A Comparison of Modified Fuzzy Weights of Evidence, Fuzzy Weights of Evidence, and Logistic Regression for Mapping Miner
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A Comparison of Modified Fuzzy Weights of Evidence, Fuzzy Weights of Evidence, and Logistic Regression for Mapping Mineral Prospectivity Daojun Zhang · Frits Agterberg · Qiuming Cheng · Renguang Zuo
Received: 13 March 2013 / Accepted: 10 October 2013 © International Association for Mathematical Geosciences 2013
Abstract Weights of evidence and logistic regression are two of the most popular methods for mapping mineral prospectivity. The logistic regression model always produces unbiased estimates, whether or not the evidence variables are conditionally independent with respect to the target variable, while the weights of evidence model features an easy to explain and implement modeling process. It has been shown that there exists a model combining weights of evidence and logistic regression that has both of these advantages. In this study, three models consisting of modified fuzzy weights of evidence, fuzzy weights of evidence, and logistic regression are compared with each other for mapping mineral prospectivity. The modified fuzzy weights of the evidence model retains the advantages of both the fuzzy weights of the evidence model and the logistic regression model; the advantages being (1) the predicted number of deposits estimated by the modified fuzzy weights of evidence model is nearly equal to that of the logistic regression model, and (2) it can deal with missing data. This method is shown to be an effective tool for mapping iron prospectivity in Fujian Province, China. Keywords Conditional independence · Mineral resource assessment · Data integration · GIS modeling · Fujian Province
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D. Zhang ( ) · Q. Cheng · R. Zuo State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430043, China e-mail: [email protected] F. Agterberg Geological Survey of Canada, 601 Booth Street, Ottawa, ON K1A0E8, Canada
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Q. Cheng ( ) Department of Earth and Space Science and Engineering, York University, Toronto M3J1P3, Canada e-mail: [email protected]
Math Geosci
1 Introduction The weights of evidence method (WofE) is one of the most popular models using the Bayesian theory of conditional probability to quantify spatial associations between evidence layers (or geological factors) and known mineral occurrences (Agterberg 1989a; Bonham-Carter 1994). It combines the prior probability of mineral occurrence with the conditional probability of mineral occurrence for each evidential layer using Bayes’ rule to derive posterior probabilities of mineral occurrence. WofE modeling of mineral potential involves a three-stage process: (1) estimation of prior probability (Pprior ) of prospect occurrence, (2) estimation of weights to be assigned to presence and absence of spatial evidence with respect to the prospects, and (3) updating of Pprior by using these weights to estimate the posterior probabilities (Pposterior ). More information about WofE can be found in Bonham-Carter et al. (1989), Agterberg (1989a), and Agterberg et al. (1990). Since WofE was first introduced into mineral pote
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