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Besides classical imaging techniques, full-waveform inversion is an increasingly popular method to derive elastic subsurface properties from seismic data. High-resolution velocity models can be obtained, and spatial sampling criteria are less strict than for imaging methods, because the entire information content of the seismic waveforms is used. As high operational costs arise from seismic surveys, the acquirable data volume is often limited by economic criteria. By selecting optimal locations for seismic sources, the information content of the data can be maximized, and the number of sources and thus the acquisition costs can be reduced compared with standard acquisition designs. The computation of such optimized designs for large-size 3D inverse problems at affordable computational cost is challenging. By using a sequential receiver-wise optimization strategy, we substantially reduce the computational requirements of the optimization process. We prove the applicability of this method by means of numerical 3D acoustic examples. Optimized source designs for different receiver patterns are computed for a realistic subsurface model, and the value of the designs is evaluated by comparing checkerboard inversion tests with different acquisition designs. Our examples show that inversion results with higher accuracy can be obtained with the optimized designs, regardless of the number of sources, the number of receivers, or the receiver distribution. Larger benefits of the optimized designs are visible when a sparse receiver geometry is used., Geophysical Prospecting, 71 (1), ISSN:0016-8025, ISSN:1365-2478

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.

We present a technique to automatically classify the wave type of seismic phases that are recorded on a single six-component recording station (measuring both three components of translational and rotational ground motion) at the earth's surface. We make use of the fact that each wave type leaves a unique 'fingerprint' in the six-component motion of the sensor. This fingerprint can be extracted by performing an eigenanalysis of the data covariance matrix, similar to conventional three-component polarization analysis. To assign a wave type to the fingerprint extracted from the data, we compare it to analytically derived six-component polarization models that are valid for pure-state plane wave arrivals. For efficient classification, we make use of the supervised machine learning method of support vector machines that is trained using data-independent, analytically-derived six-component polarization models. This enables the rapid classification of seismic phases in a fully automated fashion, even for large data volumes, such as encountered in land-seismic exploration or ambient noise seismology. Once the wave-type is known, additional wave parameters (velocity, directionality, and ellipticity) can be directly extracted from the six-component polarization states without the need to resort to expensive optimization algorithms.We illustrate the benefits of our approach on various real and synthetic data examples for applications such as automated phase picking, aliased ground-roll suppression in land-seismic exploration, and the rapid close-to real time extraction of surface wave dispersion curves from single-station recordings of ambient noise. Additionally, we argue that an initial step of wave type classification is necessary in order to successfully apply the common technique of extracting phase velocities from combined measurements of rotational and translational motion.

We present a novel seismic acquisition and processing technique to efficiently evaluate the local dispersion curves of Rayleigh waves for subsequent inversion of shear velocities and near-surface characterization.The proposed approach consists of computing the ratio between the (time derivated) horizontal spectra H(f)=(∂tVx(f)2+∂tVy(f)2)1/2 and the pseudo-divergence spectra D(f), with D being the sum of the horizontal gradients of the horizontal components (i.e. D=∂xVx+∂yVy).The processing method itself is comparable to the commonly used H/V approach, except that the H/D spectral ratio provides a direct estimate of the frequency-dependent phase velocities cR(f) instead of the site frequency amplification(s). This is demonstrated using synthetic data.We describe how the D component can be obtained in practice, i.e. by finite-differencing closely spaced horizontal phones or potentially using Distributed-Acoustic-Sensing (DAS) and fibre-optic deployed at the surface. Some limitations about wavelength dependency and impact of Love waves are discussed, as well as potential mitigation measures.A field test on several hours of ambient noise data collected in Germany with multi-component geophones results in realistic values of Rayleigh wave velocities ranging from ~770 m/s at 10 Hz to ~500 m/ at 30 Hz. Thanks to the local and omni-directional nature of the estimation, the minimal number of required channels and the applicability to ambient noise, we believe that the proposed H/D method can be an attractive alternative to expensive array-based techniques.

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

Optimized experimental design aims at reducing the cost of a seismic survey by identifying the optimal locations and amounts of sources and receivers. While the acquisition design in the context of seismic imaging applies criteria like fold, offset and spatial sampling, different attributes such as the sensitivity kernels are more relevant for seismic full waveform inversion. An ideal measure to quantify the goodness of an acquisition design relies on the eigenvalue spectrum of the approximate Hessian matrix, but this technique is computationally too expensive for practical use. A more affordable goodness measure has been proposed in the past, but we demonstrate that this measure is inappropriate for target‐oriented optimized experimental design. To address those issues, we derived a sequential receiver‐based procedure using a goodness measure based on the determinant of the approximate Hessian matrix. We show with numerical tests that it efficiently provides an optimized design for target‐oriented as well as for extensive full waveform inversion. This design allows a better reconstruction of the subsurface than an evenly spaced acquisition geometry. Furthermore, the optimization algorithm itself can easily be parallelized, therefore making it attractive for applications to large‐scale three‐dimensional surveys. In addition, our algorithm is able to incorporate variable costs, representing any kind of acquisition‐related costs, for every individual source location. The combined optimization with respect to the information content of sources and to the true cost will allow a more comprehensive and realistic survey planning and has a high potential for further applications.

