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Discussion papers | Copyright
https://doi.org/10.5194/hess-2017-734
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Feb 2018

Research article | 26 Feb 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

Contributions to uncertainty related to hydrostratigraphic modeling using Multiple-Point Statistics

Adrian A. S. Barfod1,2, Troels N. Vilhelmsen2, Flemming Jørgensen1, Anders V. Christiansen2, Julien Straubhaar3, and Ingelise Møller1 Adrian A. S. Barfod et al.
  • 1Derpartment of Groundwater and Quaternary Geological Mapping, Geological Survey of Denmark & Greenland (GEUS), C. F. Møllers Allé 8, 8000 Aarhus C, Denmark
  • 2Hydrogeophysics Group, Department of Geoscience, Aarhus University, C. F. Møllers Allé 4, 8000 Aarhus C, Denmark
  • 3Centre d'Hydrogéologie et de Géothermie (CHYN), Université de Neuchâtel, Switzerland

Abstract. Forecasting the flow of groundwater requires a hydrostratigraphic model, which describes the architecture of the subsurface. State-of-the-art Multiple-Point Statistical (MPS) tools are readily available for creating models depicting subsurface geology. We present a study of the impact of key parameters related to stochastic MPS simulation of a real-world hydrogeophysical dataset from Kasted Denmark using the snesim algorithm. The goal is to study how changes to the underlying datasets 5 propagate into the hydrostratigraphic realizations when using MPS for stochastic modeling. This study focuses on the sensitivity of the MPS realizations to the geophysical soft data, borehole lithology logs, and the Training Image (TI). The modeling approach used in this paper utilizes a cognitive geological model as a TI to simulate ensemble hydrostratigraphic models. The target model contains three overall hydrostratigraphic categories, and the MPS realizations are compared visually, as well as quantitatively using mathematical measures of similarity. The quantitative similarity analysis is carried 10 out exhaustively, and realizations are compared with each other as well as with the cognitive geological model.

The results underline the importance of geophysical data for constraining MPS simulations. Relying only on borehole data and the conceptual geology or TI, results in a significant increase in realization uncertainty. The SkyTEM data used in this study cover a large portion of the Kasted model area, and are essential to the hydrostratigraphic architecture. On the other hand, the borehole lithology logs are sparse, and only 410 boreholes were present in this study. The borehole lithology logs 15 infer local changes in the immediate vicinity of the boreholes, thus providing limited large-scale structural information. Lithological information is, however, important for the interpretation of the geophysical responses. Finally, the importance of the TI was studied. An example was presented where an alternative geological model from a neighboring area was used to simulate hydrostratigraphic models. It was shown that as long as the geological setting are similar in nature, the realizations, although different, still reflect the hydrostratigraphic architecture. If a TI containing a biased geological conceptualization is 20 used, the resulting realizations will resemble the TI and contain less structure in particular areas, where the soft data show almost even probability to two or all three of the hydrostratigraphic units.

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Short summary
The focus of this study is on the uncertainty related to using Multiple-Point Statistics (MPS) for stochastic modelling of the upper 200 m of the subsurface. The main research goal is to showcase how MPS methods can be used on real-world hydrogeophysical data and show how the uncertainty related to changing the underlying MPS setup propagates into the finalized 3-D subsurface models.
The focus of this study is on the uncertainty related to using Multiple-Point Statistics (MPS)...
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