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Probabilistic Fuzzy Logic Modeling: Quantifying Uncertainty of Mineral Prospectivity Models Using Monte Carlo Simulations

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Title Probabilistic Fuzzy Logic Modeling: Quantifying Uncertainty of Mineral Prospectivity Models Using Monte Carlo Simulations
 
Creator LISITSIN, VA
PORWAL, A
MCCUAIG, TC
 
Subject Uncertainty
Prospectivity analysis
GIS
Monte Carlo
AUSTRALIA
SYSTEMS
SETS
 
Description Significant uncertainties are associated with the definition of both the exploration targeting criteria and computational algorithms used to generate mineral prospectivity maps. In prospectivity modeling, the input and computational uncertainties are generally made implicit, by making a series of best-guess or best-fit decisions, on the basis of incomplete and imprecise information. The individual uncertainties are then compounded and propagated into the final prospectivity map as an implicit combined uncertainty which is impossible to directly analyze and use for decision making. This paper proposes a new approach to explicitly define uncertainties of individual targeting criteria and propagate them through a computational algorithm to evaluate the combined uncertainty of a prospectivity map. Applied to fuzzy logic prospectivity models, this approach involves replacing point estimates of fuzzy membership values by statistical distributions deemed representative of likely variability of the corresponding fuzzy membership values. Uncertainty is then propagated through a fuzzy logic inference system by applying Monte Carlo simulations. A final prospectivity map is represented by a grid of statistical distributions of fuzzy prospectivity. Such modeling of uncertainty in prospectivity analyses allows better definition of exploration target quality, as understanding of uncertainty is consistently captured, propagated and visualized in a transparent manner. The explicit uncertainty information of prospectivity maps can support further risk analysis and decision making. The proposed probabilistic fuzzy logic approach can be used in any area of geosciences to model uncertainty of complex fuzzy systems.
 
Publisher SPRINGER HEIDELBERG
 
Date 2014-12-28T17:26:50Z
2014-12-28T17:26:50Z
2014
 
Type Article
 
Identifier MATHEMATICAL GEOSCIENCES, 46(6)747-769
1874-8961
1874-8953
http://dx.doi.org/10.1007/s11004-014-9534-1
http://dspace.library.iitb.ac.in/jspui/handle/100/16986
 
Language English