Page 269 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

272 Chapter 8

usually rare and thus few in number, the validation strategy when using these mineral
deposits in data-driven prospectivity modeling is usually the N-1 strategy. In many cases,
however, data of grade and/or tonnage of known deposit-types of interest are not
available in mineral inventory databases of national geological survey organisations,
although data about their status (e.g., mine, past producer, prospect, showing, etc.) are
usually available in these databases. The mineral deposit status attributes can be used as
basis for cross-validation in data-driven modeling of mineral prospectivity. For example,
Carranza and Hale (2000) and Carranza (2002) used locations of large-scale gold
deposits (i.e., mines and prospects of private mining companies) and small-scale gold
deposits (i.e., artisanal workings by local people) as training and testing subsets and vice
versa for predictive modeling of prospectivity for epithermal Au in the Baguio district
(Philippines). Similarly, Carranza et al. (2008c) used showings/indications of geothermal
activity and developed/explored geothermal prospects as training and testing subsets,
respectively, for predictive modeling of geothermal prospectivity in West Java
(Indonesia).

Spatial subdivision strategies
Within a study area, a representative portion (e.g., the most prospective region) of a
mineralised landscape containing an adequate number of samples (i.e., deposit-type
locations) can be chosen as a training region. The weights (wC ji) for C ji classes in X i
maps of spatial evidential features (see equation (8.2)) derived in the training region are
then applied for data-driven modeling of mineral prospectivity in the whole study area.
Alternatively, a study area may be subdivided into, say, four equal regions, each of
which is used as a training region thereby creating four mineral prospectivity models. In
both of these cross-validation strategies, the deposit-type locations outside the training
region form a testing subset. It can be argued, however, that these cross-validation
strategies form a sort of biased sampling because the geological features and especially
the geologic controls on mineralisation vary from one region to another. It follows that
using different and spatially (or geologically) non-coherent training regions may result in
predictive models of mineral prospectivity that are dissimilar not only in terms of
empirical spatial associations but also in terms of genetic associations between deposit-
type occurrences and geological features. Nevertheless, the application of spatially
coherent training and testing regions to cross-validation allow recognition of different
regions of a mineralised landscape having similar geology and thus mineral prospectivity
compared to the known most prospective region(s) (Agterberg and Bonham-Carter,
2005). GIS-based shape-analytical tools can be useful in determining spatially coherent
prospective regions in a mineralised landscape (see Gardoll et al. (2000) for details).

Other strategies of cross-validation

It is also useful to perform experiments by varying not only the compositions of the
training and testing subsets but also (a) the combinations of X i evidential maps (e.g.,