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Distributed Acoustic Sensing (DAS) is increasingly recognised as a valuable tool for glaciological seismic applications, although analysing the large data volumes generated in acquisitions poses computational challenges. We show the potential of active-source DAS to image and characterise subglacial sediment beneath a fast-flowing Greenlandic outlet glacier, estimating the thickness of sediment layers to be 20–30 m. However, the lack of subglacial velocity constraint limits the accuracy of this estimate. Constraint could be provided by analysing cryoseismic events in a counterpart 3-day record of passive seismicity through, for example, seismic tomography, but locating them within the 9 TB data volume is computationally inefficient. We describe experiments with data compression using the frequency-wavenumber (f-k) transform ahead of training a convolutional neural network, that provides a ~300-fold improvement in efficiency. In combining active and passive-source and our machine learning framework, the potential of large DAS datasets could be unlocked for a range of future applications. [ABSTRACT FROM AUTHOR]

Distributed Acoustic Sensing (DAS) is increasingly recognised as a valuable tool for glaciological seismic applications, although analysing the large data volumes generated in acquisitions poses computational challenges. We show the potential of active-source DAS to image and characterise subglacial sediment beneath a fast-flowing Greenlandic outlet glacier, estimating the thickness of sediment layers to be 20–30 m. However, the lack of subglacial velocity constraint limits the accuracy of this estimate. Constraint could be provided by analysing cryoseismic events in a counterpart 3-day record of passive seismicity through, for example, seismic tomography, but locating them within the 9 TB data volume is computationally inefficient. We describe experiments with data compression using the frequency-wavenumber (f-k) transform ahead of training a convolutional neural network, that provides a ~300-fold improvement in efficiency. In combining active and passive-source and our machine learning framework, the potential of large DAS datasets could be unlocked for a range of future applications. [ABSTRACT FROM AUTHOR]

Subglacial hydrological systems require innovative technological solutions to access and observe. Wireless sensor platforms can be used to collect and return data, but their performance in deep and fast-moving ice requires quantification. We report experimental results from Cryoegg: a spherical probe that can be deployed into a borehole or moulin and transit through the subglacial hydrological system. The probe measures temperature, pressure and electrical conductivity in situ and returns all data wirelessly via a radio link. We demonstrate Cryoegg's utility in studying englacial channels and moulins, including in situ salt dilution gauging. Cryoegg uses VHF radio to transmit data to a surface receiving array. We demonstrate transmission through up to 1.3 km of cold ice – a significant improvement on the previous design. The wireless transmission uses Wireless M-Bus on 169 MHz; we present a simple radio link budget model for its performance in cold ice and experimentally confirm its validity. Cryoegg has also been tested successfully in temperate ice. The battery capacity should allow measurements to be made every 2 h for more than a year. Future iterations of the radio system will enable Cryoegg to transmit data through up to 2.5 km of ice. [ABSTRACT FROM AUTHOR]

While hot-water drilling is a well-established technique used to access the subsurface of ice masses, drilling into high-elevation (≳ 4000 m a.s.l.) debris-covered glaciers faces specific challenges. First, restricted transport capacity limits individual equipment items to a volume and mass that can be slung by small helicopters. Second, low atmospheric oxygen and pressure reduces the effectiveness of combustion, limiting a system's ability to pump and heat water. Third, thick supraglacial debris, which is both highly uneven and unstable, inhibits direct access to the ice surface, hinders the manoeuvring of equipment and limits secure sites for equipment placement. Fourth, englacial debris can slow the drilling rate such that continued drilling becomes impracticable and/or boreholes deviate substantially from vertical. Because of these challenges, field-based englacial and subglacial data required to calibrate numerical models of high-elevation debris-covered glaciers are scarce or absent. Here, we summarise our experiences of hot-water drilling over two field seasons (2017–2018) at the debris-covered Khumbu Glacier, Nepal, where we melted 27 boreholes up to 192 m length, at elevations between 4900 and 5200 m a.s.l. We describe the drilling equipment and operation, evaluate the effectiveness of our approach and suggest equipment and methodological adaptations for future use. [ABSTRACT FROM AUTHOR]

The phase-sensitive radio-echo sounder (pRES) is a powerful new instrument that can measure the depth of internal layers and the glacier bed to millimetre accuracy. We use a stationary 16-antenna pRES array on Store Glacier in West Greenland to measure the three-dimensional orientation of dipping internal reflectors, extending the capabilities of pRES beyond conventional depth sounding. This novel technique portrays the effectiveness of pRES in deriving the orientation of dipping internal layers that may complement profiles obtained through other geophysical surveying methods. Deriving ice vertical strain rates from changes in layer depth as measured by a sequence of pRES observations assumes that the internal reflections come from vertically beneath the antenna. By revealing the orientation of internal reflectors and the potential deviation from nadir of their associated reflections, the use of an antenna array can correct this assumption. While the array configuration was able to resolve the geometry of englacial layers, the same configuration could not be used to accurately image the glacier bed. Here, we use simulations of the performance of different array geometries to identify configurations that can be tailored to study different types of basal geometry for future deployments. [ABSTRACT FROM AUTHOR]