Page 235 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Knowledge-Driven Modeling of Mineral Prospectivity 237

In order to create maps of spatial indicators of geologic controls, the wildcat
methodology makes use of the inverse distance to geological features in the
representation or creation of evidential maps of mineral prospectivity. This is based on
general knowledge that mineral deposits preferentially occur proximal to rather distal to
certain geological features that play certain roles in mineralisation. Thus, for each class
of proximity to a set of geological features, an evidential score, S c (c=1,2,…,n) is defined
as:

S = 1 (7.20)
c ~
d c

~
where d is median distance in each proximity class. Because the types and relative
c
strengths of spatial associations of individual sets of geological features with mineral
deposits are (presumably) unknown, scoring bias due to non-uniform classification of
data is avoided by using the same number of equal-area or equal-percentile classes of
proximity to individual sets of geological features.
For the case study area, Table 7-IX shows values of S c for 5-percentile intervals of
distances to NNW-, NW- and NE-trending faults/fractures and to the mapped units of
Nabongsoran Andesite porphyry (Fig. 3-9). The NE-trending faults/fractures and the
mapped units of Nabongsoran Andesite porphyry are used here because they are,
respectively, plausible structural and heat-source controls on hydrothermal
mineralisation, but it is presumed that the case study area is a greenfields exploration
area and thus there is lack of knowledge of spatial association between epithermal Au
deposits and these geological features. The intersections of NNW- and NW-trending
faults/fractures are not used here because, in the reconnaissance stage of exploration, (it
is presumed that) there is insufficient a-priori knowledge that these particular types of
geological features are associated with hydrothermal mineral deposits in the case study
area. Table 7-IX and Fig. 7-21A show that values of S c decrease exponentially as
distance to individual sets of geological features increases. This is a rather pessimistic
representation or characterisation of spatial geological evidence of mineral deposit
occurrence, especially in the reconnaissance stage of exploration. In addition, the range
of values of S c is different for each set of geological features, suggesting, for example,
that the mapped NE-trending faults/fractures are more important structural controls of
hydrothermal mineralisation than the mapped NW-trending faults/fractures (Table 7-IX
and Fig. 7-21A). Because, in the reconnaissance stage of exploration, (it is presumed
that) there is insufficient a-priori knowledge about which sets of geological features are
geologic controls on hydrothermal mineralisation in the case study area, then it is
reasonable to ‘equalise’ the range of evidential scores for classes of proximity to
individual sets of geological features. This is achieved by applying a fuzzy logistic
membership function to a set of values of S c, thus: