Assessment of the variability of spatial interpolation methods using elevation and drill hole data over the Magmont mine area, south-east Missouri
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Spatial interpolation methods are widely used in fields of geoscience such as mineral exploration. Interpolation methods translate the distribution of discrete data into continuous field over a given study area. Many methods exist and operate differently. Choosing judiciously the best interpolation method calls for an understanding of the algorithm, the intent or goal of the investigation and the knowledge of the study area. In the field of mineral exploration, accurate assessment is important because both overestimation and underestimation at spatially defined variables result in varied consequences. Assessment of methods' variability can be used as an additional criterion to help make an informed choice. Here, eight interpolation methods were tested on two spatial data sets consisting of topographic surface elevations and subsurface elevations of the top and the bottom of lead orebody at the Magmont mine area, in South-east Missouri. Variability between the interpolation methods was assessed based on statistical paired t-test of each method against a reference value, geometric analysis the map algebra tool in Arcmap 10.4.1 and comparison of their algorithms. Two of the methods returned values not significantly different from the reference value while the others were less robust. In testing model variability a second time on a reduced sample size, results suggest that interpolation methods are sensitive to sample size. Similarly, building the orebody top and bottom surfaces from information on the depths across the mineralized intersection showed dissemblance among methods. Key words: spatial interpolation, GIS, Magmont mine area, variability, math algebra, paired t-test.