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Abstract. Multiple-point geostatistical simulation (MPS)has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from a training image (TI). However, its application in three-dimensional (3-D) simulations has been constrained by the difficulty of constructing a 3-D TI. The object-based unconditional simulation program TiGenerator may be a useful tool in this regard; yet the applicability of such parametric training images has not been documented in detail. Another issue in MPS is the integration of multiple geophysical data. The proper way to retrieve and incorporate information from high-resolution geophysical data is still under discussion. In this study, MPS simulation was applied to different scenarios regarding the TI and soft conditioning. By comparing their output from simulations of groundwater flow and probabilistic capture zone, TI from both sources (directly converted from high-resolution geophysical data and generated by TiGenerator) yields comparable results, even for the probabilistic capture zones, which are highly sensitive to the geological architecture. This study also suggests that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data (SkyTEM), but over-conditioning has to be avoided.

No abstract

Multiple-point geostatistic simulation (MPS) has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from the training image (TI). However, its application in three dimensional simulations has been constrained by the difficulty of constructing 3-D TI. The object-based TiGenerator may be a useful tool in this regard; yet the sensitivity of model predictions to the training image has not been documented. Another issue in MPS is the integration of multiple geophysical data. The best way to retrieve and incorporate information from high resolution geophysical data is still under discussion. This work shows that TI from TiGenerator delivers acceptable results when used for groundwater modeling, although the TI directly converted from high resolution geophysical data leads to better simulation. The model results also indicate that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data, but over conditioning has to be avoided

Uncertainty of groundwater model predictions has in the past mostly been related to uncertainty in the hydraulic parameters whereas uncertainty in the geological structure has not been considered to the same extent. Recent developments in theoretical methods for quantifying geological uncertainty have made it possible to consider this factor in groundwater modeling. In this study we have applied the multiple-point geostatistical method (MPS) integrated in the Stanford Geostatistical Modeling Software (SGeMS) for exploring the impact of geological uncertainty on groundwater flow patterns for a site in Denmark. Realizations from the geostatistical model were used as input to a groundwater model developed from MODFLOW within the GMS modeling environment. The uncertainty analysis was carried out in three scenarios involving simulation of groundwater head distribution and groundwater age. The first scenario implied 100 stochastic geological models all assigning the same hydraulic parameters for the same geological units. In the second scenario the same 100 geological models were subjected to model optimization where the hydraulic parameters for each of them were estimated by calibration against observations of hydraulic head and stream discharge. In the third scenario each geological model was run with 216 randomized set of parameters. The analysis documented that the uncertainty on the conceptual geological model was as significant as the uncertainty related to the embedded hydraulic parameters