Page 205 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Knowledge-Driven Modeling of Mineral Prospectivity 207

whereas the locations of faults indicated in maps are more-or-less their ‘true’ surface
locations. Thus, for locations within the range of distances to certain faults where the
positive spatial association with mineral deposits is optimal, the evidential scores
assigned are highest but these scores decrease slowly from maximum at the threshold
distance to a lower score at the minimum distance (Fig. 7-8). For locations beyond the
threshold distance to certain faults, where the positive spatial association with mineral
deposits is non-optimal, the evidential scores assigned decrease rapidly from maximum
at the threshold distance to the minimum evidential score at the maximum distance. The
same line of reasoning can be accorded to multi-class representation of evidence for the
presence of surficial geochemical anomalies, which may be significant albeit
allochthonous (i.e., located not directly over the mineralised source) (Fig. 7-8). Note,
therefore, that the graph of multi-class evidential scores versus data of spatial evidence is
more-or-less consistent with the shapes of the D curves (Figs. 6-9 to 6-12) in the
analyses of spatial associations between epithermal Au deposit occurrences and
individual sets of spatial evidential data in the case study area. Thus, multi-class
representation of evidence of mineral prospectivity is suitable in cases where the level of
knowledge applied is seemingly complete and/or when the accuracy or resolution of
available spatial data is satisfactory. We now turn to the individual techniques for
knowledge-based multi-class representation and integration of spatial evidence that can
be used in order to derive a mineral prospectivity map.

Multi-class index overlay modeling
th
This is an extension of binary index overlay modeling. Each of the j classes of the
th
i evidential map is assigned a score S ij according to their relevance to the proposition
under examination. The class scores assigned can be positive integers or positive real
values. There is no restriction on the range of class scores, except that the range of class
scores in every evidential map must be compatible (i.e., the same minimum and
maximum values). This means that it is impractical to control the relative importance of
an evidential map in terms of the proposition under consideration by making the range of
its class scores different from the range of class scores in another evidential map. The
relative importance of an evidential map compared to each of the other evidential maps
is controlled by assignment of weights W i, which are usually positive integers. Weighted
evidential maps are then combined using the following equation, which calculates an
average weighted score ( S ) for each location (Bonham-Carter, 1994):

n
S
¦ ij W i
S = i (7.2)
n
W
¦ i
i

The output value S for each location is the sum of the products of S ij and W i in each
evidential map divided by the sum of W i for each evidential map. Recent examples of