Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping

In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on...

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Vydáno v:Natural resources research (New York, N.Y.) Ročník 12; číslo 1; s. 1 - 25
Hlavní autoři: Porwal, Alok, Carranza, E. J. M., Hale, M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Heidelberg Springer 01.03.2003
Springer Nature B.V
Témata:
Areal geology. Maps
Base metal
Earth sciences
Earth, ocean, space
Exact sciences and technology
Geologic maps, cartography
Linear functions
Mapping
Metal geology
Metallic and non-metallic deposits
Mineral deposits
Rajasthan India
India
models
fuzzy sets
exploration
maps
Asia
interpretation
fuzzy logic
metallogenic provinces
metals
base metals
potential deposits
ISSN:1520-7439, 1573-8981
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Shrnutí:In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the “model” and “validation” base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.
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ISSN:1520-7439
1573-8981
DOI:10.1023/A:1022693220894