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Data-Driven Modeling of Mineral Prospectivity 251

TABLE 8-I

Bivariate mathematical models/methods used for data-driven mapping of mineral prospectivity.

Model/method References to examples
Weights-of-evidence modeling Bonham-Carter et al. (1988, 1989), Agterberg et al. (1990, 1993a),
Bonham-Carter and Agterberg (1990), Agterberg (1992), Bonham-
Carter (1991, 1994), Cheng and Agterberg (1999), Mihalasky
(1999), Raines (1999), Singer and Kouda (1999), Pan and Harris
(2000), Mihalasky and Bonham-Carter (2001), Harris et al.
(2001b), Agterberg and Cheng (2002), Harris et al. (2003),
Carranza (2004b), Porwal et al. (2001, 2006a), Coolbaugh and
Bedell (2006), Harris and Sanborn-Barrie (2006), Porwal (2006)
Evidential belief modeling Chung and Fabbri (1993), An et al. (1994b), Carranza (2002),
Carranza and Hale (2003), Carranza et al. (2005, 2008a, 2008b;
2008c), this volume

with C ji classes are being quantified. It seems that logistic regression and artificial neural
networks are the most commonly used multivariate techniques for data-driven predictive
modeling of mineral prospectivity. The multivariate techniques outnumber the bivariate
techniques for creating and then integrating predictor maps in mineral prospectivity
modeling. This indicates that, because of the highly complex nature of spatial
associations between mineral deposits and geological features, it is in most cases more
desirable to develop and/or apply multivariate rather than bivariate techniques for data-
driven modeling of mineral prospectivity. In some cases it is even more desirable to
develop and/or apply hybrid methods for data-driven modeling of mineral prospectivity,
like fuzzy weights-of-evidence modeling (Cheng and Agterberg, 1999; Porwal, 2006;
Porwal et al., 2006a), data-driven fuzzy modeling (Luo and Dimitrakopoulos, 2003;
Porwal et al., 2003b) and neuro-fuzzy modeling (Porwal et al., 2004; Porwal, 2006).
The different methods of GIS-based data-driven modeling of mineral prospectivity
are well-documented in the literature and are now mostly well-established. In Tables 8-I
and 8-II, the researches described in the references cited for each method range from
seminal studies in developing a method, to innovative or adaptive studies providing
improvements of a method, to instructive studies in various cases demonstrating or
addressing further certain aspects that are vital in the application of a method. Moreover,
some of the references cited in Tables 8-I and 8-II compare and contrast some of the
techniques for data-driven modeling of mineral prospectivity. Therefore, this chapter
does not attempt to explain and demonstrate each of the different methods of GIS-based
data-driven techniques for modeling mineral prospectivity. However, one bivariate
technique (evidential belief modeling) and one multivariate technique (discriminant
analysis) are explained and demonstrated here in a case study of mapping epithermal Au
prospectivity in the Aroroy district (Philippines).