Abstract
Abstract. In traditional hydrogeological investigations, one geological model is often used based on subjective interpretations and sparse data availability. This deterministic approach usually does not account for any uncertainties. Stochastic simulation methods address this problem and can capture the geological structure uncertainty. In this study the geostatistical software TProGS is utilized to simulate an ensemble of realizations for a binary (sand/clay) hydrofacies model in the Norsminde catchment, Denmark. TProGS can incorporate soft data, which represent the associated level of uncertainty. High-density (20 m × 20 m × 2 m) airborne geophysical data (SkyTEM) and categorized borehole data are utilized to define the model of spatial variability in horizontal and vertical direction, respectively, and both are used for soft conditioning of the TProGS simulations. The category probabilities for the SkyTEM data set are derived from a histogram probability matching method, where resistivity is paired with the corresponding lithology from the categorized borehole data. This study integrates two distinct data sources into the stochastic modeling process that represent two extremes of the conditioning density spectrum: sparse borehole data and abundant SkyTEM data. In the latter the data have a strong spatial correlation caused by its high data density, which triggers the problem of overconditioning. This problem is addressed by a work-around utilizing a sampling/decimation of the data set, with the aim to reduce the spatial correlation of the conditioning data set. In the case of abundant conditioning data, it is shown that TProGS is capable of reproducing non-stationary trends. The stochastic realizations are validated by five performance criteria: (1) sand proportion, (2) mean length, (3) geobody connectivity, (4) facies probability distribution and (5) facies probability–resistivity bias. In conclusion, a stochastically generated set of realizations soft-conditioned to 200 m moving sampling of geophysical data performs most satisfactorily when balancing the five performance criteria. The ensemble can be used in subsequent hydrogeological flow modeling to address the predictive uncertainty originating from the geological structure uncertainty.
Highlights
Constraints in accurate and realistic solute transport modeling in hydrogeology are caused by the difficulty in characterizing the geological structure
This non-stationary trend is shown in Fig. 5, where the additional sand–sand transition Markov chain models (MCM) are plotted that fit measured transition probability (TP) data from the northern and southern subdomain, defined by 13 %, 2 m, 400 m and 30 %, 5 m and 600 m, respectively
In the Norsminde catchment, it is evident from both borehole and geophysical data that the glacial sequence contains till clay and sand lenses distributed in extremely irregular patterns that are nonstationary
Summary
Constraints in accurate and realistic solute transport modeling in hydrogeology are caused by the difficulty in characterizing the geological structure. One geological model is built based on the best comprehensive knowledge from often sparse borehole data and subjective interpretations. This can lead to alleged correct results, for instance when addressing the water balance on catchment scale, but will often prove to be inadequate for simulations beyond general flows and heads, e.g., contaminant transport modeling. It is proposed by numerous studies that the uncertainty on the geological conceptualization is crucial when assessing uncertainties on flow paths (Neuman, 2003; Bredehoeft, 2005; Hojberg and Refsgaard, 2005; Troldborg et al, 2007; Seifert et al, 2008). One of the strategies often recommended for characterizing geological uncertainty and assessing its impact on hydrological predictive uncertainty is the use of multiple geological models (Renard, 2007; Refsgaard et al, 2012)
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