We present a technique to automatically classify the wave type of seismic phases that are recorded on a single six-component recording station (measuring both three components of translational and rotational ground motion) at the Earth's surface. We make use of the fact that each wave type leaves a unique 'fingerprint' in the six-component motion of the sensor (i.e. the motion is unique for each wave type). This fingerprint can be extracted by performing an eigenanalysis of the data covariance matrix, similar to conventional three-component polarization analysis. To assign a wave type to the fingerprint extracted from the data, we compare it to analytically derived six-component polarization models that are valid for pure-state plane wave arrivals. For efficient classification, we make use of the supervised machine learning method of support vector machines that is trained using data-independent, analytically derived six-component polarization models. This enables the rapid classification of seismic phases in a fully automated fashion, even for large data volumes, such as encountered in land-seismic exploration or ambient noise seismology. Once the wave-type is known, additional wave parameters (velocity, directionality and ellipticity) can be directly extracted from the six-component polarization states without the need to resort to expensive optimization algorithms. We illustrate the benefits of our approach on various real and synthetic data examples for applications such as automated phase picking, aliased ground-roll suppression in land-seismic exploration and the rapid close-to real-time extraction of surface wave dispersion curves from single-station recordings of ambient noise. Additionally, we argue that an initial step of wave type classification is necessary in order to successfully apply the common technique of extracting phase velocities from combined measurements of rotational and translational motion., Geophysical Journal International, 234 (1), ISSN:0956-540X, ISSN:1365-246X

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 built a five-component (5C) land seismic sensor that measures both the threecomponent (3C) particle acceleration and two vertical gradients of the horizontal wavefield through a pair of 3C microelectromechanical accelerometers. The sensor is a small cylindrical device planted vertically just below the earth’s surface. We show that seismic acquisition and processing 5C sensor data has the potential to replace conventional seismic acquisition with analogue geophone groups by single 5C sensors placed at the same station interval when combined with a suitable aliased ground roll attenuation algorithm. The 5C sensor, therefore, allows for sparser, more efficient, data acquisition. The accuracy of the 5C sensor wavefield gradients depends on the 3C accelerometers, their sensitivity, self-noise and their separation. These sensor component specifications are derived from various modelling studies. The design principles of the 5C sensor are validated using test data from purpose-built prototypes. The final prototype was constructed with a pair of 3C accelerometers separated by 20 cm and with a self-noise of 35 ng Hz−1/2. Results from a two-dimensional seismic line show that the seismic image of 5C sensor data with ground roll attenuated using 5C sensor gradient data was comparable to simulated analogue group data as is the standard in the industry. This field example shows that up to three times aliased ground roll was attenuated. The 5C sensor also allows for correcting vertical component accelerometer data for sensor tilt. It is shown that a vertical component sensor that is misaligned with the vertical direction by 10° introduces an error in the seismic data of around –20 dB with respect to the seismic signal, which can be fully corrected. Advances in sensor specifications and processing algorithms are expected to lead to even more effective ground roll attenuation, enabling a reduction in the receiver density resulting in a smaller number of sensors that must be deployed and, therefore, improving the operational efficiency while maintaining image quality.

This paper explores the concept of pressure measurements in a land seismic acquisition setting. We first review the theory for pressure measurements near the surface of the Earth and show the significance of the S-to-P conversion which results in the pressure being proportional to the sum of the slowness-scaled horizontal velocity fields. In the second part of this paper we test a land hydrophone prototype via a small-scale experiment to validate the pseudopressure measurement. Potential applications are briefly discussed. This study suggests that such a land hydrophone can potentially allow local and omni-directional noise attenuation, via adaptive subtraction using the pseudo-pressure data as noise model, and therefore can potentially allow sparser acquisition geometries with associated field effort and cost reduction.

