Page 244 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

246 Chapter 7

of mineral deposits (e.g., Cox and Singer, 1986; Roberts et al., 1988; Berger and Drew,
2002) and their tectonic environments (e.g., Mitchell and Garson, 1981; Sawkins, 1989;
Pirajno, 1992; Robb, 2004) is therefore essential in the wildcat methodology of modeling
mineral prospectivity as well as in the other techniques for knowledge-driven modeling
of mineral prospectivity.

CONCLUSIONS
Several techniques for knowledge-driven modeling of mineral prospectivity exist. All
the techniques demonstrated here are aided by a GIS in terms of creating and integrating
evidential maps and evaluating the performance of mineral prospectivity maps. It is
better to use multi-class evidential maps than binary evidential maps in modeling of
mineral prospectivity. Representation and incorporation of evidential uncertainty, as in
evidential belief modeling, result in better predictive models of mineral prospectivity, at
least in the case study area. The performance of knowledge-driven mineral prospectivity
maps depends chiefly on the subjective nature of expert judgments that are applied in
creating evidential maps and/or in integrating evidential maps. Therefore, depending on
the technique applied, deriving an optimum knowledge-driven predictive model of
mineral prospectivity entails trial-and-error or comparative analysis by (a) adjustment of
evidential scores of classes in evidential maps, (b) adjustment of evidential map weights
and/or (c) adjustment of inference networks for combining evidential maps.
Despite the subjectivity of ‘expert’ knowledge applied in knowledge-driven
modeling of mineral prospectivity, the techniques demonstrated here are useful in first-
pass assessments of mineral prospectivity of greenfields geologically permissive areas
where no or very few mineral deposits of interest are known to occur. This is further
demonstrated by the results of the application of the knowledge-guided data-driven
wildcat technique of mineral prospectivity, which is based on generic knowledge of
geological environments of mineral deposits rather than on knowledge of empirical
spatial associations between mineral deposits of the type sought and indicative
geological features. However, because of the subjective nature of ‘expert’ knowledge
applied in knowledge-driven or knowledge-guided data-driven modeling of mineral
prospectivity, it is obligatory to evaluate performance of derived mineral prospectivity
maps. It is possible to do so in cases where a few occurrences of mineral deposits of
interest are known, but it is difficult, if not impossible, to do so in cases where there are
no known occurrences of mineral deposits of interest. In any case, the quality and
quantity of exploration data that are available in greenfields geologically permissive
areas also influence the quality of a knowledge-driven model of mineral prospectivity.
Presumably, more and better data allow better expert judgments in creating and
integrating evidential maps of mineral prospectivity.
Knowledge-driven modeling of mineral prospectivity is also applicable in relatively
well-explored areas where the objective is to find new exploration targets for mineral
deposits of the type sought in the presence of several discovered mineral deposits of the