with a grouped split to avoid data leakage. By combining cloud and parallel computing, we develop a scalable framework that streamlines DAS data analysis and supports future operational monitoring and research on glacier dynamics. The resulting comprehensive catalog, comprising thousands of detected and classified cryoseismic events, provides a valuable resource for scientists, fostering advancements in cryosphere monitoring and hazard assessment within the context of a changing climate.

Alpine mass movements pose a considerable risk to people and infrastructure. Snow avalanches pose a particularly prominent risk due to their widespread occurrence and potential catastrophic consequences. While significant advances have been made over the last decades to forecast avalanches, the spatio-temporal conditions that lead to avalanche release remain elusive. In fact, the problem of avalanche release is comparable to earthquake nucleation: An earthquake rupture and the fracture of a snow slab avalanche share the same underlying physics. As in the case of earthquakes, the observation of the rupture process of avalanches is limited to sensors in the farfield, such as seismic and infrasound arrays, and optical and radar methods, as well as laboratory experiments. While laboratory observations, far-field measurements on experimental test sites, and numerical simulations allow us to paint an ever more precise picture of the physics of avalanche nucleation, in situ measurements of crack propagation in the near-field of a real-world avalanche remain inaccessible so far.How to perform such a measurement, which would not only allow us to understand the underlying physics better, but also might open new pathways to measuring precursory processes?We designed a field experiment tackling the in situ observation of crack propagation and precursory processes. Leveraging a dense grid of seismic sensors we aim to capture the deformation in the nearfield prior, during, and after avalanche nucleation with Distributed Acoustic Sensing (DAS). In total, more than 3 km of fibreoptic cable were pulled from the top into the the steep slopes of Brämabuel near Davos (Switzerland) in autumn 2025. The cables were installed prior and during the first significant snowfall of the season, on known avalanche release slopes. Hence, they are placed centimeters below the expected weak layers, thus effectively making them an embedded strain sensor in a real-world experiment. To increase the probability of capturing the nucleation process, our DAS interrogator continuously samples at 2 m and 200 Hz, with the possibility to increase sampling rates to 1000 Hz for periods of increased avalanche risk. This continuous high spatio-temporal sampling will allow us to differentiate naturally and human-triggered slabs; in fact, skiers are easily identified in the data. In this talk, we will report on the first measurements of the 2025/2026 season and highlight the monitoring potential of our installation

Snow avalanches are among the deadliest natural hazards in mountainous regions. Yet avalanche activity is often still documented manually, and accurate avalanche release times are mostly missing. Automated monitoring systems equipped with seismic and infrasound sensors, combined with detection algorithms, could help record avalanche occurrences and provide accurate data on release time, size, and type. This comprehensive data on avalanche activity is indispensable for improving and validating avalanche forecasts and for implementing mitigation measures. At the Vallée de la Sionne (VDLS) test site in Switzerland, a combination of avalanche monitoring systems has been deployed for over two decades, including radars, cameras, seismic and infrasound stations. Additionally, avalanche researchers have manually documented and verified most avalanche events over the past 14 winter seasons to compile a unique avalanche catalogue.To facilitate and automate avalanche detection, we aimed to implement two deep learning-based methods that scan continuous seismic and infrasound data separately in (near) real-time to detect and classify avalanche signals. Therefore, we leveraged the large volume of continuous data collected every winter at VDLS by adopting concepts from recent, powerful language models. Specifically, we pre-trained transformer networks in a self-supervised manner (i.e. without using expert labels) on a wide variety of signals mined from continuous seismic and infrasound data streams. The models receive fixed-length waveforms as input, partition them into sequential patches and compute patch-wise spectrograms. By training the models to reconstruct a portion of randomly masked patches, they learn to extract meaningful representations from the data, achieving silhouette scores of up to 0.6. This indicates good separability between avalanche and non-avalanche signals. Thus, these representations can later be used to automatically detect avalanches by fine-tuning a classifier on top. Moreover, combining predictions from the seismic and infrasound models has the potential to further improve (near) real-time avalanche detection.

