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Hazardous mass-movements pose a great danger to the population and critical infrastructure, especially in alpine areas. Monitoring and early-warning systems can potentially save many lives and improve the resilience of mountain communities to catastrophic events. Increasing coverage of seismic networks recording hazardous mass-movements opens up new warning perspectives as long as efficient algorithms screening the seismic data streams in real-time are available.We propose to combine physical and statistical properties of seismic ground velocity recordings from geophones and seismometers as a foundation for an unsupervised workflow for mass movement detection. We evaluate the performance, consistency, and generalizability of unsupervised clustering algorithms like K-means and Bayesian Gaussian Mixture Models against supervised methods like the Random Forest classifier. Focusing on debris-flow records at the Illgraben torrent in Switzerland, we present a generic mass-movement detector with high accuracy and early-warning capability. We apply this detector to other datasets form other sites to investigate its transferability.Since our results aim to enable mass-movement monitoring and early-warning worldwide, Open Research Data principles like Findability, Accessibility, Interoperability and Reusability (FAIR) are of high importance for this project. We discuss how using the Renku (renkulab.io) platform of the Swiss Data Science Center ensures FAIR data science principles in our investigation. This is a key step towards our ultimate goal to enable seismology-based early warning of mass-movements wherever it may be required.

Landslides are a major natural hazard that can cause significant loss of life and property damage around the world. As global temperatures rise and weather extremes become more frequent, we can expect an increase in the hazard emanating from landslides too. In order to better understand and mitigate landslide risks, a variety of strategies have been developed to characterize and monitor landslide activity. Many established approaches provide valuable information about surface displacement and surface properties, but are not suited to inspect the subsurface parts of a landslide body. In contrast, seismic imaging and monitoring methods allow us to study subsurface structures, properties, and internal processes that control landslide behaviour.In our project, we develop novel seismic data acquisition and interpretation approaches to characterize and monitor one of the largest active unstable slopes in the Alps, the Cuolm da Vi landslide, with an unprecedented spatial resolution. We achieve this by combining an array of over 1’000 seismic nodes with fiber-optic based monitoring techniques such as Distributed Acoustic (DAS) and Strain Sensing (DSS).The deep-seated Cuolm da Vi landslide is located near Sedrun (Central Switzerland) and consists of approximately 100-200 million m3 of unstable rock reaching displacement rates up to 10-20 cm/yr with clear seasonal cycles. In summer 2022, we buried over 6 kilometres of fiber-optic cable in this alpine environment covering the most active part of the landslide with multiple cable orientations. Additionally, we deployed a nodal array of 1046 accelerometers in a hexagonal grid covering around 1km2 with a nominal spacing of 28 meters. Seismic data were acquired with the nodes and the DAS system continuously for four weeks. This time period included the blasting of 163 dynamite shots for calibration and active-source imaging purposes. In 2023, we plan to conduct data acquisition for longer periods using primarily fibre-optic based techniques with a focus on the temporal evolution of the landslide dynamics.Our first goal is to resolve the internal structure of the landslide based on the controlled-source data acquired in summer 2022 to construct, for example, a seismic velocity model. Based on the models derived from the active-source seismic data, we plan to exploit the continuous seismic recordings of ambient vibrations and potential seismic signals produced by the landslide activity to complement structural models and study the landslide dynamics. We will present our current results and discuss their implications for the next steps towards monitoring this landslide over time.

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.

The Distributed Acoustic Sensing (DAS) method re-purposes fiber optic cables into a very-dense array of strain/strain-rate sensors, capable of detecting different types of seismic events. 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.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.

