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An understanding of what influences sinkhole formation and the ability to accurately predict sinkhole hazards is critical
to environmental management efforts in the karst lands of southeastern Minnesota. Based on the distribution of distances to
the nearest sinkhole, sinkhole density, bedrock geology and depth to bedrock in southeastern Minnesota and northwestern Iowa,
a decision tree model has been developed to construct maps of sinkhole probability in Minnesota. The decision tree model was
converted as cartographic models and implemented in ArcGIS to create a preliminary sinkhole probability map in Goodhue, Wabasha,
Olmsted, Fillmore, and Mower Counties. This model quantifies bedrock geology, depth to bedrock, sinkhole density, and neighborhood
effects in southeastern Minnesota but excludes potential controlling factors such as structural control, topographic settings,
human activities and land-use. The sinkhole probability map needs to be verified and updated as more sinkholes are mapped
and more information about sinkhole formation is obtained. 相似文献
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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. 相似文献
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广州市在以经济高度集中、资源快速利用为标志的城市化过程中,对地下水环境的影响是巨大的,其地下水NH4+和NO2-高浓度区主要集中分布于城市中心区,NO3-超标点除相对集中分布在城市中心区外,在城市外围地带和丘陵区都有分布。造成广州市不同地区的地下水环境三氮污染的原因各不相同,主要为生活和工农业生产等人类活动影响,与河网发育平原区地层沉积特点、地下水形成和赋存条件等自然因素也相关,人类活动对地下水环境的污染途径复杂多样。广州市地下水环境中三氮形态分布受水文地质条件和水文地球化学作用影响明显,主要影响因素有氧化还原环境、包气带岩性、补径排条件、水土环境中的铁锰含量和酸化程度等。 相似文献
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