The success of geological carbon storage (GCS) operations depends on the effective management of risks, such as induced seismicity. In this study, we consider prior assessments of induced seismicity risks to guide our Measurement, Monitoring, and Verification (MMV) program for a prospective CO2 injection test site near Tr & uuml;llikon, Switzerland. A diverse suite of geophysical analyses including fault identification, fault slip potential, fluid pressure modelling, seismic risk modelling, and traffic light design are synthesized into MMV performance targets, based on recent 'good practice' guidelines. These MMV performance targets are then used to guide our MMV optimization process. This optimization process indicates that installing seven new broadband stations would improve the magnitude-of-completeness from ML 0.9 to ML 0.0, while lateral and depth resolutions would be improved to 50-100 m (for a ML 1.0 earthquake). While these optimized designs are expected to meet our MMV performance targets, we also provide contingency plans in the case that our estimates are overly optimistic. This contingency largely considers the quantification of noise attenuation with depth, which we estimate to be between a 10-fold to 30-fold improvement, based on a temporary downhole fibre optic deployment. Overall, this study demonstrates how GCS risks can be embedded into MMV design; our workflow can thus serve as a template to guide future GCS MMV designs, long before CO2 injection operations commence.

From April until the end of June 2025, we deployed a dense seismic network of 271 three-component stations within an 8 km radius around Lavey-les-Bains, Switzerland, to investigate the structure of the country's hottest known natural geothermal system. The site hosts a 3 km-deep exploration well (Lavey-1), drilled in 2022, that revealed unexpectedly low flow rates despite temperatures exceeding 120 degrees C, prompting the suspension of the project. The site lies within the narrow Rh & ocirc;ne Valley, characterized by steep topography, strong lateral structural heterogeneity and elevated anthropogenic noise, complicating seismic imaging. The dense nodal array was complemented by a distributed acoustic sensing (DAS) system along a buried telecommunication cable, providing a hybrid data set suited for passive seismic imaging. We describe the network geometry, instrumentation and deployment logistics; assess data completeness and noise characteristics and present first examples of ambient noise and earthquake recordings. Preliminary analyses demonstrate a high data quality and spatial coverage. This experiment establishes a benchmark data set for developing advanced passive imaging techniques in complex Alpine environments.

Slope instabilities pose serious risks to infrastructure and communities in mountainous regions. Understanding their internal structure and time-dependent dynamics is vital for effective hazard assessment and mitigation. The Cuolm da Vi instability in central Switzerland, one of the largest slow-moving instabilities in the Alps, offers an ideal setting for field-based slope instability research. We present the motivation, design, and implementation of a novel large-scale multi-sensor seismic network to study the subsurface structure and deformation dynamics of Cuolm da Vi across an unprecedented range of spatial and temporal scales: from decimetres to kilometres and milliseconds to years. The sensor network includes a hexagonal grid of more than 1,000 seismic nodes primarily deployed for high-resolution 3D characterization. This temporary nodal array was complemented with a trenched 6.5km fibre-optic configuration, which covers the most unstable parts of Cuolm da Vi using a multi-directional cable layout, suited for Distributed Acoustic and Strain Sensing measurements (DAS & DSS). Data acquisition spanned two years so far, including controlled-source experiments and continuous seismic and strain sensing campaigns. Initial data screening demonstrates the network's potential to facilitate imaging of the internal structure and monitoring of seasonal subsurface instability processes. Our study shows the feasibility of dense long-term seismic monitoring in challenging Alpine terrain using nodal and distributed fibre-optic sensing techniques, opening new opportunities for slope instability research and hazard assessment.

Crevassing critically controls glacier stability. Crevasses can penetrate deep into a glacier or ice shelf, promoting calving, ice avalanches, and even sudden catastrophic ice shelf collapse. Yet quantifying subsurface fracture damage remains largely unquantified. Here, we show how distributed acoustic sensing technology can quantify subsurface fracture damage in unprecedented detail at an alpine glacier. We first demonstrate that seismic anisotropy can quantify fracture extent. We also study crevasse icequake failure mechanisms, which fail predominantly via tensile opening. Icequake-derived crevasse opening is consistent with anisotropy-derived estimates (∼8% of total ice volume), suggesting that damage is dominated by fracture rather than melt. These results establish a scalable approach for monitoring subsurface ice damage that complements existing satellite surface observations. Applications range from monitoring the stability of alpine glaciers that pose a risk to alpine communities to providing foundations for assessing subsurface fracture extent at globally pertinent ice sheets and ice shelves.

