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.

Catastrophic mass movements threaten mountain communities worldwide. Rockfalls, avalanches, debris flows and sediment pulses in rivers are common geomorphological processes but can destroy homes and infrastructure with little warning. Population pressure, thawing permafrost and other climatic affects will likely exacerbate this threat in the near future requiring new risk management strategies and monitoring tools.In recent years, seismology has emerged as an efficient observational method to capture rapid mass movements and study their dynamics as well as variations in event activity. Multi-million cubic meter rock-ice avalanches like the 2025 event destroying parts of the village of Blatten, Switzerland, are often detected by national seismic networks primarily designed to monitor earthquake activity. Smaller events like rockfalls and debris flows require denser seismic networks with station spacing of a few kilometres or less. Nevertheless, their seismic signature is usually clear when seismic stations are close enough.The straightforward detection of mass movements using seismic instrumentation has motivated new monitoring approaches. However, the challenge remains to automatically identify the seismic mass movement signature in continuous data streams given a wealth of other signals like anthropogenic noise and earthquakes, which are recorded at the same time and may mask the sought-after mass movement signals. Recent applications of machine learning algorithms have provided promising first results and allowed for mass movement detection in cases where empirical threshold-based triggering rules yield impermissible amounts of false positives.Here we present a new approach to detect mass movements signals in continuous seismic catalogues. To tackle the challenge of algorithm transferability between sites with different seismic background noise we treat mass movement signals as anomalies given their catastrophic nature and rare occurrence. We use the isolation forest algorithm to quantify the degree of anomaly (‘anomaly score’) associated with any recorded signal. Using data from polar fjord systems, our results show that anomaly detection can efficiently reduce continuous seismic data sets to a handful of signals, which are likely related to rock avalanches and glacier break-off events. On smaller scales, anomaly scores can be processed to identify general characteristics of debris flow seismograms recorded near active torrents. The anomaly score approach thus facilitates systematically searching for large-scale mass movement seismograms in earthquake monitoring data and may be a stepping stone for flexible and transferable detection algorithms for monitoring and warning purposes.

The properties of laser signals are affected by deformation of the optical fibre through which they are transmitted. While this deformation dependence is undesirable in telecommunication, it can be exploited for the construction of novel seismic sensors that fill a niche in data acquisition where traditional seismometer arrays would be difficult to deploy. This includes densely populated urban centers, the oceans, volcanoes and the Earth’s polar regions. These notes complement a presentation on recent methodological developments and applications in fibre-optic seismology. The first part is focused on the use of distributed fibre-optic sensing in cryosphere research, and specifically the investigation of the internal structure and seismicity of glaciers and ice sheets. The second part is dedicated to recent advances in integrated fibre-optic sensing, with emphasis on novel measurement principles and sensitivity.

Hydromechanical soil response to moisture variations reflects complex subsurface dynamics that are critical for geoengineering, slope stability, and other soil health-related fields. While laboratory experiments have provided insights into soil behavior under varying wetness and loading conditions, field-scale observations with high spatial and temporal resolution remain limited. In this study, we present a 2 month field monitoring approach using Distributed Acoustic Sensing (DAS), which enables high-resolution, full-coverage, and continuous monitoring of a grass-covered soil slope. DAS allows for subsurface characterization and time-lapse monitoring of soil moisture dynamics using ambient noise interferometry. Furthermore, by analyzing nanostrain-scale deformation in conjunction with stress state derived from in situ soil moisture measurements, we demonstrate that DAS can track real-time volumetric changes in response to both long-term and daily cyclic moisture variations. We suggest DAS as a valuable tool for the continuous detection of moisture-driven changes in soil mechanical properties with high resolution.

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.