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 Vallee 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., Journal of Geophysical Research: Earth Surface, 128 (5), ISSN:0148-0227, ISSN:2169-9003, ISSN:2169-9011

We present and evaluate array processing techniques and algorithms for the characterization of snow avalanche signals recorded with Distributed Acoustic Sensing (DAS). Avalanche observations rely on comprehensive measurements of sudden and rapid snow mass movement that is hard to predict. Conventional avalanche sensors are limited to observations on or above the surface. Recently, seismic sensors have increased in their popularity for avalanche monitoring and characterization due to their avalanche detection and characterization capabilities. To date, however, seismic instrumentation in avalanche terrain is sparse, thereby limiting the spatial resolution significantly. As an addition to conventional seismic instrumentation, we propose DAS to measure avalanche-induced ground motion. DAS is a technology using backscattered light along a fiber-optic cable to measure strain (-rate) along the fiber with unprecedented spatial and temporal resolution - in our example with 2 m spatial sampling and a sampling rate of 1kHz. We analyze DAS data recorded during winter 2020/2021 at the Valleé de la Sionne avalanche test site in the Swiss Alps, utilizing an existing 700 m long fiber-optic cable. Our observations include avalanches propagating on top of the buried cable, delivering near-field observations of avalanche-ground interactions. After analyzing the properties of near-field avalanche DAS recordings, we discuss and evaluate algorithms for (1) automatic avalanche detection, (2) avalanche surge propagation speed evaluation and (3) subsurface property estimation. Our analysis highlights the complexity of near-field DAS data, as well as the suitability of DAS-based monitoring of avalanches and other hazardous granular flows. Moreover, the clear detectability of avalanche signals using existing fiber-optic infrastructure of telecommunication networks opens the opportunity for unrivalled warning capabilities in Alpine environments., EGUsphere

We demonstrate the logistic feasibility and scientific potential of distributed acoustic sensing (DAS) in alpine volcano-glacial environments that are subject to a broad range of natural hazards. Our work considers the Mount Meager massif, an active volcanic complex in British Columbia, estimated to have the largest geothermal potential in Canada, and home of Canada's largest recorded landslide in 2010. From September to October 2019, we acquired continuous strain data, using a 3-km long fiber-optic cable, deployed on a ridge of Mount Meager and on the uppermost part of a glacier above 2,000 m altitude. The data analysis detected a broad range of unexpectedly intense, low-magnitude, local seismicity. The most prominent events include long-lasting, intermediate-frequency (0.01–1 Hz) tremor, and high-frequency (5–45 Hz) earthquakes that form distinct spatial clusters and often repeat with nearly identical waveforms. We conservatively estimate that the number of detectable high-frequency events varied between several tens and nearly 400 per day. We also develop a beamforming algorithm that uses the signal-to-noise ratio (SNR) of individual channels, and implicitly takes the direction-dependent sensitivity of DAS into account. Both the tremor and the high-frequency earthquakes are most likely related to fluid movement within Mount Meager's geothermal reservoir. Our work illustrates that DAS carries the potential to reveal previously undiscovered seismicity in challenging environments, where comparably dense arrays of conventional seismometers are difficult to install. We hope that the logistics and deployment details provided here may serve as a starting point for future DAS experiments., Journal of Geophysical Research: Solid Earth, 126 (11), ISSN:2169-9313, ISSN:0148-0227, ISSN:2169-9356

We demonstrate the logistic feasibility and scientific potential of Distributed Acoustic Sensing (DAS) in alpine volcano-glacial environments that are subject to a broad range of natural hazards. O...

We present one of the first applications of Distributed Acoustic Sensing (DAS) in a volcanic environment. The goals are twofold: First, we want to examine the feasibility of DAS in such a remote and extreme environment, and second, we search for active volcanic signals of Mount Meager in British Columbia (Canada). The Mount Meager massif is an active volcanic complex that is estimated to have the largest geothermal potential in Canada and caused its largest recorded landslide in 2010. We installed a 3-km long fibre-optic cable at 2000 m elevation that crosses the ridge of Mount Meager and traverses the uppermost part of a glacier, yielding continuous measurements from 19 September to 17 October 2019.We identify ~30 low-frequency (0.01-1 Hz) and 3000 high-frequency (5-45 Hz) events. The low-frequency events are not correlated with microseismic ocean or atmospheric noise sources and volcanic tremor remains a plausible origin. The frequency-power distribution of the high-frequency events indicates a natural origin, and beamforming on these events reveals distinct event clusters, predominantly in the direction of the main peaks of the volcanic complex. Numerical examples show that we can apply conventional beamforming to the data, and that the results are improved by taking the signal-to-noise ratio of individual channels into account.The increased data quantity of DAS can outweigh the limitations due to the lower quality of individual channels in these hazardous and remote environments. We conclude that DAS is a promising tool in this setting that warrants further development.

