2014 GSA Annual Meeting in Vancouver, British Columbia (19–22 October 2014)

Paper No. 34-2
Presentation Time: 9:15 AM


MAUEL, Stephen W., Wisconsin Geological and Natural History Survey, University of Wisconsin-Extension, 3817 Mineral Point Road, Madison, WI 53705

Mapping bedrock elevation in landscapes devoid of outcrop and covered with glacial deposits poses a significant challenge, as does mapping bedrock elevation in unglaciated landscapes that exhibit mature drainage and extensive erosion. Within the borders of the state of Wisconsin are thousands of square miles of both glaciated and unglaciated land. Creating an accurate representation of bedrock elevation requires the synthesis of all available data that indicates bedrock depth or elevation.

In Wisconsin, well construction reports (WCRs) serve as surrogate geologic logs and can be used to assign bedrock elevation to points in a GIS. WCR data is then combined with all existing bedrock elevation data from soil mapping, Quaternary mapping, and previous bedrock elevation mapping. The resultant synthesis can provide a good base to construct best-fit topographic models of the bedrock elevation as demonstrated in Calumet, Chippewa, and Crawford counties in Wisconsin. Where it is suspected landforms are comprised of bedrock at their core, geophysical surveys are often useful to determine whether bedrock could be deeper or shallower than indicated by the models.

Utilizing all available bedrock elevation data, and following these techniques, bedrock elevation models can be generated at appropriate scales to serve as the base for bedrock and hydrogeologic investigations. Bedrock elevation models can also be used to produce three-dimensional representations of the thickness of unlithified material between bedrock and the land surface. Geologic, hydrogeologic, and “depth to bedrock” models can all aid in making more informed land use decisions. While development of bedrock elevation models using various available geologic data sets is not new, recent refinements in data management and analysis have permitted the incorporation of much more and varied data, creating better models.