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Here we present new measurements of an anisotropic ice fabric in a fast moving (377 ma−1) ice stream in West Antarctica. We use ∼6000 measurements of shear wave splitting observed in microseismic signals from the bed of Rutford Ice Stream, to show that in contrast to large‐scale ice flow models, which assume that ice is isotropic, the ice in Rutford Ice Stream is dominated by a previously unobserved type of partial girdle fabric. This fabric has a strong directional contrast in mechanical properties, shearing 9.1 times more easily along the ice flow direction than across flow. This observed fabric is likely to be widespread and representative of fabrics in other ice streams and large glaciers, suggesting it is essential to consider anisotropy in data‐driven models to correctly predict ice loss and future flow in these regions. We show how passive microseismic monitoring can be effectively used to provide these data. Anisotropic ice fabric in an Antarctic ice stream measured using seismic shear wave splittingMeasured fabric results in ice shearing 9.1 times more easily along flow direction than across flowThe measured fabric has strong implications for parametrizing data‐driven models of past and future ice flow

Firn densification profiles are an important parameter for ice‐sheet mass balance and palaeoclimate studies. One conventional method of investigating firn profiles is using seismic refraction surveys, but these are difficult to upscale to large‐area measurements. Distributed acoustic sensing (DAS) presents an opportunity for large‐scale seismic measurements of firn with dense spatial sampling and easy deployment, especially when seismic noise is used. We study the feasibility of seismic noise interferometry (SI) on DAS data for characterizing the firn layer at the Rutford Ice Stream, West Antarctica. Dominant seismic energy appears to come from anthropogenic noise and shear‐margin crevasses. The DAS cross‐correlation interferometry yields noisy Rayleigh wave signals. To overcome this, we present two strategies for cross‐correlations: (a) hybrid instruments—correlating a geophone with DAS, and (b) stacking of selected cross‐correlation panels picked in the tau‐p domain. These approaches are validated with results derived from an active survey. Using the retrieved Rayleigh wave dispersion curve, we inverted for a high‐resolution 1D S‐wave velocity profile down to a depth of 100 m. The profile shows a “kink” (velocity gradient inflection) at ∼12 m depth, resulting from a change of compaction mechanism. A triangular DAS array is used to investigate directional variation in velocity, which shows no evident variations thus suggesting a lack of azimuthal anisotropy in the firn. Our results demonstrate the potential of using DAS and SI to image the near‐surface and present a new approach to derive S‐velocity profiles from surface wave inversion in firn studies. The density distribution (density change with depth) over tens of meters at the top of a glacier is an important feature of ice‐sheet mass balance and palaeoclimate research. It can be estimated using the empirical relationship between density and seismic P‐wave velocity. The P‐wave velocity can be measured using a seismic refraction survey with geophones and active sources. However, refraction seismic surveys are expensive for measurements over large areas. Distributed Acoustic Sensing (DAS) using fiber optic cables to detect seismic waves is an emerging dense spatial sampling seismic acquisition technology. It can be used in conjunction with seismic noise cross‐correlation to make large‐scale measurements easier and cheaper than with conventional geophones. We investigate the feasibility of this approach on Rutford Ice Stream, West Antarctica, and propose two approaches to improve DAS seismic‐noise cross‐correlation results. Surface waves are retrieved by seismic noise cross‐correlation and are used to estimate the S‐wave velocity structure. Our S‐velocity profile resembles an independently measured P‐velocity in‐shape and presents a velocity gradient inflection—related to changes in the snow compaction mechanism. We show that DAS and seismic noise interferometry can be used for future firn measurements, but also more generally in studies of the near‐surface. Distributed acoustic sensing (DAS) is used for the first time to derive the S‐wave velocity structure and anisotropy of the firn layer in AntarcticaDAS seismic interferograms are greatly improved through selective stacking and cross‐correlation with a geophoneOur method is suitable for large‐scale measurements and is feasible in the presence of ice lenses where refraction methods are inadequate Distributed acoustic sensing (DAS) is used for the first time to derive the S‐wave velocity structure and anisotropy of the firn layer in Antarctica DAS seismic interferograms are greatly improved through selective stacking and cross‐correlation with a geophone Our method is suitable for large‐scale measurements and is feasible in the presence of ice lenses where refraction methods are inadequate

