Spatial data analysis of mineral deposit point patterns: Applications to exploration targeting |
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| Institution: | 1. CSRE, Indian Institute of Technology Bombay, Powai, 400076 Mumbai India;2. Centre for Exploration Targeting, University of Western Australia, Crawley 6009, WA, Australia;3. Department of Earth and Oceans, James Cook University, Townsville, Queensland, Australia;1. Kenex Limited, PO Box 41136, Eastbourne, Wellington 5047, New Zealand;2. Chatham Rock Phosphate Ltd., PO Box 231, Takaka 7142, New Zealand;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: | Systematic spatial analysis of mineral deposit point patterns can reveal significant spatial properties of mineral systems, with major implications for regional mineral prospectivity modelling. For valid results, a study area needs to be clearly defined, taking into account permissiveness of the geological units for a particular mineral system and effects of cover. Standard statistical tests assuming an isometric contiguous study area with regionally homogeneous distribution of deposits are likely to produce invalid results. Analysis of regional uniformity of spatial deposit density is required for adequate design and interpretation of tests for clustering. Spatial distribution of orogenic gold deposits in the Hodgkinson Province in Queensland and the Western Lachlan Orogen in Victoria (Australia) indicates the presence of significant regional linear metallogenic zones, probably controlled by deep crustal domain boundaries oblique and not related to any recognised major faults. Within the metallogenic zones in both regions, individual gold occurrences are strongly clustered into ore fields, but the distribution of ore fields is random. |
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