2004 Denver Annual Meeting (November 7–10, 2004)

Paper No. 1
Presentation Time: 8:00 AM


GAO, Yongli, Department of Physics, Astronomy, and Geology, East Tennessee State University, Johnson City, TN 37614 and ALEXANDER Jr, E. Calvin, Department of Geology and Geophysics, Univ. of Minnesota, 310 Pillsbury Dr., SE, Minneapolis, MN 55455-0219, yogao@indiana.edu

A relational GIS-based database has been developed for currently mapped karst features in Minnesota. When systematically mapped the distribution of sinkholes proved to cover a much larger area than had been initially assumed. Hypothesis tests of sinkhole distributions and sinkhole formation were conducted using data stored in the Karst Feature Database of Minnesota. Nearest neighbor analysis was extended to include different orders of nearest neighbor analysis, different scales of concentrated zones of sinkholes, and directions to the nearest sinkholes. The statistical results, along with the sinkhole density distribution, indicate that sinkholes tend to form in highly concentrated zones instead of as scattered individuals. The pattern changes from clustered to random to regular as the scale of the analysis decreases from 10 - 100 km2 to 5 - 30 km2 to 2 - 10 km2. Hypotheses that may explain this phenomenon are: 1) areas in the highly concentrated zones of sinkholes have similar geologic and topographical settings that favor sinkhole formation; 2) existing sinkholes change the hydraulic gradient in the surrounding area and increase the solution and erosional processes that eventually form more new sinkholes.

Based on the distribution of distances to the nearest sinkhole and the sinkhole density in Fillmore County, a mathematical decision tree model has been developed to construct maps of sinkhole hazard. The decision tree model was implemented to construct a revised map of sinkhole hazard in Fillmore County using ArcView and ArcInfo GIS. To facilitate the comparison with the original maps the six probability zones were revised as six relative hazard zones. The decision tree model reproduces most of the important features seen on the original maps in the high density areas and has led to new insights about the internal structure of high density areas. The model is less successful in capturing the details of the lower density areas where the subjective criteria are more significant. Currently, there is no simple way of extrapolating across areas in which the sinkholes have not been mapped although karst phenomena extend well beyond the regions of high sinkhole density.