Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity
In GIS-based data-driven modeling of mineral prospectivity, a suitably fine unit cell size is used for spatial representation of known occurrences of mineral deposits of the type sought (D) in a study area (T). However, until now, the unit cell size is chosen subjectively. In this paper, a methodology is proposed for objective selection of the most suitable unit cell size for data-driven modeling of mineral prospectivity using a raster-based GIS. A set of choices of suitable unit cell sizes is first derived via point pattern analysis of a set of known occurrences of mineral deposits of the type sought. Then, (a) the lower limit of a set of choices of suitable unit cell sizes is considered and defined according to the map scales from which spatial data for mineral prospectivity mapping were derived, and (b) the upper limit of the same set of choices of suitable unit cell sizes is considered (and revised as necessary) based on knowledge of spatial extents of mineral deposits of the type sought or via analysis of reflexive nearest neighbour points. Finally, it is shown that fractal analysis of spatial contrast between unit cells containing D and unit cells not containing D in T provides for objective selection of the most suitable unit cell size. In a case study application of the weight-of-evidence method to mineral prospectivity mapping, using the most suitable unit cell size, found via the proposed methodology, results in spatial evidence weights and weight uncertainties that are nearly identical to those derived by using the finest (i.e., lower limit) unit cell size. In contrast to using the most suitable unit cell size, using coarser unit cell sizes result in higher positive weights, lower negative weights and higher weight uncertainties of spatial evidence of mineral prospectivity. The proposed methodology for objective selection of the most suitable unit cell size in data-driven modeling of mineral prospectivity using a raster-based GIS is robust and can easily be implemented.
gis point pattern analysis fractal analysis spatial contrast weights-of-evidence