Is groundwater abundant in Antarctica and does it modulate ice flow? Answering this question matters because ice streams flow by gliding over a wet substrate of till. Water fed to icestream beds thus influences ice-sheet dynamics and, potentially, sea-level rise. It is recognized that both till and the sedimentary basins from which it originates are porous and could host a reservoir of mobile groundwater that interacts with the subglacial interfacial system. According to recent numerical modelling, up to half of all water available for basal lubrication, and time lags between hydrological forcing and ice-sheet response as long as millennia, may have been overlooked in models of ice flow. Here, we review evidence in support of Antarctic groundwater and propose how it can be measured to ascertain the extent to which it modulates ice flow. We present new seismoelectric soundings of subglacial till, and magnetotelluric and transient electromagnetic forward models of subglacial groundwater reservoirs. We demonstrate that multifaceted and integrated geophysical datasets can detect, delineate and quantify the groundwater contents of subglacial sedimentary basins and, potentially, monitor groundwater exchange rates between subglacial till layers. The paper thus describes a new area of glaciological investigation and how it should progress in future.Gold Open Access: This article is published under the terms of the CC-BY 3.0 license. Water beneath the ice sheetAntarctic ice-sheet flow is fundamentally affected by water at the bed, as it reduces basal friction to encourage sliding and weakens till to enable bed deformation. Subglacial hydrology -the flow of water beneath the ice -is therefore a key element of the ice-sheet system. Studies to date on subglacial hydrology, and its impact on ice flow, have concentrated on water at or very near to the bed of the ice sheet.Basal water modulation of ice flow can be achieved in a number of ways. Over an impermeable bed, water can flow through channels cut either downwards into the substrate or upwards into the ice. Enhanced basal water pressures may occur where the channels and their linkages are distributed, increasing overriding ice flow through a reduction in the substrate's effective pressure. Conversely, where a well-organized channel system is formed, water pressures are lower and the hydrological effect on ice flow is reduced. If the ice stream rests on permeable subglacial till, its strength can affect ice flow as controlled by porewater pressures. High pressures lead to a reduction in material strength by pushing till grains apart, reducing bed friction and thus

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Surface nuclear magnetic resonance is a technique capable of providing insight into subsurface aquifer properties. To produce estimates of aquifer properties (such as the spatial distribution of water content and parameters controlling the duration of the nuclear magnetic resonance signal), an inversion is required. Essential to the reliable interpretation of the estimated subsurface models is an understanding of the uncertainty and correlation between the parameters in the estimated models. To quantify parameter uncertainty and correlation in the surface nuclear magnetic resonance inversion, a Markov chain Monte Carlo approach is demonstrated. Markov chain Monte Carlo approaches have been previously employed to invert surface nuclear magnetic resonance data, but the primary focus has been on quantifying parameter uncertainty. The focus of this paper is to further investigate whether the parameters in the estimated models exhibit correlation with one another; equally important to building a reliable interpretation of the subsurface is an understanding of the parameter uncertainty. The utility of the Markov chain Monte Carlo approach is demonstrated through the investigation of three questions. The first question investigates whether the parameters describing the water content and thickness of a layer exhibit a strong correlation. This question stems from applying concepts known to electromagnetic surveys (that the layer thickness and layer resistivity parameters are strongly correlated) to the surface nuclear magnetic resonance inversion. A water content–layer thickness correlation in surface nuclear magnetic resonance would not have large effects for quantifying total water content but would affect the ability to identify layer boundaries. The second question examines whether the parameter controlling the duration of the nuclear magnetic resonance signal exhibits a correlation with the water content and layer thickness parameters. The resolution of surface nuclear magnetic resonance typically does not consider the duration of the signal and focuses primarily on the distribution of current amplitudes that form the suite of transmit pulses. It is common to treat regions with short‐duration signal with greater uncertainty, but it is important to understand whether the signal duration controls resolution for medium to long duration signals as well. The third question explores if the parameter uncertainty produced by the Markov chain Monte Carlo approach is consistent with that produced by an alternative approach based upon the posterior covariance matrix (for the linearised inversion). The ability of the Markov chain Monte Carlo approach to more thoroughly explore the model space provides a means to improve the reliability of surface nuclear magnetic resonance aquifer characterisations by quantifying parameter uncertainty and correlation.

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Airborne electromagnetic (AEM) data is used throughout the world for mapping of mineral targets and groundwater resources. The development of technology and inversion algorithms has been tremendously over the last decade and results from these surveys are high-resolution images of the subsurface. In this keynote talk, we discuss an effective inversion algorithm, which is both subjected to intense research and development as well as production. This is the well know Laterally Constrained Inversion (LCI) and Spatial Constrained Inversion algorithm. The same algorithm is also used in a voxel setup (3D model) and for sheet inversions. An integral part of these different model discretization is an accurate modelling of the system transfer function and of auxiliary parameters like flight altitude, bird pitch, etc.