Page 188 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

190 Chapter 7

applicability of knowledge from well-explored areas to frontier geologically permissive
areas and/or (b) degree of accuracy of exploration data available in frontier geologically
permissive areas. If the degree of one or both of these two factors is considered high,
then it is appropriate to model mineral prospectivity by using multi-class evidential
maps; otherwise, it is appropriate to model mineral prospectivity by using binary
evidential maps.
This chapter explains the concepts of different modeling techniques for knowledge-
driven mapping of mineral prospectivity, which employ either binary or multi-class
evidential maps. Each of the modeling techniques is then demonstrated in mapping of
prospectivity for epithermal Au deposits in the case study Aroroy district (Philippines)
(Fig. 3-9). It is assumed that (a) there are very few known occurrences of epithermal Au
deposits in the case study area and (b) the following prospectivity recognition criteria
represent knowledge of epithermal Au prospectivity developed in other areas having
very similar geological settings as the case study area.
ƒ Proximity to NNW-trending faults/fractures.
ƒ Proximity to NW-trending faults/fractures.
ƒ Proximity intersections of NNW- and NW-trending faults/fractures.
ƒ Presence of multi-element stream sediment geochemical anomalies.
The common spatial data sets used in the applications of the individual modeling
techniques are: (a) distance to NNW-trending faults/fractures; (b) distance to NW-
trending faults/fractures; (c) distance to intersections of NNW- and NW-trending
faults/fractures; and (d) integrated PC2 and PC3 scores obtained from the catchment
basin analysis of stream sediment geochemical data (see Fig. 5-12). For the first three
prospectivity recognition criteria and the first three data sets, the threshold distances to
geologic structures that are used for the case demonstrations below are (a) 0.35
(rounded-off from 0.325) km of NNW-trending faults/fractures (see Table 6-IX), (b) 0.9
km of NW-trending faults/fractures (see Table 6-IX) and 1 km of intersections of NNW-
and NW-trending faults/fractures (see Table 6-IX). For the last prospectivity recognition
criterion and the last data set, the threshold value of multi-element geochemical anomaly
that is used for the case demonstrations below is 0.34 (see Figs. 6-12E and 6-12F).
The threshold values of spatial data specified by the prospectivity recognition criteria
form the bases for assignment of evidential class scores in individual evidential maps,
particularly binary evidential maps, in knowledge-driven mineral prospectivity mapping.
This means that the conceptual model of mineralisation controls is effectively a
prescriptive model. The implementation of this prescriptive model in knowledge-driven
mineral prospectivity mapping, especially in new exploration areas, results in a
predictive model. The performance of a knowledge-driven predictive map of mineral
prospectivity can then be evaluated against the (very) few known occurrences of mineral
deposits of the type sought in a study area (Agterberg and Bonham-Carter, 2005; Chung
and Fabbri, 2005). Because the known occurrences of mineral deposits of the type
sought are not directly used (i.e., they are presumed undiscovered) in the creation of
evidential maps (i.e., estimation of evidential class scores and evidential map weights),
as in data-driven modeling (see next chapter), the performance evaluation or cross-