As fibre-optic DAS deployments become more common, researchers are turning to tried-and-true methods of locating or characterizing seismic sources such as beamforming. However, the strain measurement from DAS intrinsically carries its own sensitivities to both wave type and polarization (Martin et al. 2018, Paitz 2020 doctoral thesis). Additionally, a measurement along a conventional fibre-optic cable only provides one component of motion, and so certain azimuths may be blind to certain types of seismic sources, unless the cable layout can be designed to be oriented in multiple directions.In this work, we explore the development and application of a beamforming algorithm that explicitly searches for multiple wavetypes. This builds on 3-component beamforming or Matched Field Processing (MFP) algorithms by Riahi et al. (2013), and Gal et al. (2018), where in addition to gridsearching over possible source azimuths, a distinct gridsearch is performed for each possible wavetype of interest. This does not solve the problem that a given cable orientation might be less sensitive to certain directions, but at least an array-response function can be robustly defined for each type of seismic excitation. This might help further distinguish whether beamforming observations are dominated by primary sources or by secondary scattering (van der Ende and Ampuero, 2020 preprint).Much of this work uses analytic theory and synthetic examples. Time permitting, the enhanced algorithm will also be applied to data from the Mt. Meager experiment to explore its feasibility and efficacy with real data (EGU contribution from Klaasen et. al, 2021).

With an increasing availability of next-generation instruments in seismology such as Distributed Acoustic Sensing (DAS) interrogators and rotation sensors, as well as public datasets from these instruments, there is a demand for incorporating these new gradient observables into the workflows of seismic interferometry and noise source inversion.Dropping the common assumption of Green’s function retrieval, we derive a generalized formulation for seismic interferometry that can utilize not only displacement measurements but also spatial and temporal gradients thereof – including velocity, strain and rotation.Based on this formulation, we are able to simulate interferometric wavefields of displacement and gradient observations or arbitrary combinations of these observables, for heterogeneous visco-elastic media, and for arbitrary noise source distributions.We demonstrate how to derive adjoint-based expressions for finite-frequency sensitivity kernels of the interferometric wavefields with respect to subsurface structure and noise source distributions, for a wide range of observed quantitates and combinations thereof. We provide numerical examples of such sensitivity kernels.Especially in environments where the common assumption of a homogeneous noise source distribution is violated, our formulation enables correlation-wavefield based inversions, combining different seismic observables.The discussed theoretical and numerical developments bring us one step closer to multi-observational full waveform ambient noise inversion, underlining the potential and possible impact of recent developments in seismic instrumentation to seismology across all scales.

With the potential of high temporal and spatial sampling and the capability of utilizing existing fiber-optic infrastructure, distributed acoustic sensing (DAS) is in the process of revolutionizing geophysical ground-motion measurements, especially in remote and urban areas, where conventional seismic networks may be difficult to deploy. Yet, for DAS to become an established method, we must ensure that accurate amplitude and phase information can be obtained. Furthermore, as DAS is spreading into many different application domains, we need to understand the extent to which the instrument response depends on the local environmental properties. Based on recent DAS response research, we present a general workflow to empirically quantify the quality of DAS measurements based on the transfer function between true ground motion and observed DAS waveforms. With a variety of DAS data and reference measurements, we adapt existing instrument-response workflows typically in the frequency band from 0.01 to 10 Hz to different experiments, with signal frequencies ranging from 1/3000 to 60 Hz. These experiments include earthquake recordings in an underground rock laboratory, hydraulic injection experiments in granite, active seismics in agricultural soil, and icequake recordings in snow on a glacier. The results show that the average standard deviations of both amplitude and phase responses within the analyzed frequency ranges are in the order of 4 dB and 0.167π radians, respectively, among all experiments. Possible explanations for variations in the instrument responses include the violation of the assumption of constant phase velocities within the workflow due to dispersion and incorrect ground-motion observations from reference measurements. The results encourage further integration of DAS-based strain measurements into methods that exploit complete waveforms and not merely travel times, such as full-waveform inversion. Ultimately, our developments are intended to provide a quantitative assessment of site- and frequency-dependent DAS data that may help establish best practices for upcoming DAS surveys.

