Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectivity analysis of Mississippi Valley‐type mineralisation |
| |
| Authors: | C D'Ercole D I Groves C M Knox-Robinson |
| |
| Institution: | 1. Dante Geoscientific Consulting , 207 Clontarf Road, Hamilton Hill, WA, 6163, Australia;2. Centre for Global Metallogeny, Department of Geology and Geophysics , University of Western Australia , WA, 6907, Australia;3. Centre for Global Metallogeny, Department of Geology and Geophysics , University of Western Australia , WA, 6907, Australia;4. Spatial Analysis Services , PO Box 5001, South Lake, WA, 6964, Australia |
| |
| Abstract: | Geographic Information Systems (GIS) provide an efficient vehicle for the generation of mineral prospectivity maps, which are products of the integration of large geological, geophysical and geochemical datasets that typify modern global‐scale mineral exploration. Conventionally, two contrasting approaches have been adopted, an empirical approach where there are numerous deposits of the type being sought in the analysed mature terrain, or a conceptual approach where there are insufficient known deposits for a statistically valid analysis. There are also a variety of potential methodologies for treatment of the data and their integration into a final prospectivity map. The Lennard Shelf represents the major Mississippi Valley‐type (MVT) province in Australia; however, there are only 13 deposits or major prospects known, making an empirical approach to prospectivity mapping impractical. Instead, a conceptual approach was adopted, where critical features that control the location of MVT deposits on the Lennard Shelf, as defined by widely accepted genetic models, were translated into features related to fluid pathways, depositional traps and fluid outflow zones, which can be mapped in a GIS and categorised as either regional or restricted diagnostic, or permissive criteria. All criteria were derived either directly from geological and structural data, or indirectly from geophysical and geochemical datasets. A fuzzy‐logic approach was adopted for the prospectivity analysis, where each interpreted critical feature of the conceptual model was assigned a weighting between 0 and 1 based on its inferred relative importance and reliability. The fuzzy‐logic method is able to cope with incomplete data, a common problem in regional‐scale exploration datasets. The data were best combined using the gamma operator to produce a fuzzy‐logic map for the prospectivity of MVT deposits on the southeastern Lennard Shelf. Five categories of prospectivity were defined. Importantly, from an exploration viewpoint, the two lowest prospectivity categories occupy ～90% and the highest two categories only 1.6% of the analysed area, yet eight of the 13 known MVT deposits lie in the latter and none in the former: i.e. all lie within ～10% of the area, despite the fact that deposit locations were not used directly in the analysis. The propectivity map also defines potentially mineralised areas in the central southeastern Lennard Shelf and the southern part of the Oscar Ranges, where there are currently no known deposits. Overall, the analysis demonstrates the power of fuzzy‐logic prospectivity mapping on a semi‐regional to regional scale, and emphasises the value of geological data, particularly accurate geological maps, in exploration for hydrothermal mineral deposits that formed late in the evolution of the terrain under exploration. |
| |
| Keywords: | conceptual models exploration criteria fuzzy logic genetic models GIS Mississippi Valley‐type deposits |
|
|
