We present an overview of a mature, robust and general algorithm providing a single framework for the inversion of most electromagnetic and electrical data types and instrument geometries. The implementation mainly uses a 1D earth formulation for electromagnetics and magnetic resonance sounding (MRS) responses, while the geoelectric responses are both 1D and 2D and the sheet's response models a 3D conductive sheet in a conductive host with an overburden of varying thickness and resistivity. In all cases, the focus is placed on delivering full system forward modelling across all supported types of data. Our implementation is modular, meaning that the bulk of the algorithm is independent of data type, making it easy to add support for new types. Having implemented forward response routines and file I/O for a given data type provides access to a robust and general inversion engine. This engine includes support for mixed data types, arbitrary model parameter constraints, integration of prior information and calculation of both model parameter sensitivity analysis and depth of investigation. We present a review of our implementation and methodology and show four different examples illustrating the versatility of the algorithm. The first example is a laterally constrained joint inversion (LCI) of surface time domain induced polarisation (TDIP) data and borehole TDIP data. The second example shows a spatially constrained inversion (SCI) of airborne transient electromagnetic (AEM) data. The third example is an inversion and sensitivity analysis of MRS data, where the electrical structure is constrained with AEM data. The fourth example is an inversion of AEM data, where the model is described by a 3D sheet in a layered conductive host

The occurrence of groundwater in Antarctica, particularly in the ice-free regions and along the coastal margins is poorly understood. Here we use an airborne transient electromagnetic (AEM) sensor to produce extensive imagery of resistivity beneath Taylor Valley. Regional-scale zones of low subsurface resistivity were detected that are inconsistent with the high resistivity of glacier ice or dry permafrost in this region. We interpret these results as an indication that liquid, with sufficiently high solute content, exists at temperatures well below freezing and considered within the range suitable for microbial life. These inferred brines are widespread within permafrost and extend below glaciers and lakes. One system emanates from below Taylor Glacier into Lake Bonney and a second system connects the ocean with the eastern 18 km of the valley. A connection between these two basins was not detected to the depth limitation of the AEM survey (∼350 m).

Geological heterogeneity is a very important factor to consider when developing geological models for hydrological purposes. Using statistically based stochastic geological simulations, the spatial heterogeneity in such models can be accounted for. However, various types of uncertainties are associated with both the geostatistical method and the observation data. In the present study, TProGS is used as the geostatistical modeling tool to simulate structural heterogeneity for glacial deposits in a head water catchment in Denmark. The focus is on how the observation data uncertainty can be incorporated in the stochastic simulation process. The study uses two types of observation data: borehole data and airborne geophysical data. It is commonly acknowledged that the density of the borehole data is usually too sparse to characterize the horizontal heterogeneity. The use of geophysical data gives an unprecedented opportunity to obtain high-resolution information and thus to identify geostatistical properties more accurately especially in the horizontal direction. However, since such data are not a direct measurement of the lithology, larger uncertainty of point estimates can be expected as compared to the use of borehole data. We have proposed a histogram probability matching method in order to link the information on resistivity to hydrofacies, while considering the data uncertainty at the same time. Transition probabilities and Markov Chain models are established using the transformed geophysical data. It is shown that such transformation is in fact practical; however, the cutoff value for dividing the resistivity data into facies is difficult to determine. The simulated geological realizations indicate significant differences of spatial structure depending on the type of conditioning data selected. It is to our knowledge the first time that grid-to-grid airborne geophysical data including the data uncertainty are used in conditional geostatistical simulations in TProGS. Therefore, it provides valuable insights regarding the advantages and challenges of using such comprehensive data.

The McMurdo Dry Valleys are a polar desert in coastal Antarctica, where glaciers, permafrost, ice-covered lakes, and ephemeral summer streams coexist. Liquid water is found at the surface only in lakes and in the temporary streams that feed them. Past geophysical exploration has yielded ambiguous results regarding the presence of subsurface water. In 2011, we used a helicopter-borne, time-domain electromagnetic (TDEM) sensor to map resistivity in the subsurface across the Dry Valleys. The airborne electromagnetic (AEM) method excels at finding subsurface liquid water in polar deserts, where water remains liquid under cold conditions if it is sufficiently saline, and therefore electrically conductive. Over the course of 26 h of helicopter time, we covered large portions of the Dry Valleys and vastly increased our geophysical understanding of the subsurface, particularly with respect to water. Our data show extensive subsurface low-resistivity layers approximately 150–250 m below the surface and beneath higher resistivity layers. We interpret the low-resistivity layers as geologic materials containing freeze-concentrated or “cryoconcentrated” hyper saline brines lying beneath glaciers and frozen permafrost. These brines appeared to be contiguous with surface lakes, subglacial regions, and the Ross Sea, which could indicate a regional-hydrogeologic system, wherein solutes might be transported between surface reservoirs by ionic diffusion and subsurface flow. The presence of such brines underneath glaciers might have implications for glacier movement. Systems such as this, where brines exist beneath glacial ice and frozen permafrost, may exist elsewhere in coastal Antarctica; AEM resistivity is an ideal tool to find and survey them. Our application of TDEM demonstrates that in polar subsurface environments containing conductive brines, such a diffusive electromagnetic method is superior to radar surveying in terms of depth of penetration and ability to differentiate hydrogeologic conditions.