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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 8
Hydrol. Earth Syst. Sci., 18, 2943–2954, 2014
https://doi.org/10.5194/hess-18-2943-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 18, 2943–2954, 2014
https://doi.org/10.5194/hess-18-2943-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 07 Aug 2014

Research article | 07 Aug 2014

The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modelling

X. L. He1, T. O. Sonnenborg2, F. Jørgensen2, and K. H. Jensen1 X. L. He et al.
  • 1Department of Geosciences and Natural Resource Management, Copenhagen University, Copenhagen, Denmark
  • 2Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark

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.

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