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Interpretation of Transient ElectroMagnetic (TEM) data and wire-line logs has led to the delineation of an intricate pattern of buried tunnel valleys, along with new evidence of glaciotectonically dislocated layers in recessional moraines in the central part of Vendsyssel, Denmark. The TEM data have been compared with recent results of stratigraphical investigations based on lithological and biostratigraphical analyses of borehole samples and dating with Optically Stimulated Luminescence (OSL) and radiocarbon. This has provided an overview of the spatial distribution of the late Quaternary lithostratigraphical formations, and the age of the tunnel valleys has been estimated. The tunnel valleys are typically 5-10 km long, 1 km wide and are locally eroded to depths of more than 180 m b.s.l. The valleys are interpreted to have been formed by subglacial meltwater erosion beneath the outermost part of the ice sheet during temporary standstills and minor re-advances during the overall Late Weichselian recession of the Scandinavian Ice Sheet. The formation of the tunnel valleys occurred after the retreat of the Main ice advance c. 20 kyr BP and before the Lateglacial marine inundation c. 18 kyr BP. Based on the occurrence of the tunnel valleys and the topography, four ice-marginal positions related to the recession of the northeastern Main advance and seven ice-marginal positions related to the recession from the following eastern re-advance across Vendsyssel are delineated. All the tunnel valleys were formed within a time interval of a few thousand years, giving only a few hundred years or less for the formation of the tunnel valleys at each ice-marginal position.

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.

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Airborne electromagnetic (AEM) data have proven successful for the purpose of near-surface geological mapping and are increasingly being collected worldwide. However, conversion of data from measured resistivity to lithology is not a straightforward task. Therefore, it is still challenging to make full use of these data. Many limitations must be considered before a successful geological interpretation can be performed and a reasonable 3D geological model constructed. In this paper, we propose a method for 3D geological modelling of AEM data in which the limitations are jointly considered together with a cognitive and knowledge-driven data interpretation. The modelling is performed iteratively by using voxel modelling techniques with tools developed for this exact purpose. Based on 3D resistivity grids, the tools allow the geologist to select voxel groups that define any desirable volumetric shape in the 3D model. Recent developments in octree modelling ensure exact modelling with a limited number of voxels.