Over the past 1-2 decades, seismological measurements have provided new and unique insights into glacier and ice sheet dynamics. At the same time, sensor coverage is typically limited in harsh glacial environments with littile or no access. Turning kilometer-long fiber optic cables placed on the Earth’s surface into thousands of seismic sensors, Distributed Acoustic Sensing (DAS) may overcome the limitation of sensor coverage in the cryosphere. First DAS applications on the Greenland and Antarctic ice sheets and on Alpine glacier ice have highlighted the technique’s superiority. Signals of natural and man-made seismic sources can be resolved with an unrivaled level of detail. This offers glaciologists new perspectives to interpret their seismograms in terms of ice structure, basal boundary conditions and source locations. However, previous studies employed only relatively small network scales with a point-like borehole deployment or < 1 km cable aperture at the ice surface. Here we present a DAS installation, which aims to cover the majority of an Alpine glacier catchment: For one month in summer 2020 we deployed a 9 km long fiber optic cable on Rhonegletscher, Switzerland, and gathered continuous DAS data. The cable followed the glacier’s central flow line starting in the lowest kilometer of the ablation zone and extending well into the accumulation area. Even for a relatively small mountain glacier such as Rhonegletscher, cable deployment was a considerable logistical challenge. However, initial data analysis illustrates the benefit compared to conventional cryoseismological instrumentation: DAS measurements capture ground deformation over many octaves, including typical high-frequency englacial sources (10s to 100s of Hz) related to crevasse formation and basal sliding as well as long period signals (10s to 100s of seconds) of ice deformation. Depending on the presence of a snow cover, DAS records contain strong environmental noise (wind, meltwater flow, precipitation) and thus exhibit lower signal-to-noise ratios compared to conventional on-ice seismic installations. This is nevertheless outweighed by the advantage of monitoring ground unrest and ice deformation of nearly an entire glacier. We present a first compilation of signal and noise records and discuss future directions to leverage DAS data sets in glaciological research., EGUsphere

Avalanche research requires comprehensive measurements of sudden and rapid snow mass movement that is hard to predict. Automatic cameras, radar and infrasound sensors provide valuable observations of avalanche structure and dynamic parameters, such as velocity. Recently, seismic sensors have also gained popularity, because they can monitor avalanche activity over larger spatial scales. Moreover, seismic signals elucidate rheological properties, which can be used to distinguish different types of avalanches and flow regimes. To date, however, seismic instrumentation in avalanche terrain is sparse. This limits the spatial resolution of avalanche details, needed to characterise flow regimes and maximise detection accuracy for avalanche warning. As an alternative to conventional seismic instrumentation, we propose Distributed Acoustic Sensing (DAS) to measure avalanche-induced ground motion. DAS is based on fibre-optic technology, which has previously been used already for environmental monitoring, e.g., of snow avalanches. Thanks to recent technological advances, modern DAS interrogators allow us to measure dynamic strain along a fibre-optic cable with unprecedented temporal and spatial resolution. It therefore becomes possible to record seismic signals along many kilometres of fibre-optic cables, with a spatial resolution of a few metres, thereby creating large arrays of seismic receivers. We test this approach at an avalanche test site in the Valleé de la Sionne, in the Swiss Alps, using an existing 700 m long fibre-optic cable that is permanently installed underground for the purpose of data transfer of other, independent avalanche measurements. During winter 2020/2021, we recorded numerous snow avalanches, including several which reached the valley bottom, travelling directly over the cable during runout. The DAS recordings show clear seismic signatures revealing individual flow surges and various phases/modes that may be associated with roll waves and avalanche arrest. We compare our observations to state-of-the-art radar and seismic measurements which ideally complement the DAS data. Our initial analysis highlights the suitability of DAS-based monitoring and research for avalanches and other hazardous granular flows. Moreover, the clear detectability of avalanche signals using existing fibre-optic infrastructure of telecommunication networks opens the opportunity for unrivalled warning capabilities in Alpine environments., EGUsphere

