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14 Chapter 1
Fig. 1-3. Elements of predictive modeling of mineral prospectivity.
generation and to the size of the study area. Furthermore, analysis of spatial distributions
of mineral deposits of the type sought (e.g., Carlson, 1991; Vearncombe and
Vearncombe, 1999) and analysis of spatial associations between mineral deposits of the
type sought and certain geological features (e.g., Bonham-Carter, 1985; Carranza and
Hale, 2002b) are useful in defining, testing and, if necessary, re-defining a conceptual
model of mineral prospectivity and the prospectivity recognition criteria. This aspect of
mineral prospectivity modeling is demonstrated in Chapter 6.
The geological, geophysical and geochemical characteristics of areas that contain
mineral deposits of the type sought constitute the prospectivity recognition criteria. The
conceptual model of mineral prospectivity and the prospectivity recognition criteria
provide the framework for mineral prospectivity modeling in terms of determining the
suitable (a) geoscience spatial data sets to be used, (b) evidential features to enhance and
extract from individual geoscience spatial data sets, (c) method of transforming mapped
evidential features into maps of individual prospectivity recognition criteria, (d) method
of weighting classes of individual prospectivity recognition criteria to create predictor
maps and (e) method of integrating predictor maps to create a predictive model or map
of mineral prospectivity. The preceding items (b), (c) and (d) constitute the analysis of
predictive model parameters.
Methods of enhancing and extracting evidential features representing a prospectivity
recognition criterion are specific to evidential themes (i.e., geochemical, geological,
geophysical) and types of geoscience spatial data. The concepts of mapping significant
geochemical anomalies are discussed briefly in the previous section and are treated
further in Chapters 3 to 5. Mapping of evidential geological features of certain mineral