One major hurdle for understanding earthquake mechanics are observational limitations. Important phenomena like strain localisation, fault dilation, and fault healing are readily studied in rock mechanical laboratory experiments and with numerical models. At the scale of natural earthquakes, however, these phenomena are often unresolvable, even by state-of-the-art observatories. To overcome this limitation, we are currently building the Earthquake Physics Testbed at the Bedretto Underground Laboratory for Geosciences and Geoenergies (BedrettoLab), an experimental testbed where we can activate an extensively instrumented natural fault zone via hydraulic stimulation. The goal of the Fault Activation and Earthquake Rupture (FEAR) project is to induce earthquakes of up to Mw~1.0 on this exceptionally well characterised and instrumented fault zone. Here we summarize the main scientific goals and current FEAR project status, and present first results from conducted experiments. We discuss how this large-scale experimental approach may allow us to tackle both fundamental science as well as practical questions on earthquake physics, induced seismicity and seismic hazard.

Ensuring caprock integrity is essential for the safety of long-term CO2 storage, particularly in faulted formations. As part of the Carbon Sequestration, Series-E (CS-E) experiment at the Mont Terri Rock Laboratory, we acquired new cross-hole seismic data using both cemented geophones and fibre-optic Distributed Acoustic Sensing (DAS) to image a clay-rich fault zone at metre scale. We developed a workflow for extracting first-arrival travel times from DAS data without trigger timing, including data-based shot timing, denoising, and anisotropy correction, and applied the same tomographic inversion used for geophones. As DAS was permanently installed in multiple boreholes, it could also be used to illuminate other planes with the same shot series. Both sensing systems resolve a low-velocity zone coincident with the mapped Main Fault. Despite a lower signal-to-noise ratio and larger zero-timing uncertainty, DAS produces tomograms comparable to the geophones because of its dense spatial sampling and broad ray coverage. The DAS-based models exhibit higher residuals but reliably capture the principal fault-zone structure, demonstrating that DAS can be used for static high-density seismic imaging of metre-scale structures in clay-rich caprock. Our results confirm that DAS is a practical and complementary tool to geophones for borehole seismic monitoring in caprock settings, particularly in CO2 storage where long-term durability and multi-borehole coverage are required.

Abstract Improving our understanding of induced and natural earthquakes benefits from controlled experiments in insitu laboratories. To investigate the processes during an Mw∼0 earthquake, we performed the “ Mzero ” experiments in a densely instrumented testbed of the Bedretto Underground Laboratory. These multi‐day hydraulic stimulation experiments went the opposite direction to typical induced seismicity research in that they were designed to enhance seismic rupture. In the first experiment, MzeroA, the rock mass was preconditioned by injecting water for 4 days at a pressure just below fracture reactivation pressure followed by a hydraulic stimulation above reactivation pressure. This strategy aimed at increasing the fractures area near critical stress conditions for shear failure, thus facilitating larger ruptures. During MzeroA, an event with Mw–0.54 was induced, followed by a distinct aftershock sequence. This event was one magnitude unit larger than any preceding event. In the second experiment, MzeroB, stimulation was initiated directly, without preconditioning. Compared to MzeroA, MzeroB exhibited higher seismicity rates, a larger seismicity cloud, and a pronounced migrating seismicity front. Combining seismological and hydromechanical observations, we discuss mechanisms that may have influenced the contrasting seismicity responses. In addition to fluid preconditiong, stress transfer from the Mw–0.54 mainshock, and natural seasonal pressure changes, may have contributed to modulating the seismogenic response. However, the relative importance of these processes remains uncertain given experimental limitations. Our results highlight both the potential and challenge of designing hydraulic stimulations to enhance or suppress seismic rupture, with implications for earthquake physics and induced seismicity.

Microseismic source processes can be closely monitored during hydraulic stimulations with optical fiber deployed behind borehole casing, using Distributed Acoustic Sensing (DAS). The Bedretto Underground Laboratory for the Geosciences and Geoenergies (BULGG) provides a test site at the scale of hundreds of meters (meso-scale), where multiple boreholes are instrumented with fibers around a stimulation well. This enables the characterization of source properties of induced seismicity thanks to the dense sampling of the wavefield close to the stimulated region.In 2023 various stimulation activities in the BedrettoLab produced M100 Hz). This study provides a step forward to monitoring microseismicity in hydraulic stimulations with fiber-optic measurements.

