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In traditional hydrogeological investigations, one geological model is oftenused based on subjective interpretations and sparse data availability. Thisdeterministic approach usually does not account for any uncertainties.Stochastic simulation methods address this problem and can capture thegeological structure uncertainty. In this study the geostatistical softwareTProGS is utilized to simulate an ensemble of realizations for a binary(sand/clay) hydrofacies model in the Norsminde catchment, Denmark. TProGScan incorporate soft data, which represent the associated level ofuncertainty. High-density (20 m × 20 m × 2 m) airborne geophysical data(SkyTEM) and categorized borehole data are utilized to define the model ofspatial variability in horizontal and vertical direction, respectively, andboth are used for soft conditioning of the TProGS simulations. The categoryprobabilities for the SkyTEM data set are derived from a histogramprobability matching method, where resistivity is paired with thecorresponding lithology from the categorized borehole data. This studyintegrates two distinct data sources into the stochastic modeling processthat represent two extremes of the conditioning density spectrum: sparseborehole data and abundant SkyTEM data. In the latter the data have a strongspatial correlation caused by its high data density, which triggers theproblem of overconditioning. This problem is addressed by a work-aroundutilizing a sampling/decimation of the data set, with the aim to reduce thespatial correlation of the conditioning data set. In the case of abundantconditioning data, it is shown that TProGS is capable of reproducingnon-stationary trends. The stochastic realizations are validated by fiveperformance criteria: (1) sand proportion, (2) mean length, (3) geobodyconnectivity, (4) facies probability distribution and (5) facies probability–resistivitybias. In conclusion, a stochastically generated set ofrealizations soft-conditioned to 200 m moving sampling of geophysical dataperforms most satisfactorily when balancing the five performance criteria. Theensemble can be used in subsequent hydrogeological flow modeling to addressthe predictive uncertainty originating from the geological structureuncertainty

Challenges in conditioning a stochastic geological model of a heterogeneous glacial aquifer to a comprehensive soft data set