Combining 3D geological modelling techniques to address variations in geology, data type and density – An example from Southern Denmark

Computers & Geosciences, Elsevier, ISSN 0098-3004

Volume 81, 2015

DOI:10.1016/j.cageo.2015.04.010, Dimensions: pub.1000401906,


* Corresponding author



  1. (1) Geological Survey of Denmark and Greenland – GEUS, Lyseng Alle 1, DK-8270 Højbjerg, Denmark
  2. (2) Cowi (Denmark), grid.423764.0
  3. (3) Aarhus University, grid.7048.b, AU






The very complex near-surface geology in Denmark is a big challenge when constructing 3D geological models. Borehole data alone are normally insufficient for proper 3D modelling because data are too widespread. Therefore, Airborne ElectroMagnetic (AEM) techniques are often used to obtain supplementary information on the spatial distribution and composition of the geology.A large-scale AEM survey and high-resolution seismic data along with both new and existing borehole data and seismic data from hydrocarbon exploration were available for the construction of a detailed 3D geological model in our study area. The data are unevenly distributed, and only part of the study area was covered by the AEM survey. Cross-cutting tunnel valleys, erosional unconformities, delta units and a large glaciotectonic complex are among the geological features identified in the area. The geological complexity varies significantly across the model area.A broad geological overview and understanding of the area was obtained by joint cognitive interpretation of the geophysical and the geological data. To address the geological complexity and the very high level of detail gained from the AEM data, the model was constructed as a voxel model with lithofacies attributes supplemented by a number of bounding surfaces. In areas where the geology is not too detailed and complex, the model was constructed manually, whereas automated methods were used to populate voxels in areas with a high complexity. The automated methods comprised clay fraction modelling, which was used where AEM data are available, and stochastic modelling, which was used outside the area covered by AEM data.Our study shows that it is advantageous to combine several modelling methods in areas with varying geological complexity and data density. The choice of modelling methods should depend on the character and coverage of available data and on variations in geology throughout the model area.


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