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. Specifically, basal sources such as stick-slip events often stay unnoticed due to long travel paths to surface sensors and accompanied wave attenuation. Yet, stick-slip events play a crucial role in understanding glacier sliding and therefore, it is of great interest to investigate their spatio-temporal evolution, across the entire glacier from its ablation to its accumulation zone.Distributed Acoustic Sensing (DAS) is a technology for measuring strain rate by using common fiber-optic cables in combination with an interrogation unit. This technology enables us to acquire seismic data over an entire glacier with great spatial and temporal resolution. To unmask stick-slip events, new techniques are required that effectively and efficiently denoise large cryoseismological DAS data sets. Here, we propose an autoencoder, a type of deep neural network, which is able to separate the incoherent environmental noise from the temporally and spatially coherent signals of interest (e.g., stick-slip events or crevasse formations). We trained the autoencoder in order to denoise a DAS data set acquired on Rhonegletscher, Switzerland, in July 2020. Due to the highly active and dynamic cryospheric environment as well as non-ideal cable-ground coupling the collected DAS data are characterized by a low signal to noise ratio compared to classical point sensors.Several models were trained on a variety of data subsets, differing in recording positions (ablation or accumulation zone), event types (stick-slip event or surface event) and the quantity of training events. We compare and discuss the denoising capabilities of these models with several metrics, such as inter-channel coherence, similarity between seismometer and DAS recordings, and visual assessment. This evaluation is conducted while considering different data types in a qualitative and quantitative manner. All models show an increase in inter-channel coherence of the seismic records after denoising. Further, all models uncover previously undetected stick-slip events, whereby models trained on manually picked training data perform better than models trained on randomly picked training data. We believe that the application of our models can improve the understanding of basal stick-slip information in cryoseismological DAS datasets, potentially uncovering previously hidden information.

The icy parts of the Earth, known as the cryosphere, are an integral part of the climate system. Comprehensively understanding the cryosphere, requires dense observations, not only of its surface, but also of its internal structure and dynamics. Seismic methods play a central role in this endeavour. Fibre-optic sensing is emerging as valuable complement and alternative to well-established electro-mechanical seismometers. Offering metre-scale channel spacing, interrogation distances of up to around 100 km, and a bandwidth from mHz to kHz, it has enabled new seismological applications, for instance, under water, in cities and on volcanoes. Cryosphere research particularly benefits from fibre-optic sensing because long cables can be deployed with relative ease in icy environments where dense arrays of seismometers are difficult to install, including glaciers, ice sheets and deep boreholes. Intended to facilitate future fibre-optic seismology research in the cryosphere, this Expository Review combines a classical publication review with theoretical background, a practical field guide, a cryospheric signal gallery, and open-access data examples for hands-on training. Following a summary of recent findings about firn and ice structure, glacial seismicity, hydrology and avalanche dynamics, we derive the ideal instrument response of a distributed fibre-optic deformation sensor. To approach this ideal in field experiments, we propose numerous practical dos and don'ts concerning the choice and handling of fibre-optic cables, required equipment, splicing in the field at low temperatures, cable layout and trenching, and the deployment and coupling of cables in boreholes. A cryospheric signal gallery provides examples of data from a wide range of sources, such as explosions, land and air traffic, electricity generators, basal stick-slip icequakes, surface crevassing, englacial icequake cascades, floating ice shelf resonance, surface water flow and snow avalanches. Many of these data are enclosed as an open-access training resource, together with code for reading, visualisation and simple analyses. This review concludes with a discussion of grand open challenges in our understanding of cryosphere structure and dynamics, and how further advances in fibre-optic sensing may help to overcome them.

We present a workflow for producing shallow subsurface velocity models from passive urban distributed acoustic sensing (DAS) data. This method is demonstrated using a dataset collected in Bern, Switzerland, using in situ telecommunications fiber. We compute noise correlations to extract Rayleigh-wave dispersion curves, which we then use to produce a series of overlapping 1D velocity models of the top tens of meters of the subsurface. This dataset represents a realistic “best-case” scenario when using real urban telecommunications fiber—the cable layout is linear, its location is well known, and coupling is broadly sufficient. Nevertheless, a number of nontrivial complexities still exist in such a dataset and are highlighted in this study. Rather than prescribing one optimal workflow for all similar experiments, we focus on the steps taken and decisions made that led to a velocity model in this setting. It is our hope that such a text will be useful to future researchers exploring DAS interferometry and may provide some guidance on overcoming the difficulties and imperfections of working with such datasets.

