Abstract
Abstract. We present an automatic method for parameterization of a 3-D model of the subsurface, integrating lithological information from boreholes with resistivity models through an inverse optimization, with the objective of further detailing of geological models, or as direct input into groundwater models. The parameter of interest is the clay fraction, expressed as the relative length of clay units in a depth interval. The clay fraction is obtained from lithological logs and the clay fraction from the resistivity is obtained by establishing a simple petrophysical relationship, a translator function, between resistivity and the clay fraction. Through inversion we use the lithological data and the resistivity data to determine the optimum spatially distributed translator function. Applying the translator function we get a 3-D clay fraction model, which holds information from the resistivity data set and the borehole data set in one variable. Finally, we use k-means clustering to generate a 3-D model of the subsurface structures. We apply the procedure to the Norsminde survey in Denmark, integrating approximately 700 boreholes and more than 100 000 resistivity models from an airborne survey in the parameterization of the 3-D model covering 156 km2. The final five-cluster 3-D model differentiates between clay materials and different high-resistivity materials from information held in the resistivity model and borehole observations, respectively.
Highlights
In a large-scale geological and hydrogeological modeling context, borehole data seldom provide an adequate database due to low spatial density in relation to the complexity of the subsurface to be mapped
Last we demonstrate the method in a field example with resistivity data from an airborne SkyTEM survey combined with quality-rated borehole information
The clay fraction (CF) procedure is a further development to three dimensions of the accumulated clay thickness procedure by Christiansen et al (2014), which is formulated in 2-D
Summary
In a large-scale geological and hydrogeological modeling context, borehole data seldom provide an adequate database due to low spatial density in relation to the complexity of the subsurface to be mapped. One way of building 3-D models is through a knowledge-driven (cognitive), manual approach (Jørgensen et al, 2013a). This can be carried out by making layer-cake models composed of stacked layers or by making models composed of structured or unstructured 3-D meshes where each voxel is assigned a geological/hydrogeological property. The latter allows for a higher degree of model complexity to be incorporated (Turner, 2006; Jørgensen et al, 2013a).
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