Distributed Acoustic Sensing (DAS) is an optical interferometry based ground motion sensing technology which has the potential to revolutionize the field of seismological data acquisition. It offers the possibility to replace very large numbers of cost-intensive conventional point sensors (seismometers or geophones) by interrogating a single low-cost optic-fibre cable. Being unaffected by spatial aliasing, DAS is emerging as a potential next-generation broad-band geo-hazard (e.g. earthquakes, landslides) and reservoir (e.g. geothermal, oil and gas) seismic monitoring tool.For borehole applications, with the cable appropriately coupled with the casing, the reliability and benefit of DAS-based VSP acquisition is now widely recognized. At the surface however, for reflection seismic for example, the adequate deployment procedure is less well documented, and experiments are performed with cables sometimes directly deployed on the surface, or sometimes buried quite deep (e.g. one meter) in the ground. Especially for non-permanent monitoring, the trenching effort can be substantial or unaffordable due to logistic or permitting issues. One may wonder if such an effort with its associated cost is actually beneficial.We present here the results of a surface-based active seismic experiment conducted in Switzerland in the context of a geothermal reservoir characterization project with “co-located” stretches of cable deployed at different depths. The repeatability of the DAS measurements is quantified and compared to a dense array of conventional multi-component geophones. The study shows that deeply (50 cm) deployed cables offers only marginal data quality improvements compared to very shallow (2 cm) cables. In contrast, the parts of the cable directly laid down at the surface exhibit much larger noise levels and very poor repeatability (approximately one order of magnitude larger NRMS). Our study suggests that only a minor amount of elastic material covering the cable is enough to provide a good coupling and that a modest machine to conveniently perform such a shallow deployment would greatly benefit the growing DAS user community.

Anticipating the risks natural hazards pose to an urban environment requires an understanding of the shallow Earth structure of the region. While urban infrastructure often hinders the deployment of a traditional seismic array, Distributed Acoustic Sensing (DAS) technology facilitates the use of existing telecommunication fibre-optic cables for seismic observation, with spatial resolution down to the metre scale. Through collaboration with the SWITCH foundation, we were able to use existing, in-situ fibres beneath Bern, Switzerland for seismic data acquisition over two weeks, covering a distance of 6 km with a spatial resolution of 2 m. This allowed for not only real-time visualisation of anthropogenic noise sources (e.g. road traffic), but also of the propagation of resulting seismic waves. Data is analysed in the time and frequency domain to explore the range of signals captured and to assess the consistency of data quality along the cable. The local velocity structure can be constrained using both noise correlations and deterministic signals excited by traffic. Initial results reveal the ability of DAS to capture signals over a wide range of frequencies and distances, and show promise for utilising urban DAS data to perform urban seismic tomography and hazard analysis., EGUsphere

Summary Distributed Acoustic Sensing is a maturing ground motion sensing technology based on the interrogation of a low-cost fiber optic cable to derive broad-band, potentially unaliased, strain variations over long distances. DAS is a recognized option for seismic downhole applications and one may also foresee promising surface applications, for instance by simultaneously interrogating the large cable-based communication network of modern digital point-sensor acquisition systems. However, clear recommendations on appropriate cable deployment for surface seismic are still lacking. Burial in deep trenches may benefit coupling and the overall data quality, but this is likely to be unaffordable in practice. In this study, we analyse the impact of burial depth on the DAS data quality by comparing “co-located” stretches of DAS cable buried at different depths during an active-source seismic survey. Our analysis reveals comparable data quality for cables buried at shallow (2cm) depth and cables at 40cm depth. In contrast, cables deployed on the surface provide unacceptably low data quality likely due to inadequate coupling. Our study suggests that costly deep trenching can be avoided as long as the cables are soil-covered and not directly exposed at the surface.