A major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a variety of environmental processes. Particular Distributed Acoustic Sensing (DAS) data often suffers from low signal‐to‐noise ratios (SNR) potentially resulting in a multitude of undetected events of interest, which further remain unanalyzed. To record seismicity, we deployed a DAS system on Rhône Glacier, Switzerland, using a 9 km long fiber‐optic cable that covered the entire glacier, from its accumulation to its ablation zone. The highly active and dynamic cryospheric environment, in combination with poor coupling, resulted in DAS data characterized by a low SNR. Our objective is to develop and evaluate a method to effectively denoise this cryoseismological DAS data set while comparing our approach to state‐of‐the‐art filtering and denoising methods. We propose the ‐invariant cryo denoiser, specifically trained on cryoseismological data and capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest, based on a self‐supervised ‐invariant U‐Net autoencoder. The method enhances inter‐channel coherence, improves waveform similarity with co‐located seismometers, and increases SNR. The comparison of different methods shows that our approach obtains the highest gain in SNR and highest similarity with co‐located seismometers while suffering from denoising artifacts in rare cases. The proposed denoiser 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.

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) technology enables the detection of waves generated by seismic events, generally as uniaxial strain/strain rate time-series observed for dense, subsequent, portions of a Fibre Optic Cable (FOC). Despite the advantages in measurement density, data quality is often affected by uniaxial signal polarization, site effects and cable coupling, beyond the physical energy decay with distance. To better understand the relative importance of these factors for data inversion, we attempt a first modelling of noise patterns affecting DAS arrival times for a set of seismic events. The focus is on assessing the impact of noise statistics, together with the geometry of the problem, on epicentral location uncertainties. For this goal, we consider 15 'real-world' cases of DAS arrays with different geometry, each associated with a seismic event of known location. We compute synthetic P-wave arrival times and contaminate them with four statistical distributions of the noise. We also estimate P-wave arrival times on real waveforms using a standard seismological picker. Eventually, these five data sets are inverted using a Markov chain Monte Carlo method, which offers the evaluation of the relative event location differences in terms of posterior probability density (PPD). Results highlight how cable geometry influences the shape, extent and directionality of the PPDs. However, synthetic tests demonstrate how noise assumptions on arrival times often have important effects on location uncertainties. Moreover, for half of the analysed case studies, the observed and synthetic locations are more similar when considering noise sources that are independent of the geometrical characteristics of the arrays. Thus, the results indicate that axial polarization, site conditions and cable coupling, beyond other intrinsic features (e.g. optical noise), are likely responsible for the complex distribution of DAS arrival times. Overall, the noise sensitivity of DAS suggests caution when applying geometry-only-based approaches for the a priori evaluation of novel monitoring systems.

Observations of glacier melt and runoff are of fundamental interest in the study of glaciers and their interactions with their environment. Considerable recent interest has developed around distributed acoustic sensing (DAS), a sensing technique which utilizes Rayleigh backscatter in fiber optic cables to measure the seismo-acoustic wavefield in high spatial and temporal resolution. Here, we present data from a month-long, 9 km DAS deployment extending through the ablation and accumulation zones on Rhonegletscher, Switzerland, during the 2020 melt season. While testing several types of machine learning (ML) models, we establish a regression problem using the DAS data as the dependent variable to predict the glacier discharge observed at a proglacial stream gauge. We also compare two models that only depend on meteorological station data. We find that the seismo-acoustic wavefield recorded by DAS can be utilized to infer proglacial discharge. Models using DAS data outperform both the models trained on meteorological data with mean absolute errors (MAE) of 0.64 m3/s, 2.25 m3/s, and 2.72 m3/s, respectively. This study demonstrates the ability of in situ glacier DAS to be used for quantifying proglacial discharge and points the way to a new approach to measuring glacier runoff.

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.

Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have emerged as a promising alternative. This study explores the use of distributed acoustic sensing (DAS), a technique that detects local stress in optical fibers, for underwater sound recording. An experiment was conducted in Lake Zurich, where a fiber-optic cable and a self-made hydrophone array were deployed. A test signal was broadcasted at various locations, and the resulting data was synchronized and consolidated into files. Analysis revealed distinct frequency responses in the DAS channels and provided insights into sound propagation in the lake. Challenges related to cable sensitivity, sample rate, and broadcast fidelity were identified. This dataset serves as a valuable resource for advancing acoustic sensing techniques in underwater environments, especially for studying marine mammal vocal behavior.

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 are 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.