Distributed Acoustic Sensing (DAS) represents a leap in seismic monitoring capabilities. Compared to traditional single-seismometer stations, DAS measures seismic strain at meter to sub-meter intervals along fiber-optic cables thus offering unprecedented temporal and spatial resolution. Leveraging the resolution of DAS enables us to monitor and detect seismogenic processes in the domain of hazardous mass-movements, including catastrophic rock avalanches. Here, we present a semi-supervised neural network algorithm for screening DAS data related to mass movements at the Brienz landslide in Eastern Switzerland, which partially failed on 15 June 2023. A DAS interrogator connected to a 10 km-long dark fiber provided by Swisscom Broadcast AG near the landslide recorded seismic data from 16 May to 30 June 2023, with a sampling frequency of 200 Hz and a channel spacing of 4m. During a test period from June 1 to June 19, 2023, a total of 634 characteristic waveforms potentially related to slope failures, including the 15 June 2023 event, were detected, along with vehicle and other anthropogenic noise sources with characteristic diurnal and weekday/weekend variations. For information extraction, we selected a subset of adjacent DAS channels, which include cable sections that were parallel to the failure event trajectory and thus particularly sensitive to mass movement activity. To facilitate efficient processing, we downsampled the data to 20 Hz, considering that slope failure events predominantly excite seismicity at below 10 Hz. We conceptualize the DAS data as a series of images representing consecutive strain rate data in the two dimensions of time and space. To bring out signal coherence between DAS channels, we transform the waveforms into cross-spectral density matrices (CSDM’s) which serve as the input image for unsupervised feature learning using an autoencoder (AE). Leveraging the features learned from the AE, we focus on activity classification using approximately 1500 samples. As ground truth for the slope failure class, we utilize concurrent Doppler radar data. The radar provides an event magnitude, which scales with failure volume and the number of individual rockfalls. Furthermore, the radar provides a measure of the moving mass’s trajectory length and front speed. The radar detected 516 slope failures during the test period. Our algorithm captures 41.09 % of the slope failures recorded by the Doppler radar. The undetected events mainly have low radar magnitudes suggesting that they are associated with mass movements generating reduced seismic activity. Among the slope failure-type signals detected by DAS, 87.85% are also present in the radar catalogue. Interference from vehicle or human-triggered seismic waves, deteriorating the signal-to-noise ratio significantly, poses a challenge for our algorithm to differentiate between slope failures and those activities. Our study thus provides a benchmark for future natural hazard monitoring and suggests that using existing fiber optic infrastructure has a high potential for early warning purposes.

Slope instabilities, further destabilized by global warming and extreme weather conditions, pose increasing risks to life and property. Hence, understanding these potentially destructive phenomena is crucial to mitigate associated losses. Established approaches like remote sensing and radar-based observations yield important information on surface displacement. However, seismic imaging and monitoring techniques offer complementary insights into subsurface structures, physical properties and internal time-dependent processes that drive the slope instability evolution. The ‘Cuolm da Vi’ slope near Sedrun in Central Switzerland is one of the largest mass movements in the Alps (100-200 million m3) and is moving by up to 20cm/year. Even though it currently does not pose an immediate threat, the surface displacement of the slope instability is closely monitored. Yet, knowledge about its internal structure is limited such as, for example, the vertical extent of the unstable section which is suspected to reach several hundred meters in depth. The main objective of our project is to gain new insights into the slope instability structure and evolution. Furthermore, we aim to extend this towards innovative seismic strategies for the characterization and monitoring of large-scale mass movements in general. In summer 2022, we deployed an extensive seismic sensor network at Cuolm da Vi covering an area of approximately 0.6 km2. This network consisted of over 1'000 autonomous nodes arranged in a hexagonal grid pattern. In addition, we installed a 6-kilometer-long fiber-optic cable, targeted for long-term Distributed Acoustic Sensing (DAS) and Distributed Strain Sensing (DSS) measurements. This unique multi-sensor geophysical network enables us to investigate the unstable slope with an unprecedented level of spatial and temporal resolution, allowing us to monitor time-dependent changes over a broad spectrum of scales in space and time. During 2022 and 2023, we collected an extensive data set, including extended periods of continuous acquisition using the nodal, DAS, and DSS systems. During the summer 2022 acquisition period, we conducted a controlled-source seismic experiment to characterize the 3D subsurface structure using seismic imaging techniques. Recordings of 163 dynamite shots by the 1’000 node array resulted in more than 30’000 P-wave first-arrival travel-time picks. Using 3D travel-time tomography, we established a first 3D subsurface P-wave velocity model of the Cuolm da Vi body. The resultant tomograms exhibit strong lateral and vertical velocity contrasts, which correlate at the surface with mapped tectonic features and identified instable sections. Furthermore, velocity anomalies within the slope instability volume indicate significant structural and/or geological variations in space. In combination with the other seismic and geotechnical information, the 3D seismic velocity model allows us to, for example, revise hazard scenarios.