Observations of surface lowering on Larsen C Ice Shelf (LCIS), Antarctica, have prompted concern about its stability. In this study, an ocean model is used to investigate the extent to which changes in ocean forcing may have influenced ice loss and the distribution of stabilizing marine ice beneath LCIS. The model uses a new bathymetry, containing a southern seabed trough discovered using seismic observations. The modeled extent of marine ice, thought to stabilize LCIS, is in good agreement with observations. Experiments applying idealized ocean warming yield an increase in melting over the southern trough. This is inconsistent with lowering observed in northern LCIS, suggesting oceanic forcing is not responsible for that signal. The marine ice extent and thickness reduces significantly under ocean warming, implying a high sensitivity of LCIS stability to changes in ocean forcing. This result could have wide implications for other cold‐water ice shelves around Antarctica. Satellite observations have revealed a lowering in recent decades of the surface of Larsen C Ice Shelf (LCIS), Antarctica, which has led to concern about its stability. By modeling ocean conditions under LCIS, we investigate the extent to which ocean melting may have caused the ice to thin, leading to the observed lowering, or altered the pattern of marine ice beneath LCIS. Marine ice forms when seawater freezes to the base of the ice shelf, and is thought to stabilize LCIS. The model uses a new seabed data set that contains a wide, deep seabed trough in the south, found by a seismic survey. In modeled ocean warming experiments, an increase in melting is concentrated in this southern region. However, greater lowering has been observed in the north, suggesting that changes in ocean conditions are not responsible for the lowering. The calculated pattern of marine ice at the base of LCIS looks similar to the observed pattern. With a warmer ocean, marine ice is significantly reduced in crucial regions of the ice shelf. This shows that the stability of LCIS is sensitive to changes in ocean conditions and other ice shelves around Antarctica are likely sensitive to these changes too. An ocean model with new Larsen C bathymetry shows greatest melting and melt sensitivity where seismic data indicate a deep southern troughThe calculated marine ice distribution under Larsen C Ice Shelf, based on model melt/freeze rates, is in good agreement with observationsA reduction in marine ice with ocean warming implies a threat to Larsen C stability, with wide implications for cold‐water ice shelves An ocean model with new Larsen C bathymetry shows greatest melting and melt sensitivity where seismic data indicate a deep southern trough The calculated marine ice distribution under Larsen C Ice Shelf, based on model melt/freeze rates, is in good agreement with observations A reduction in marine ice with ocean warming implies a threat to Larsen C stability, with wide implications for cold‐water ice shelves

Basal properties beneath ice streams and glaciers are known to be a control for ice flow dynamics, hence knowledge of them is crucial for predicting sea level due to changes in glacial dynamics. Basal properties, processes and topography also drive the formation of subglacial landforms. Bed properties beneath Rutford Ice Stream (West Antarctica) have previously been described using seismic acoustic impedance measurements at a sparse spatial coverage. Here, we derive bed properties in a 15 × 17 km grid of surface radar data with coverage and sampling much higher than previous seismic studies. Bed reflection amplitudes in surface radar data were calibrated using sediment porosities (ranging from 0.4–0.5) derived from seismic acoustic impedance. We find the bed properties are spatially variable, consisting of low porosity material in some areas and soft sediment in other areas. Comparison of seismic and surface radar data imply the low porosity material to be a consolidated sediment or sedimentary rock. Mega‐scale glacial lineations (MSGLs) are ubiquitous on the bed and consist of soft, high porosity, probably deforming sediment, consistent with previous interpretations of MSGLs. We find some MSGLs have high reflectivity on their crest, interpreted as water bodies overlying high porosity sediment, whereas the trough around and the upstream end of some landforms consist of low porosity material. Integrating these different observations, we place constraints on possible explanations for the occurrence of water on the crest of landforms. Physical properties of rocks and sediments as well as the presence of water beneath ice streams and glaciers control the flow of the ice. Understanding glacial flow is important to better constrain predictions of sea‐level changes. Within this study we present physical properties of material beneath Rutford Ice Stream in West Antarctica over a 15 × 17 km area. Our data show areas of high porosity interpreted as wet sediment and areas of low porosity beneath the ∼2 km thick ice stream. Ice movement above areas of soft sediment is assumed to be maintained by sediment deformation, whereas in areas overlain by low porosity material, sliding of the ice over this material is assumed to maintain flow. The topography beneath Rutford Ice Stream shows many elongated landforms, some over 14 km long. We show that these landforms partly consist of soft sediment. Some of these seem to have a water layer on their crest, but it is not well understood why. However, there might be variations in the sediment composition of the landforms enabling the creation of this water layer. The occurrence of materials with different properties and water show us how heterogenous the bed beneath ice streams can be, which is important for modeling ice stream motion. Glacier bed properties (water content, porosity) are constrained using surface radar data calibrated with seismic acoustic impedanceResults show sequences of low porosity material (consolidated sediment or rock), soft sediment and free‐water beneath Rutford Ice StreamMega‐scale glacial lineations are pervasive beneath ice streams, some with a free water layer >10 km long and 100 m wide along their crest Glacier bed properties (water content, porosity) are constrained using surface radar data calibrated with seismic acoustic impedance Results show sequences of low porosity material (consolidated sediment or rock), soft sediment and free‐water beneath Rutford Ice Stream Mega‐scale glacial lineations are pervasive beneath ice streams, some with a free water layer >10 km long and 100 m wide along their crest

