Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 5
Articles | Volume 30, issue 5
https://doi.org/10.5194/hess-30-1421-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-1421-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 5
Research article
 | 
17 Mar 2026
Research article |  | 17 Mar 2026

Enhanced Markov-type Categorical Prediction with geophysical soft constraints for hydrostratigraphic modeling

Liming Guo, Thomas Hermans, Nicolas Benoit, David Dudal, Ellen Van De Vijver, Rasmus Madsen, Jesper Nørgaard, and Wouter Deleersnyder

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Cited articles

Allard, D., D'Or, D., and Froidevaux, R.: An efficient maximum entropy approach for categorical variable prediction, Eur. J. Soil Sci., 62, 381–393, 2011. a, b, c
Andersen, L. T.: 3D hydrogeological layer model of the Egebjerg area, GEUS Dataverse, V1 [data set], https://doi.org/10.22008/FK2/FHP1XK, 2024. a
Auken, E., Christiansen, A. V., Jacobsen, B. H., Foged, N., and Sørensen, K. I.: Piecewise 1D laterally constrained inversion of resistivity data, Geophys. Prospect., 53, 497–506, https://doi.org/10.1111/j.1365-2478.2005.00486.x, 2005. a
Barfod, A. A. S., Møller, I., Christiansen, A. V., Høyer, A.-S., Hoffimann, J., Straubhaar, J., and Caers, J.: Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods, Hydrol. Earth Syst. Sci., 22, 3351–3373, https://doi.org/10.5194/hess-22-3351-2018, 2018. a, b, c, d, e, f, g
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Understanding what lies beneath the ground is often difficult due to limited drilling data. Our research combines geostatistical method with large-scale geophysical surveys to create more realistic underground maps. We developed a method that merges both data sources to better predict underground layers, helping improve decisions in groundwater management and future geological studies.
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