With the upside of high spatial and temporal sampling even in remote or urban areas using existing fiber-optic infrastructure, Distributed Acoustic Sensing (DAS) is in the process of revolutionising the way we look at seismological data acquisition. However, recent publications show variations of the quality of DAS measurements along a single cable. In addition to site- and orientation effects, data quality is strongly affected by the transfer function between the deforming medium and the fiber, which in turn depends on the fiber-ground coupling and the cable properties. Analyses of the DAS instrument response functions in a limited part of the seismological frequency band are typically based on comparisons with well-coupled conventional seismometers for which the instrument response is sufficiently well known to be removed from the signal. In this study, we extend the common narrow-band analyses to DAS response analyses covering a frequency range of five orders of magnitude ranging from ~4000 s period to frequencies up to ~100 Hz. This is based on a series of experiments in Switzerland, including (1) active controlled-source experiments with co-located seismometers and geophones, (2) low-frequency strain induced by hydraulic injection in a borehole with co-located Fiber-Bragg-Grating (FBG) strain-meters, and (3) local to teleseismic ice- and earthquake recordings with co-located broadband stations. Initial results show a site-unspecific, approximately flat instrument response for all experiments. The initial results suggest that the amplitude and phase information of DAS recordings are sufficient for conventional geophysical methods such as event localisation, full-waveform inversion, ambient noise tomography and even event magnitude estimation. Despite the promising initial results, further engagement by the DAS community is required to evaluate the DAS performance and repeatability among different interrogation units and study sites., EGUsphere

Interest in measuring seismic rotation and strain is growing in many areas of geophysical research. This results in a great need for reliable and field deployable instruments measuring ground rotation and strain. To further establish a high quality standard for rotation and strain measurements in seismology, researchers from the Ludwig-Maximilians University of Munich (LMU), the German Federal Institute for Geosciences and Natural Resources, the University of Potsdam and the ETH Zürich organized a comparative sensor test experiment which took place in November 2019 at the Geophysical Observatory of the LMU in Fürstenfeldbruck, Germany. More than 40 different sensors such as ring-laser and fiber optic gyroscopes, a Distributed Acoustic Sensing (DAS) cable and interrogator, liquid-based as well as mechanical rotation sensors were involved in addition to 12 classical broadbandseismometers and a 80 channel, 4Hz geophone chain. The experiment consisted of two parts: during the first part, the sensors were co-located in a huddle test recording self noise and signals from small, nearby explosions. In a second part, the sensors were distributed into the field in various array configurations recording active seismic signals generated by small amounts of explosive and a vibro-seis truck. This contribution presents details on the setup of the experiment and first results on sensor performance characteristics and signal similarities.

We propose a theory for rotational and strain ambient noise interferometry, motivated by the recent development of rotational ground motion sensors and distributed acoustic sensing (DAS) technology. In this context, we demonstrate that displacement, strain and rotation interferograms can be generically written in the form of a representation theorem, that is, as a solution to the seismic wave equation that we refer to as the interferometric wavefield. The physical quantity (displacement, strain or rotation) determines the distributed source of the interferometric wavefield, as well as an observational operator that extracts the correct type of noise correlation function. The proposed interferometric equations are free of assumptions on the distribution of noise sources or the equipartitioning of the ambient field, typically required for Green’s function retrieval. In addition to being valid for any kind of heterogeneous source and viscoelastic medium, they allow us to account for measurement details, such as the gauge length in DAS. We illustrate the practical feasibility of our approach with a series of numerical examples, based on regional-scale, spectral-element simulations of the interferometric wavefield. Specifically, we compare displacement and strain interferograms for homogeneous and heterogeneous earth models, and for homogeneous and heterogeneous noise sources. Ultimately, our developments are intended to enable adjoint-based waveform inversion with emerging measurement technologies that provide spatial gradient information in addition to conventional seismic displacement recordings., Geophysical Journal International, 216 (3), ISSN:0956-540X, ISSN:1365-246X