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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Veröffentlicht in:	Natural resources research (New York, N.Y.) Jg. 12; H. 1; S. 1 - 25
Hauptverfasser:	Porwal, Alok, Carranza, E. J. M., Hale, M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Heidelberg Springer 01.03.2003 Springer Nature B.V
Schlagworte:	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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Abstract	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.
AbstractList	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.
Author	Carranza, E. J. M. Porwal, Alok Hale, M.
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Snippet	In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership...
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SubjectTerms	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
Title	Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping
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