Microseismicity, induced by the sliding of a glacier over its bed, can be used to characterize frictional properties of the ice‐bed interface, which are a key parameter controlling ice stream flow. We use naturally occurring seismicity to monitor spatiotemporally varying bed properties at Rutford Ice Stream, West Antarctica. We locate 230,000 micro‐earthquakes with local magnitudes from −2.0 to −0.3 using 90 days of recordings from a 35‐station seismic network located ∼40 km upstream of the grounding line. Events exclusively occur near the ice‐bed interface and indicate predominantly flow‐parallel stick‐slip. They mostly lie within a region of interpreted stiff till and along the likely stiffer part of mega‐scale glacial lineations. Within these regions, micro‐earthquakes occur in spatially (<100 m radius) and temporally (mostly 1–5 days activity) restricted event‐clusters (up to 4,000 events), which exhibit an increase, followed by a decrease, in event magnitude with time. This may indicate event triggering once activity is initiated. Although ocean tides modulate the surface ice flow velocity, we observe little periodic variation in overall event frequency over time and conclude that water content, bed topography and stiffness are the major factors controlling microseismicity. Based on variable rupture mechanisms and spatiotemporal characteristics, we suggest the event‐clusters relate to three end‐member types of bed deformation: (1) continuous creation and seismogenic destruction of small‐scale bed‐roughness, (2) ploughed clasts, and (3) flow‐oblique deformation during landform formation or along bedrock outcrops. This indicates that multiple processes, simultaneously active during glacial sliding, can accommodate stick‐slip behavior and that the bed continuously reorganizes. We locate 230,000 micro‐earthquakes, with rupture mechanisms, at the base of a fast‐flowing West Antarctic ice stream within a 3‐month periodEvent distribution is little affected by tidal modulations and indicates basal sliding most affected by bed topography, stiffness, and fluidsEvents occur clustered, likely due to different types of bed deformation: mobile asperities, ploughed clasts and flow‐oblique bed features We locate 230,000 micro‐earthquakes, with rupture mechanisms, at the base of a fast‐flowing West Antarctic ice stream within a 3‐month period Event distribution is little affected by tidal modulations and indicates basal sliding most affected by bed topography, stiffness, and fluids Events occur clustered, likely due to different types of bed deformation: mobile asperities, ploughed clasts and flow‐oblique bed features

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The article talks about the application of 3G, 4G, and Long-Term Evolution (LTE) connectivity for new Machine to Machine (M2M) applications and capabilities and discusses a few applications like digital signage and business continuity solutions.

the article talks about the impact of Internet of Things (IoT) and Big Data on capacity of machine to machine (M2M) networks. Topics discussed include cellular-connected M2M applications, application of Iot in healthcare, and Big Data in context with network bandwidth. Other topic discussed include implementation of sensors monitoring in modern automobile.