Hydrostratigraphic modelling using multiple-point statistics and airborne transient electromagnetic methods

Adrian A. S. Barfod, Julien Straubhaar, Anne-Sophie Høyer, Júlio Hoffimann, Anders V. Christiansen, Ingelise Møller, Jef Caers
2017 Hydrology and Earth System Sciences Discussions  
Creating increasingly realistic hydrological models involves the inclusion of additional geological and geophysical data in the hydrostratigraphic modelling procedure. Using Multiple Point Statistics (MPS) for stochastic hydrostratigraphic modelling provides a degree of flexibility that allows the incorporation of elaborate datasets and provides a framework for stochastic hydrostratigraphic modelling. This paper focuses on comparing three MPS methods: <i>snesim</i>, <i>DS</i> and <i>iqsim</i>.
more » ... he MPS methods are tested and compared on a real-world hydrogeophysical survey from Kasted in Denmark, which covers an area of 45&amp;thinsp;km<sup>2</sup>. The comparison of the stochastic hydrostratigraphic MPS models is carried out in an elaborate scheme of visual inspection, mathematical similarity and consistency with boreholes. Using the Kasted survey data, a practical example for modelling new survey areas is presented. A cognitive hydrostratigraphic model of one area is used as Training Image to create a suite of stochastic hydrostratigraphic models in a new survey area. The advantage of stochastic modelling is that detailed multiple point information from one area can be easily transferred to another area considering uncertainty. <br><br> The presented MPS methods each have their own set of advantages and disadvantages. The <i>DS</i> method had average computation times of 6&amp;ndash;7&amp;thinsp;h, which is large, compared to <i>iqsim</i> with average computation times of 10&amp;ndash;12&amp;thinsp;min. However, <i>iqsim</i> generally did not properly constrain the near-surface part of the spatially dense soft data variable. The computation time of 2&amp;ndash;3&amp;thinsp;h for <i>snesim</i> was in between <i>DS</i> and <i>iqsim</i>. The <i>snesim</i> implementation used here is part of the Stanford Geostatistical Modeling Software, or SGeMS. The <i>snesim</i> setup was not trivial, with numerous parameter settings, usage of multiple grids and a search tree database. However, once the parameters had been set it yielded comparable results to the other methods. Both, <i>iqsim</i> and <i>DS</i>, are easy to script and run in parallel on a server, which is not the case for the <i>snesim</i> implementation in SGeMS.
doi:10.5194/hess-2017-413 fatcat:bvmy27wlpfaybf7rhzbsgoxmra