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> Home > Water and soil > Mapping > 3D geological modelling

3D geological modelling​

GEUS conduct research and development within 3D geological modelling and has established detailed 3D geological models for several areas throughout Denmark. The models integrate borehole data, geophysical (especially airborne and seismic data) and geochemical data with geological mapping and other geological information and knowledge. The models are typically targeted groundwater flow modelling, but since they are basically build as multi purpose models, they can be applied in other relations.

3D geological modelling at GEUS is performed in several ways and by use of different methods. The methods are called layer-based modelling, voxel-modelling, geostatistical og automated modelling, buried valley modelling combined modelling methods.

Layer-based modelling:
Layer-based models are typically created by a number of 2D cross sections across the model area. Stratigraphical interpretations are digitised on the sections by stratigraphical or lithological correlation between the boreholes and geophysics. Surfaces are interpolated from digitised points. This approach is based on iterative, manual and cognitive interpretation. The geologist makes use of background knowledge about geology and geological processes in the modelling process. The result is a subjective model that facilitates the use of all available information.​

 
Voxels are like bricks. A 3D geological model is created, when each voxel is given geological characteristica.
Voxel-modelling:
The geology is often difficult to describe as “layered” and is too complex to be represented in a layer model. We therefore make use of voxel modelling, which do not have the limitations of the layer-cake model. In a voxel model each voxel is assigned a lithology and/or another type of geological property. With this approach arbitrary shapes can be created and statistical methods can be applied, because 3D interpolation methods can be used to calculate properties for voxels by using information from, for example, boreholes or airborne EM geophysics. The modelling is performed iteratively by using dedicated voxel modelling tools developed for this exact purpose. Our approach allow the geologist to interactively incorporate his/her geological expert knowledge directly into the voxel model.

 
Geostatistical and automated modelling:
We also apply geostatistical modelling, especially Multiple Point Simulation (MPS) and Clay Fraction (CF) modelling. This is mainly based on Airborne EM data and borehole data. The stochastic modelling methods provide a set of equally plausible models depending on the given assumptions and conditions. With CF modelling, the clay fraction is calculated through inversion of borehole data and AEM resistivity models. Common for the automated methods is that they are normally restricted to providing only a few model properties; typically sand and clay. But they are effective in use and have other advantages.​
 

 
Buried-valley modelling:
The figure shows a 3D geological model with several buried valleys. Some of the valleys are crossing each other.
Buried tunnel valleys are common features in formerly glaciated areas. Where present, they are very important for the groundwater management. Mapping and modelling of the structures are therefore very important. Borehole data are normally too sparse for the mapping and modelling, whereas airborne electromagnetic data combined with seismic data has proven to be successful. We work with different techniques and strategies for 3D modelling of complex networks of buried valleys. These include combined usage of layer modelling and voxel modelling. Surfaces describe the erosional boundaries of the valley incisions, and voxels are used to represent the infill lithology and stratigraphy.

 
Combining modelling methods:
Model areas are often composed of varying geological complexity and differences in data type an density. To optimise the model results, we combine several modelling methods according to these factors. The modelling methods are chosen on the basis of the character and coverage of available data and on variations in geology. The output from the different techniques used in different sub-volumes of the model are combined into one final 3D model.