In the last 5 years, the seismological community has experienced an impressive growth in the novel Distributed Acoustic Sensing (DAS) technique, in terms of both the number of experiments and the generated data volume. DAS experiments can generate data at a much finer resolution in space and time, than is seen with standard acquisition techniques. This creates challenges not only for data centres regarding the data management, but also for users that need to access and process this data. The current seismological standards for data and metadata formats, as well as community services specifications, are not capable of handling these datasets in an effective way - not unexpected considering that data volumes and ‘station’ numbers are orders of magnitude larger than typical broad-band experiments. Within the context of the “Geosphere INfrastructures for QUestions into Integrated REsearch” project (Geo-INQUIRE, https://www.geo-inquire.eu/), we have defined a roadmap to advance towards community standards for some of these aspects. The main objective of improving the FAIRness of these datasets was separated in 3 steps. First, we defined how to archive downsampled versions of the datasets in standard community formats (i.e. miniseed and StationXML). Second, we wanted to support the definition (and foster the adoption) of a new metadata standard for DAS experiments based on the outcome of the DAS Research Coordination Network group (DAS-RCN), an initiative led by US researchers. And finally, we wanted to work on the definition of a data format capable of providing fast processing on the data centre side, as well as being able to provide the data to the user to be processed elsewhere. We worked with 3 datasets from the Global DAS Month (February 2023), acquired by INGV, ETH and GFZ. These datasets had been published and made available in different non-standard formats. We used these experiments as test cases to later apply this workflow to the datasets generated by the Transnational Access Calls of this project at a variety of Research Infrastructures across Europe (e.g. at Etna, Bedretto, Ligurian, Madeira, Irpinia, and others). Regarding the data volume and lack of standardisation, we have improved “dastools”, a software package developed at GFZ, to read DAS data in proprietary formats from different manufacturers and convert it to standard miniseed. Downsampling in time and space it provides a reduced version ready to be archived in seismological data centres. Regarding metadata formats, we included in “dastools” the support for the DAS-RCN proposal, discussed and agreed within the community during the last 3 years. We can generate a first draft version of the metadata based on the information available in the raw data of the experiment. We also added a converter to StationXML (still beta) in order to support each step of the archival of a downsampled version of the DAS data. We plan to work soon on the definition of a data format for this type of experiment as it is a key part of our project. In parallel, we’ve just started the development of a Seedlink plugin (real-time transmission) to be deployed and tested at interrogators.

Rock slope toppling typically occurs in slopes with steep, deep-seated discontinuities and involves large unstable rock masses that may transform into catastrophic secondary failures. Understanding the long-term weakening processes of such slopes remains challenging due to limited subsurface access and the lack of continuous deformation monitoring under diverse external forcings. To address these limitations, this study implements a comprehensive, tunnel-based multi-parameter monitoring system in the toppling zone intersected by the first 500 meters of the Bedretto Tunnel in Ticino, Switzerland.The system integrates high-resolution (~0.5 m) distributed fibre optic sensors for strain and temperature monitoring along the tunnel with GPS measurements of 3D surface displacements. In-tunnel hydraulic sensors installed, in both stable and critical zones, continuously capture changes in pore water pressure, tunnel inflow dominated by fractures, and groundwater origins through high-frequency recordings of pressure, temperature, and electrical conductivity. Meteorological stations at the slope toe and toppling crown measure rainfall, air temperature, snow depth, and humidity. Complementary manual snow water equivalent measurements support a degree-day model to estimate surface infiltration onsets and volumes.Initial results from early 2024 suggest that structural orientation primarily controls deformation patterns. While reversible strain correlated with periodic temperature fluctuations is evident, strain variations become more dynamic after precipitation events, particularly intensified in the highly fractured ductile hinge zone. These observations are reinforced by hydrological evidence, which shows gradual seasonal inflow trends near toppling boundaries punctuated by intermittent inflow spikes in response to rainfall and snowmelt events. The findings provide insights into the coupled hydromechanical and thermomechanical processes driving damage accumulation within large toppling slopes. Long-term data collection and integration with historical records aim to pinpoint the primary drivers of deformation variability. As data monitoring efforts continue and more weather events are captured, the results will support the development of modelling toppling failure evolution and contribute to a deeper understanding of rock slope weakening mechanisms.

