Karst database implementation in Minnesota: analysis of sinkhole distribution |
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| Authors: | Y Gao EC Alexander Jr RJ Barnes |
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| Institution: | (1) Department of Physics, Astronomy, and Geology, East Tennessee State University, Johnson City, TN 37604, USA;(2) Department of Geology & Geophysics, University of Minnesota, Minneapolis, MN 55455, USA;(3) Department of Civil Engineering, University of Minnesota, Minneapolis, MN 55455, USA |
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| Abstract: | This paper presents the overall sinkhole distributions and conducts hypothesis tests of sinkhole distributions and sinkhole formation using data stored in the Karst Feature Database (KFD) of Minnesota. Nearest neighbor analysis (NNA) was extended to include different orders of NNA, 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 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. |
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| Keywords: | Karst Feature database (KFD) Nearest neighbor analysis (NNA) Nearest neighbor index (NNI) Complete spatial randomness (csr) Distance to nearest neighbor (DNN) Minnesota |
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