Porwal, Alok, and Carranza, E.J.M. (2008) Classifiers for modeling of mineral potential. In: Pourret, Olivier, Naim, Patrick, and Marcot, Bruce, (eds.) Bayesian Networks: a practical guide to applications. Wiley-Blackwell, Chichester, UK, pp. 149-171.

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Abstract

[Extract] Classification and allocation of land-use is a major policy objective in most countries. Such an undertaking, however, in the face of competing demands from different stakeholders, requires reliable information on resources potential. This type of information enables policy decision-makers to estimate socio-economic benefits from different possible land-use types and then to allocate most suitable land-use. The potential for several types of resources occurring on the earth's surface (e.g., forest, soil, etc.) is generally easier to determine than those occurring in the subsurface (e.g., mineral deposits, etc.). In many situations, therefore, information on potential for subsurface occurring resources is not among the inputs to land-use decision-making [85]. Consequently, many potentially mineralized lands are alienated usually to, say, further exploration and exploitation of mineral deposits.

Areas with mineral potential are characterized by geological features associated genetically and spatially with the type of mineral deposits sought. The term 'mineral deposits' means .accumulations or concentrations of one or more useful naturally occurring substances, which are otherwise usually distributed sparsely in the earth's crust. The term 'mineralization' refers to collective geological processes that result in formation of mineral deposits. The term 'mineral potential' describes the probability or favorability for occurrence of mineral deposits or mineralization. The geological features characteristic of mineralized land, which are called recognition criteria, are spatial objects indicative of or produced by individual geological processes that acted together to form mineral deposits. Recognition criteria are sometimes directly observable; more often, their presence is inferred from one or more geographically referenced (or spatial) datasets, which are processed and analyzed appropriately to enhance, extract, and represent the recognition criteria as spatial evidence or predictor maps. Mineral potential mapping then involves integration of predictor maps in order to classify areas of unique combinations of spatial predictor patterns, called unique conditions [51] as either barren or mineralized with respect to the mineral deposit-type sought.

Item ID:	40324
Item Type:	Book Chapter (Research - B1)
ISBN:	978-0-470-06030-8
Date Deposited:	10 Dec 2015 04:58
FoR Codes:	04 EARTH SCIENCES > 0499 Other Earth Sciences > 049999 Earth Sciences not elsewhere classified @ 50% 08 INFORMATION AND COMPUTING SCIENCES > 0802 Computation Theory and Mathematics > 080205 Numerical Computation @ 25% 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 25%
SEO Codes:	84 MINERAL RESOURCES (excl. Energy Resources) > 8401 Mineral Exploration > 840199 Mineral Exploration not elsewhere classified @ 100%
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