Paper No. 5
Presentation Time: 10:00 AM


JØRGENSEN, Flemming1, SANDERSEN, Peter B.E.2, HØYER, Anne-Sophie2, PALLESEN, Tom Martlev3, FOGED, Nikolaj4, HE, Xiulan5 and SONNENBORG, Torben O.6, (1)Dept. of Groundwater and Quaternary Geology Mapping, Geological survey of Denmark and Greenland, GEUS, Lyseng Alle 1, Højbjerg, DK-8270, Denmark, (2)Dept. of Groundwater and Quaternary Geology Mapping, Geological Survey of Denmark and Greenland, GEUS, Lyseng Alle 1, Højbjerg, DK-8270, Denmark, (3)I•GIS, Voldbjergvej 14, Risskov, DK 8240, Denmark, (4)Deptartment of Geoscience, Aarhus University, Høegh-Guldbergs Gade 2, building 1120, Aarhus C, DK-8000, (5)Department of Geosciences and Natural Resource Management, Section of Geology, University of Copenhagen, Øster Voldgade 10, Copenhagen K, DK-1350, (6)Geological Survey of Denmark and Greenland (GEUS), Ø. Voldgade 10, Copenhagen K, 1350, Denmark,

A very complex architecture of the near-surface deposits in Denmark is a big challenge when 3D models are constructed. Borehole data are normally insufficient for proper 3D modeling due to low data density. Instead, airborne electromagnetic techniques are used to provide information on the spatial distribution and composition of the geology. Airborne surveys are normally combined with seismic data to map the stratigraphic framework in the model area.

To transform airborne electromagnetic data into geology is complicated with many pitfalls and requires geophysical as well as geological insight. Thorough geological background knowledge must be implemented in the interpretation while concurrently identifying and acknowledging the basic limitations of the method. Cognitive modeling is therefore normally preferred.

A large-scale airborne transient electromagnetic survey (conducted with the SkyTEM system) and 38 km high-resolution seismic data have together with new and existing borehole data and hydrocarbon exploration data been available for the model construction. The data are unevenly distributed across the area and the entire model area is not covered by the SkyTEM survey. Cross-cutting tunnel valleys, faults, erosional unconformities, delta units and glaciotectonic complexes are among the geological features identified in the area. The complexity varies as a result of shifting geological environments across the model area.

A broad geological overview and understanding of the area is gained by cognitive co-interpretation of the geophysical and geological data. To address the high level of detail in the SkyTEM data, the model is constructed as a voxel model with lithofacies attributes supplemented with surfaces. The model is mainly constructed manually by cognitive interpretation, but geostatistic inversion has been used to distribute lithology to voxels within the glaciotectonic complexes (where SkyTEM exists). Stochastic modeling (SGeMS) has been used for the shallow part of the subsurface in the area without SkyTEM data.