 
Contactpersons:
Reports - mostly in danish:
  • Kristensen, M., Vangkilde-Pedersen, T., Rasmussen, E. S., Dybkjær, K. & Andersen, L. T. 2014: Miocæn 3D opdateret 2014, GEUS rapport 1014/75. 160 pp​. ​​​​Download report without appendix 9 MBDownload appendix 1 Paneler 1,3 MBDownload appendix 2 Udbredelseskort 6 MB​​
  • Jørgensen, F., Sandersen, P., Høyer, A.-S., Møller, R.R., Pallesen, T.M., He, X., Kristensen. M. & Sonnenborg, T. 2014: 3D geologisk model ved Tønder. Danmarks og Grønlands Geologiske Undersøgelse Rapport 2014/39. 126 pp.
  • Lauritsen L.U. & Jørgensen, F. 2014: Geologiske modeller. Vand & Jord, 2. 53-57.
  • Jørgensen, F., Møller, R.R., Høyer, A.H. & Christiansen, A.V. 2012: Geologisk model ved Ølgod og Skovlund – eksempel på effektiviseret modellering i et heterogent geologisk miljø. Danmarks og Grønlands Geologiske Undersøgelse Rapport 2012/82. 83 pp.
  • Møller, R.R. & Jørgensen, F. 2011: Geologisk model ved Egebjerg. Danmarks og Grønlands Geologiske Undersøgelse Rapport 2011/37. 95 pp.
  • Jørgensen, F., Kristensen, M., Højberg A.L., Klint, K.E.S, Hansen, C., Jordt, B.E. Richardt, N og Sandersen, P. 2008. Opstilling af geologiske modeller til grundvandsmodellering. Geo-Vejledning 3. De Nationale Geologiske Undersøgelser for Danmark og Grønland - GEUS. 176 pp. Download 5 MB​
  • Sandersen, P. 2013: Geologisk model for Svendborg by: Opdatering og viderebearbejdelse af eksisterende model. Rapport udarbejdet af GEUS for Region Syddanmark. 39 s., maj 2013.
  • Sandersen, P. 2014: Skitser til en rumlig geologisk model for Vejen området. Rapport udarbejdet af GEUS for Region Syddanmark. 86 s., marts 2014 (Foreløbig udgave).
  • Odense - Er under udarbejdelse!

 
Scientific articles:

 

 

  • Jørgensen, F., Høyer, A.-S., Sandersen, P.B.E, He, X., and Foged, N. accepteret: Combining 3D geological modelling techniques to adress variations in geology, data type and density - an example from southern Denmark. Computers and Geosciences. In press.
  • Høyer, A.S., Jørgensen, F., Sandersen, P.B.E. and Møller, I. 3D geological modelling of a complex buried-valley network recognized on borehole and airborne electromagnetic data. Submitted to Journal of Applied Geophysics.
  • Høyer, A.-S., Jørgensen, F., Foged, N., He, X. and Christiansen, A.V. 2015: Three-dimensional geological modelling of AEM resistivity data - a comparison of three methods. Journal of Applied Geophysics, 115, 65-78. DOI:10.1016/j.jappgeo.2015.02.005.
  • Foged, N., Marker, P.A., Christansen, A.V., Bauer-Gottwein, P., Jørgensen, F., and Høyer, A. 2014: Large scale 3D-modeling by integration of resistivity models and borehole data through inversion. Hydrology and Earth System Sciences, 18, 4349–4362, 2014. DOI: 10.5194/hess-18-4349-2014.
  • Høyer, A., Jørgensen, F., Lykke-Andersen, H. and Christiansen, A.V. 2014: Iterative modelling of AEM data based on geological a priori information from seismic and borehole data. Near Surface Geophysics, 2014, 12, 635-650. DOI:10.3997/1873-0604.2014024.
  • He, X., Koch, J., Sonnenborg, T.O., Jørgensen, F., Schamper, C. and Refsgaard, J.C 2014: Transition probability-based stochastic geological modeling using airborne geophysical data and borehole data. Water Resources Research. Vol. 50, 3. DOI:10.1002/2013WR014593.
  • Jørgensen, F., Møller, R.R., Nebel, L., Jensen, N.-P., Christiansen A.V. and Sandersen, P.B.E. 2013: A method for cognitive 3D geological voxel modelling of AEM data. Bulletin of Engineering Geology and the Environment. Vol. 72, 3, 421-432. DOI: 10.1007/s10064-013-0487-2.
  • Jørgensen, F., Møller, R.R, Sandersen, P.B.E. and Nebel, L. 2010: 3-D geological modelling of the Egebjerg area based on hydrogeophysical data. Geological Survey of Denmark and Greenland Bulletin. Review of Survey Activities 2009. pp. 27-30


 

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3D geological modelling