Page 189 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Knowledge-Driven Modeling of Mineral Prospectivity 191

validation of a knowledge-driven mineral prospectivity map yields estimates of its
prediction-rate.
Although this chapter discusses the performances of knowledge-driven mineral
prospectivity maps derived via applications of the individual modeling techniques
explained, it does not mean that the examples of evidential class scores, evidential maps
weights and output maps presented portray the ‘best’ possible prediction models of
mineral prospectivity in the case study area. The general ways of deriving an optimal
prediction model of mineral prospectivity (i.e., predictive model calibration) are
discussed in Chapter 1. One must note, however, that calibration of knowledge-driven
predictive modeling of mineral prospectivity is possible only when cross-validation
deposits are available. Considering that this is the case, some additional guidelines for
calibration of GIS-based knowledge-driven predictive modeling of mineral prospectivity
are given here.

GENERAL PURPOSE APPLICATIONS OF GIS
The types of GIS operations principally used in knowledge-driven mineral
prospectivity mapping include retrieval, (re-)classification and map overlay (see Chapter
2). The first two operations are concerned with spatial evidence representation (i.e.,
evidential map creation) whilst the last operation is concerned with spatial evidence
integration. In the case when certain prospectivity recognition criteria are represented by
input spatial data of continuous fields (e.g., distances to faults/fractures), the
classification operation results in an evidential map of either binary or multi-class
discrete geo-objects (e.g., classes of proximity) (Fig. 7-1). When certain prospectivity
recognition criteria are represented by input spatial data of discrete fields (e.g.,
derivative data obtained from geochemical data analysis; see Chapters 3 to 5), the re-
classification operation also results in an evidential map of either binary or multi-class
discrete geo-objects (e.g., ranges of derivative geochemical data) (Fig. 7-1). The scores
for the evidential classes are then assigned in the attribute tables associated with
individual evidential maps. The assignment of evidential class scores and evidential map
weights and the integration of evidential maps vary depending on which modeling
technique is applied (see further below).
In order to obtain a mineral prospectivity map, evidential maps are combined via
certain computational functions considered by the modeler as appropriately representing
the interactions or inter-relationships among the various geologic controls and surficial
manifestations of mineral occurrence portrayed by the individual evidential maps, thus:

prospectiv ity map = f (evidential maps ) .

There are different forms of the computational function f. In knowledge-driven modeling
of mineral prospectivity, f can be either logical functions (e.g., AND and/or OR
operators, etc.) or arithmetic functions. The applications of these functions, which are