Fuzzy inference systems for prospectivity modeling of mineral systems and a case-study for prospectivity mapping of surficial Uranium in Yeelirrie Area,Western Australia |
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| Institution: | 1. Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, 400076 India;2. Spatial Cognitive Engineering Lab, Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC 3010, Australia;3. Minerals Down Under Flagship, CSIRO, Kensington, Perth, WA, Australia;4. Corporate Geoscience Group, 39 Morritt Close, Rockingham, WA, Australia;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands;2. Fomicruz S.E. Alberdi 643, 9400 Río Gallegos, Santa Cruz, Argentina;3. School of Earth and Environmental Sciences, James Cook University, Townsville, Queensland, Australia;1. Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China;2. State Key Lab of Geological Processes and Mineral Resources, China University of Geosciences, Beijing 100083, China;3. School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China;4. Department of Earth and Space Science and Engineering, Department of Geography, York University, 4700 Keele Street, Toronto, ON M3J1P3, Canada;5. Department of Earth and Oceans, James Cook University, Townsville 4811, Queensland, Australia;1. School of Earth and Oceans, James Cook University, Townsville 4811, Queensland, Australia;2. International Rice Research Institute, Los Baños 4030, Laguna, Philippines;1. X-plore Geoconsulting, 39 Morritt Close, Rockingham, WA 6168, Australia;2. Economic Geology Research Centre (EGRU), School of Earth & Environmental Science, James Cook University, Townsville, QLD 4811, Australia;3. ARC National Key Centre for Geochemical Evolution and Metallogeny of Continents (GEMOC), Department of Earth and Planetary Sciences, Macquarie University, North Ryde, NSW 2109, Australia;4. Kenex Ltd, PO Box 41136, Eastbourne, Wellington, New Zealand |
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| Abstract: | A Mamdani-type fuzzy inference system for prospectivity modeling of mineral systems is described. The system is a type of knowledge-driven symbolic artificial intelligence that is transparent, intuitive and is easy to construct by geologists because they are built in natural language and use linguistic values. No examples are used for training the system and expert-opinions are incorporated indirectly in terms of objective mathematical functions, which reduce the possibility of over-emphasizing the known deposits usually used as training data. The cognitive reasoning of the exploration geologist is captured in explicit if–then type of statements written in natural language using linguistic values. Conditional dependencies in the exploration data sets are managed through the use of fuzzy operators. A case study for surficial uranium prospectivity modeling in the Yeelirrie area, Western Australia, is used to demonstrate the approach. In the output prospectivity map, the SE-NW trending Yeelirrie and E-W trending Hinkler's Well palaeochannels show high prospectivity, while other channels show very low prospectivity ranges. The known surficial uranium deposits fall in high prospectivity areas, although minor showings and anomalies in the southern part of the study area fall in low prospectivity areas. A comparison of the prospectivity model with the radiometric image shows that several channels showing high surface uranium concentrations in the NW and NE quadrants may not be prospective. |
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