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

Recent progress in rotational sensor technology has made it possible to directly measure rotational ground-motion induced by seismic waves. When combined with conventional inertial seismometer recordings, the new sensors allow one to locally observe six degrees of freedom (6DOF) of ground-motion, composed of three orthogonal components of translational motion and three orthogonal components of rotational motion. The applications of such 6DOF measurements are manifold—ranging from wavefield characterization, separation, and reconstruction to the reduction of non-uniqueness in seismic inverse problems—and have the potential to revolutionize the way seismic data are acquired and processed. However, the seismological community has yet to embrace rotational ground-motion as a new observable. The aim of this paper is to give a high-level introduction into the field of 6DOF seismology using illustrative examples and to summarize recent progress made in this relatively young field. It is intended for readers with a general background in seismology. In order to illustrate the seismological value of rotational ground-motion data, we provide the first-ever 6DOF processing example of a teleseismic earthquake recorded on a multicomponent ring laser observatory and demonstrate how wave parameters (phase velocity, propagation direction, and ellipticity angle) and wave types of multiple phases can be automatically estimated using single-station 6DOF processing tools. Python codes to reproduce this processing example are provided in an accompanying Jupyter notebook., Sensors, 20 (23), ISSN:1424-8220

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.

Interest in measuring displacement gradients, such as rotation and strain, is growing in many areas of geophysical research. This results in an urgent demand for reliable and field-deployable instruments measuring these quantities. In order to further establish a high-quality standard for rotation and strain measurements in seismology, we organized a comparative sensor test experiment that took place in November 2019 at the Geophysical Observatory of the Ludwig-Maximilians University Munich in Fürstenfeldbruck, Germany. More than 24 different sensors, including three-component and single-component broadband rotational seismometers, six-component strong-motion sensors and Rotaphone systems, as well as the large ring laser gyroscopes ROMY and a Distributed Acoustic Sensing system, were involved in addition to 14 classical broadband seismometers and a 160 channel, 4.5 Hz 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 seismic signals that were generated by small amounts of explosive and a Vibroseis truck. This paper presents details on the experimental setup and a first sensor performance comparison focusing on sensor self-noise, signal-to-noise ratios, and waveform similarities for the rotation rate sensors. Most of the sensors show a high level of coherency and waveform similarity within a narrow frequency range between 10 Hz and 20 Hz for recordings from a nearby explosion signal. Sensor as well as experiment design are critically accessed revealing the great need for reliable reference sensors., Sensors, 21 (1), ISSN:1424-8220

We have introduced a workflow that allows subsurface imaging using upcoming body-wave arrivals extracted from ambient-noise land seismic data. Rather than using the conventional seismic interferometry approach based on correlation, we have developed a deconvolution technique to extract the earth response from the observed periodicity in the seismic traces. The technique consists of iteratively applying a gapped spiking deconvolution, providing multiple-free images with higher resolution than conventional correlation. We have validated the workflow for zero-offset traces with simple synthetic data and real data recorded during a small point-receiver land seismic survey.

Rotation measurements have found applications in various fields of geophysics ranging from near-surface archaeological mapping to large scale upper mantle tomographic inversion. In the absence of a cost-effective sensor sensitive over the typical seismic frequency bandwidth, rotation measurement can be approximated by finite-differencing the response of the vertical particle velocity over a short distance. Accurate estimates of the horizontal gradient of the vertical wavefield can be obtained when perturbations associated with the measurement are minimised. These perturbations can be sensor related, for example geophone sensitivity and natural frequency, and/or deployment related such as tilt and coupling errors. We developed a “sensitivity” chart ranking these perturbations according to impact. We recommend the use of geophones with low variance in sensitivity, deployed as vertical as possible (less than 5 degrees tilt error) at highly accurate position (error in the order of centimetres). Moreover, the distance between the two receivers should be about 1/8 of the minimum wavelength in order to reduce the error due to finite-differencing.

A novel acquisition and processing technique is described to derive surface-wave dispersion curves from seismic ambient noise. We have determined that the use of spatial gradients of the wavefield provides new opportunities for high-resolution near-surface characterization with minimal field effort. In contrast to conventional active source data analysis that provides spatially smoothed results from large and dense arrays of receivers, our method provides local phase velocity information from ambient noise using only three closely spaced geophones. A time-frequency domain polarization-based analysis scheme is implemented to (1) detect the useful part of the data satisfying fundamental gradiometry assumptions, (2) estimate the noise source azimuthal distribution, and finally, (3) derive the desired local dispersion curves. The robustness and effectiveness of the developed method is demonstrated using the synthetic and real data, with a comparison to results from active source measurements.

SUMMARY This paper deals with theoretical aspects of wavefield decomposition of Ocean Bottom Cable (OBC) data in the τ –p domain, considering a horizontally layered medium. We present both the acoustic decomposition and elastic decomposition procedures in a simple and compatible way. Acoustic decomposition aims at estimating the primary upgoing P wavefield just above the ocean-bottom, whereas elastic decomposition aims at estimating the primary upgoingP and S wavefields just below the ocean-bottom. Specific issues due to the interference phenomena at the receiver level are considered. Our motivation is to introduce the two-step decomposition scheme called ‘receiver function’ (RF) decomposition that aims at determining the primary upgoing P and S wavefields (RFP and RFS, free of any water layer multiples). We show that elastic decomposition is a necessary step (acting as pre-conditioning) before applying the multiple removal step by predictive deconvolution. We show the applicability of our algorithm on a synthetic data example.