Snow avalanches pose significant threats in alpine regions, leading to considerable human and economic losses. The ability to promptly identify the locations and timing of avalanche events is essential for effective prediction and risk mitigation. Conventional automatic avalanche detection systems typically rely on radars and/or seismo-acoustic sensors. While these systems operate successfully regardless of weather conditions, their coverage is often confined to a single slope or a small catchment (distances < 3 km). In our study, we demonstrate the feasibility of detecting snow avalanches using Distributed Acoustic Sensing (DAS) through existing fiber-optic telecommunication cables. Our pilot experiment, conducted over the 2021/2022 winter, involved a 10km long fiber-optic dark cable running parallel to the Flüelapass road in the eastern Swiss Alps close to Davos. The DAS data reveal distinct evidence of numerous dry- and wet-snow avalanches, even when they do not reach the cable, as confirmed photographically. We show that avalanches can be distinguished from other signals (e.g., vehicle traffic) using a frequency-dependent STA/LTA attribute, enabling their detection with high spatiotemporal resolution. These findings pave the way for cost-effective and near-real-time avalanche monitoring over extensive distances, leveraging existing fiber-optic infrastructure.

Summary We present a novel cost-effective land acquisition and processing strategy that does not require dense sensor arrays nor active sources for Rayleigh wave dispersion curve estimation and subsequent near-surface characterization. The proposed approach consists of using the divergence D of the seismic wavefield, which is insensitive to the Love wave component but closely related the horizontal acceleration of particle motion H induced by Rayleigh waves. We show that the H/D spectral ratio yields a direct estimate of the desired dispersion curve(s). The method does not rely on travel time analysis and applies to waves originating from any directions therefore is particularly attractive to process Rayleigh wave dominated ambient noise. How to collect the divergence in practice is discussed and we emphasize limitations when using closely spaced sensors for divergence measurements by finite-difference. We propose an alternative sensing technique based on the Distributed-Acoustic-Sensing technology interrogating horizontally coiled fiber-optic at the surface to obtain high-fidelity, low-noise, broadband divergence data, therefore potentially enabling deeper and more detailed sub-surface characterization. The proposed method is validated with synthetic data and field data show promising avenues.

Distributed Acoustic Sensing (DAS) captures the longitudinal strain fluctuations along fiber optic cables. With locally straight cables, the measurement is closely related to the horizontal gradient of the horizontal velocity fields ∂xVx which could alternatively be obtained by differencing closely spaced conventional point sensors such as geophones and seismometers. The latter approach however often suffers from instrument and deployment perturbations as well as finite-difference bias and we discuss the advantage of using DAS to obtain higher fidelity gradients over a larger operating bandwidth, both spatially and temporally. We then introduce the potential of DAS to extract the divergence (∂xVx+∂yVy) of the seismic wavefield by interrogating horizontally coiled fiber. This results in an omni-directional measurement that is closely related to near-surface pressure fluctuations which, we demonstrate, is insensitive to Love waves but closely related the horizontal acceleration of particle motion H induced by Rayleigh waves. Such a wavefield separation is attractive for local ground-roll attenuation and reflection imaging with reduced field effort. We finally show that the H/D spectral ratio provides a local estimate of the Rayleigh wave dispersion curve(s). The proposed method does not rely on travel time analysis and applies to waves originating from any directions, therefore it is particularly suitable to process Rayleigh wave dominated ambient noise, as illustrated with a real data example collected in urban environment (Zurich, Switzerland). In brief, we propose a novel land acquisition and processing strategy that does not require dense sensor arrays nor active sources for cost-effective near-surface characterization.

During February 2023, a total of 32 individual distributed acoustic sensing (DAS) systems acted jointly as a global seismic monitoring network. The aim of this Global DAS Month campaign was to coordinate a diverse network of organizations, instruments, and file formats to gain knowledge and move toward the next generation of earthquake monitoring networks. During this campaign, 156 earthquakes of magnitude 5 or larger were reported by the U.S. Geological Survey and contributors shared data for 60 min after each event’s origin time. Participating systems represent a variety of manufacturers, a range of recording parameters, and varying cable emplacement settings (e.g., shallow burial, borehole, subaqueous, and dark fiber). Monitored cable lengths vary between 152 and 120,129 m, with channel spacing between 1 and 49 m. The data has a total size of 6.8 TB, and are available for free download. Organizing and executing the Global DAS Month has produced a unique dataset for further exploration and highlighted areas of further development for the seismological community to address.