The Burlington-Keokuk Limestone bedrock underlies more than 70% of Greene County and 98 % of the identified sinkholes in the county (Missouri Geological Survey- GeoSTRAT, 2016) formed in this unit. Analysis of the sinkholes’ spatial distribution and patterns suggest that the sinkholes are not randomly distributed, but are spatially clustered. This implies that there is a process controlled by a finite set of factors that promote the formation and development of karst sinkholes.
In this study GIS-based multivariate regression methods (OLS and GWR) were applied to evaluate the spatial relationships between potential sinkhole influencing factors (explanatory variables) and sinkhole density (dependent variable), with the aim of evaluating the significant controlling factors. The OLS analysis revealed that seven of the
twelve possible influencing factors considered in the analysis likely play important roles in triggering the formation of sinkholes. These factors are overburden thickness, slope of ground surface, depth to groundwater, distance to the nearest drainage line, distance to the nearest road, distance to the nearest geological structure, and distance to the nearest spring.
The OLS results also indicated that the relationship between some or perhaps all of the explanatory variables and the dependent variable are non-stationary across the study area. Hence, GWR emerged as being more appropriate for analyzing those relationships because it has the capability of capturing the spatial non-stationarity of the influencing factors. GWR improved the model and explained 86% (better than OLS=57%) of the sinkhole density variability. The GWR model coefficient values for each explanatory variable provide visual insight into the influence of these variables on localized sinkhole density and patterns, and the values can be used to provide an objective means of parameter weighting in models of sinkhole susceptibility or hazard mapping/zoning.
Due to paucity of data, insufficient data form and model criteria, there are some potential influencing factors which were not included in the model (this may include falling or rising depth-to-groundwater, soil type, geochemical processes e.t.c). The OLS and GWR models were able to explain only 57% and 86% of the processes responsible for the formation of mapped sinkholes, respectively. Therefore further research incorporating more data with better resolution is recommended to improve the model.
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II. ERT-BASED INVESTIGATION OF A SINKHOLE IN GREENE