Rotational components just below the free-surface can be estimated by differencing closely spaced vertical geophones. This is particularly convenient because dedicated rotational sensors do not yet have the high sensitivity and robustness for commercial use in the seismic industry. In this paper, we show that this new data type contains highly desirable information about the seismic wavefield. For instance, they provide a direct measurement of the ray parameter, or equivalently, the apparent velocity of the vertically polarized wavefield. Compared to conventional traveltime methods, this has the advantage of being completely local and, therefore, does not require long and dense arrays of receivers to be deployed. Potential applications include, among others, wavefield interpolation, wavefield decomposition, near-surface characterization and ground-roll attenuation.

We present a novel acquisition and processing technique for crossline scattered noise attenuation. The process is based on the use of crossline rotational data to provide a crossline scattered noise model and adaptively subtract it from the data, while the inline propagating noise is removed using conventional array-based methods. Synthetic tests demonstrate the effectiveness of the technique, with significant improvement thanks to the new type of data. Data from a field test also demonstrate the improvement offered by the new technique.

A 2D land seismic data acquisition method that combines measurements of the vertical component particle velocity and the horizontal rotation rate of the wavefield is presented. It is shown that the station spacing of such a survey can be increased to beyond that corresponding to the spatial Nyquist wavenumber. An algorithm is described that can separate once-aliased data from the vertical component wavefield data. The separated data can be used for wavefield interpolation. A field experiment shows that rotation rate sensors can directly measure at the free surface, the required horizontal gradient of the vertical component of the wavefield. A synthetic data set is used to demonstrate the algorithm for the separation and interpolation of regularly spaced aliased data.

Summary We introduce a workflow that allows subsurface imaging using the upcoming body-wave arrivals extracted from ambient noise land data. Rather than using the conventional seismic interferometry approach (based on correlation), we propose a specific deconvolution technique to extract the earth response from the observed periodicity in the seismic traces. This technique consists of iteratively applying a gapped spiking deconvolution, providing sharper images, with higher resolution than conventional correlation. Our workflow is validated with simple synthetic data and real data recorded during a small point-receiver land seismic

We describe a novel S-wave near-surface characterization tool based on P-wave polarization analysis of multicomponent land data and apply this method to an arctic dataset. Taking into account the free surface effect (i.e., the additional reflection at the solid/air interface), it can be demonstrated that the measured polarization angle of the P-wave depends only on the S-wave velocity near the surface for a given horizontal slowness. Therefore, by isolating a pure P event of known horizontal slowness (typically, the first break), we are able to estimate the S-wave velocity profile at each receiver location. Such an approach can be extended to a frequency-dependent procedure to derive a pseudodepth model. Synthetic tests validate the method and application to a challenging point-receiver arctic dataset shows results that correlate with known near-surface features and independently derived P-wave static estimates. The method is both robust and reliable, and the derived near-surface velocity estimated can potentially be used to identify shallow drilling hazards, P-S wavefield separation, and possibly static corrections.

We describe a novel S-wave near-surface characterization tool based on P-wave polarization analysis of multicomponent land data and apply this method to an arctic dataset. Taking into account the free surface effect (i.e., the additional reflection at the solid/air interface), it can be demonstrated that the measured polarization angle of the P-wave depends only on the S-wave velocity near the surface for a given horizontal slowness. Therefore, by isolating a pure P event of known horizontal slowness (typically, the first break), we are able to estimate the S-wave velocity profile at each receiver location. Such an approach can be extended to a frequency-dependent procedure to derive a pseudodepth model. Synthetic tests validate the method and application to a challenging point-receiver arctic dataset shows results that correlate with known near-surface features and independently derived P-wave static estimates. The method is both robust and reliable, and the derived near-surface velocity estimated can potentially be used to identify shallow drilling hazards, P-S wavefield separation, and possibly static corrections.

P195 Estimation of the Primary PS Wave Response in OBC Data Using the Receiver Function Approach P. Edme* (Schlumberger Cambridge Research (formerly at the Institut de Physique du Globe de Paris-IPGP)) & S.C. Singh (Institut de Physique du Globe de Paris-IPGP) SUMMARY Multi-component recordings provide informations on both the P and S wave structure of the subsurface but most of the methods are developed for the P waves. It is necessary to develop new techniques to isolate the PS wavefield from mixed components. In OBC data it is also necessary to remove the water multiples to improve the sub-seafloor image ...