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

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.

Spatial validation of large‐scale land surface models against monthly land surface temperature patterns using innovative performance metrics

“…Within the field of hydrogeology, connectivity is a widespread measure to characterize the heterogeneity of an aquifer [ dell Arciprete et al ., ; Koch et al ., ]. From a hydrogeological perspective, the degree of connectivity has direct physical implications on groundwater flow and solute transport.…”

Modeling Depth of the Redox Interface at High Resolution at National Scale Using Random Forest and Residual Gaussian Simulation

“…The clear benefit of geostatistical techniques such as kriging is that uncertainties are estimated at grid scale (Hengl et al, ). Geostatistical methods are well established in the field of water resources research to interpolate sparse data sets and to assess uncertainty of subsurface, surface, and atmospheric variables (Abdu et al, ; Bárdossy & Li, ; Koch et al, ). As an alternative, machine learning methods provide tools to estimate uncertainty as well, but their application is so far limited to digital soil mapping (Szatmári & Pásztor, ; Vaysse & Lagacherie, ).…”

Large-Scale $$\hbox {CO}_2$$ CO 2 Storage in a Deep Saline Aquifer: Uncertainties in Predictions Due to Spatial Variability of Flow Parameters and Their Modeling

“…The risk assessment cannot rely on average results. In addition, the problem of uncertainties in geological media may have to be addressed through stochastic modeling (Renard 2007;Koch et al 2014). Multiple realizations of a geological model could be needed to obtain uncertainty estimates.…”