Page 243 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Knowledge-Driven Modeling of Mineral Prospectivity 245

Fig. 7-25. (A) An integrated geochemical-geological wildcat model of hydrothermal deposit
prospectivity, Aroroy district (Philippines), derived as product of fuzzified evidential scores of
multi-element geochemical anomaly scores (Fig. 7-23) and the PC1 scores (Table 7-XI; Fig. 7-
22A) of fuzzified evidential scores of proximity to geological features (Table 7-X). Triangles
represent locations of known epithermal Au deposit occurrences. (B) Prediction-rate curve of
proportion of deposits demarcated by the predictions versus proportion of study area predicted as
prospective. The dots along the prediction-rate curve represent classes of prospectivity values that
correspond spatially with a number of cross-validation deposits (indicated in parentheses).

geological wildcat model performs equally as well as the fuzzy logic model (Fig. 7-17B)
and the evidential belief model (Fig. 7-19C). However, if more 50% of the case study
area is considered prospective, then the integrated geochemical-geological wildcat model
is inferior to the fuzzy logic model and evidential belief model. These results,
nonetheless, indicate that wildcat modeling of mineral prospectivity is a potentially
useful tool for guiding further exploration in greenfields frontier areas.
The wildcat methodology bears out the usefulness of reconnaissance (or small-scale)
geological maps in first-pass assessment of mineral prospectivity of greenfields
geologically permissive areas. The methodology, which is a knowledge-guided data-
driven technique for modeling of mineral prospectivity, is sensitive to the widths of
classes of proximity to geological features and to the types of geological features used in
the analysis. As demonstrated by Carranza and Hale (2002d), narrower classes of
proximity to geological features results in predictive models of mineral prospectivity
with higher prediction-rates. In the case study, classes of proximity narrower than the 5-
percentile intervals of map distances are likely to result in higher prediction-rates
although proving this hypothesis is not an objective here. The choice of which type of
geological features to include in modeling mineral prospectivity depends on the general
knowledge of which general types of mineral deposits are likely to occur in certain
greenfields geologically permissive areas. A good knowledge of general characteristics