&lt;p&gt;The Distributed Acoustic Sensing (DAS) method re-purposes fiber optic cables into a very-dense array of strain/strain-rate sensors, &lt;span class=&quot;Y2IQFc&quot; lang=&quot;en&quot;&gt;capable of detecting different types of seismic events. &lt;/span&gt;However, DAS data are characterized by lower SNRs compared with standard seismic sensors, mainly because of a) strong directivity effects, 2) ground coupling inhomogeneities, and 3) site effects. Hence, beyond the array geometry, specific noise sources may reduce the potential of DAS for seismic monitoring. Previous research has already shown successful case-studies for event detection/location. Nevertheless, a coherent test on the performances of various arrays of different sizes and geometries is still lacking.&lt;/p&gt; &lt;p&gt;In this study, an extensive DAS database is organized for such a goal, including 15 DAS arrays that recorded at least one seismic event (located at a range of distances from the arrays). P wave arrival times are exploited to estimate the epicentral parameters with a Markov Chain Monte Carlo method. Then, to analyze the effects of cable geometry and potential sources of noise/ambiguity on the location uncertainties, a series of synthetic tests are performed, where synthetic traveltimes are modified as follows: a) adding noise with equal variance to all the DAS channels (SYNTH-01), b) adding noise characterized by an increasing variance with the distance from the event (SYNTH-02), c) simulating the mis-pick between P and S phases (SYNTH-03) and d) adding noise with a variance influenced by cable coupling inhomogeneities (SYNTH-04). Results show that the epicentral locations with automatic P wave arrival times have different degrees of uncertainty, given the geometrical relation between the event and the DAS arrays. This behavior is confirmed by the SYNTH-01 test, indicating that specific geometries provide a lower constraint on event location. Moreover, SYNTH-04 shows that simulating cable coupling inhomogeneities primarily reproduces the observed location uncertainties. Finally, some cases are not explained by any of the synthetic tests, stressing the possible presence of more complex noise sources contaminating the signals.&lt;/p&gt;

Data and code working together is fundamental to machine learning (ML), but the context around datasets and interactions between datasets and code are in general captured only rudimentarily. Context such as how the dataset was prepared and created, what source data were used, what code was used in processing, how the dataset evolved, and where it has been used and reused can provide much insight, but this information is often poorly documented. That is unfortunate since it makes datasets into black-boxes with potentially hidden characteristics that have downstream consequences. We argue that making dataset preparation more accessible and dataset usage easier to record and document would have significant benefits for the ML community: it would allow for greater diversity in datasets by inviting modification to published sources, simplify use of alternative datasets and, in doing so, make results more transparent and robust, while allowing for all contributions to be adequately credited. We present a platform, Renku, designed to support and encourage such sustainable development and use of data, datasets, and code, and we demonstrate its benefits through a few illustrative projects which span the spectrum from dataset creation to dataset consumption and showcasing.

One major challenge in Environmental Seismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Those signals potentially stay unnoticed and thus, might not be analyzed further.Distributed acoustic sensing (DAS) is an emerging technology for measuring strain rate data by using common fiber-optic cables in combination with an interrogation unit. This technology enables researchers to acquire seismic monitoring data on poorly accessible terrain with great spatial and temporal resolution. We utilized a DAS unit in a cryospheric environment on a temperate glacier. The data collection took place in July 2020 on Rhonegletscher, Switzerland, where a 9 km long fiber-optic cable was installed, covering the entire glacier from its accumulation to its ablation zone. During one month 17 TB of data were acquired. Due to the highly active and dynamic cryospheric environment, our collected DAS data are characterized by a low signal to noise ratio compared to classical point sensors. Therefore, new techniques are required to denoise the data efficiently and to unmask the signals of interest. Here we propose an autoencoder, which is a deep neural network, as a denoising tool for the analysis of our cryospheric seismic data. An autoencoder can potentially separate the incoherent noise (such as wind or water flow) from the temporally and spatially coherent signals of interest (e.g., stick-slip event or crevasse formation). We test this approach on the continuous microseismic Rhonegletscher DAS records. To investigate the autoencoder’s general suitability and performance, three different types of training data are tested: purely synthetic data, original data from on-site seismometers, and original data from the DAS recordings themselves. Finally, suitability, performance as well as advantages and disadvantages of the different types of training data are discussed.