基于gis的空间分析确定密苏里州格林县天坑形成的影响因素

Shishay T. Kidanu, N. Anderson, J. Rogers
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引用次数: 4

摘要

天坑是密苏里州格林县喀斯特地形的固有特征,给建筑/基础设施发展带来了危害和工程挑战。分析塌陷区空间分布与可能的影响因素之间的关系,有助于认识塌陷区形成的控制因素。本文概述的空间分析有助于评估潜在的天坑危害。本文采用基于地理信息系统的普通最小二乘回归(OLS)和地理加权回归(GWR)方法,确定并评价了影响岩溶陷落孔形成和分布的主要因素。从OLS结果来看,在12个可能的影响因素中,有7个因素对研究区天坑形成过程起着重要的控制作用。这些因素包括覆盖层厚度、地下水深度、地表坡度、到最近地表排水线的距离、到最近地质构造(如断层或褶皱)的距离、到最近道路的距离以及到最近泉水的距离。然后将这些因素作为GWR模型中的独立变量。GWR模型检验了各因素之间的空间非平稳性,表现出比OLS更好的性能。绘制每个变量的GWR模型系数估计值。这些地图提供了对整个研究区域的天坑密度的变量影响的空间见解。GWR空间分析是了解地陷影响因素的有效方法。研究结果可为天坑易感性模型或灾害制图提供客观的参数加权方法。
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Using Gis-based Spatial Analysis To Determine Factors Influencing the Formation of Sinkholes in Greene County, Missouri
Sinkholes are inherent features of the karst terrain of Greene County, Missouri, that present hazards and engineering challenges to construction/infrastructure development. Analysis of relationships between the spatial distribution of sinkholes and possible influencing factors can help in understanding the controls involved in the formation of sinkholes. The spatial analysis outlined herein can aid in the assessment of potential sinkhole hazards. In this research, Geographic Information System–based ordinary least squares regression (OLS) and geographically weighted regression (GWR) methods were used to determine and evaluate principal factors appearing to influence the formation and distribution of karst sinkholes. From the OLS result, seven out of 12 possible influencing factors were found to exert significant control on sinkhole formation processes in the study area. These factors are overburden thickness, depth to groundwater, slope of the ground surface, distance to the nearest surface drainage line, distance to the nearest geological structure (such as faults or folds), distance to the nearest road, and distance to the nearest spring. These factors were then used as independent variables in the GWR model. The GWR model examined the spatial non-stationarity among the various factors and demonstrated better performance over OLS. GWR model coefficient estimates for each variable were mapped. These maps provide spatial insights into the influence of the variables on sinkhole densities throughout the study area. GWR spatial analysis appears to be an effective approach to understand sinkhole-influencing factors. The results could be useful to provide an objective means of parameter weighting in models of sinkhole susceptibility or hazard mapping.
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