Page 268 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Data-Driven Modeling of Mineral Prospectivity 271

In the preceding chapter, Fig. 7-2 shows the schematic GIS-based procedures for
creating a prediction-rate curve associated with a mineral prospectivity map. The same
schematic procedures can be applied for creating a fitting-rate curve, but using a map of
the subset of prediction (or training) deposit-type locations instead of the subset of cross-
validation (or testing) deposit-type locations. By switching the roles of the training and
testing subsets, at least two data-driven models of mineral prospectivity are thus derived,
which provide the opportunity to answer the two model validation questions posed in
Chapter 1. There are various strategies of cross-validation in data-driven modeling of
mineral prospectivity (cf. Agterberg and Bonham-Carter, 2005; Chung and Fabbri, 2005;
Fabbri and Chung, 2008), the common objective of which is to establish an optimum
predictive model of mineral prospectivity having the best fitting- and prediction-rates.

N–n strategies
Given a set of a number, N, of known deposit-type locations, n (≤50% of N) deposit-
type locations can be used for testing and the remaining N–n deposit-type locations are
used for training. In cases where N is relatively small, one deposit-type location is used
for testing and the remaining N–1 deposit-type locations are used for training so that
data-driven modeling of mineral prospectivity is thus performed N times, each time with
th
a different N-1 training subset and a different N testing subset. In cases where N is large
(say 321 as in the case of modeling of prospectivity for alkalic porphyry Cu-Au deposits
in British Columbia (see Carranza et al., 2008b), the N-1 (or jack-knife) strategy can be
impractical. In such a case, n>1 deposit-type locations out of the N deposit-type
locations can be used for testing and the remaining N–n deposit-type locations are used
for training so that it is not necessary to perform data-driven modeling of mineral
prospectivity N times. Still, several (although less than N) iterations of mineral
prospectivity modeling with different N–n subsets are necessary to establish an optimum
predictive model. In each of these iterations, the n deposit-type locations are usually
chosen randomly.
The N–(n>1) strategy is probably the most commonly used strategy of cross-
validation in data-driven modeling of mineral prospectivity. Skabar (2005) presented a
sound version of this strategy by replicating an original set of deposit-type locations four
times. From each replicate set, ¾ and ¼ of the deposit-type locations were used for
training and testing, respectively, and each of the four testing subsets did not contain
common deposit-type locations.

Deposit-type classification strategies
Because mineral exploration endeavours to find mineral deposits, especially those
with commercially viable concentrations of minerals or metals for mining purposes, it is
instructive to derive mineral prospectivity models that provide the opportunity for
discovery of high-grade and large tonnage mineral deposits likely to be commercially
viable. Because high-grade and/or large-tonnage mineral deposits of the type sought are