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Predictive Modeling of Mineral Exploration Targets 15

deposits, such as hydrothermal alterations, could be performed in the field and/or by
using remotely-sensed data sets (e.g., Spatz, 1997; Sabins, 1999; Carranza and Hale,
2002a). Mapping of geological features such as faults and intrusive rocks representing,
respectively, structural and heat-source controls of certain mineral deposits, could be
performed in the field and/or by analysis and interpretation of appropriate geophysical
data sets (Telford et al., 1990; Parasnis, 1997; Kearey et al., 2002). Procedures for
enhancement and extraction of evidential features from geological and geophysical data
sets, however, are beyond the scope of this volume.
Certain geoscience spatial data or certain mapped evidential features require
manipulation or transformation in order to represent a prospectivity recognition criterion.
Data manipulation or transformation involves one or more types of map operations
(Chapter 2), the choice of which depends on the prospectivity recognition criterion to be
represented. For example, a prospectivity recognition criterion of presence of or
proximity to strike-slip faults first requires selection of such faults from the database,
followed by creation of a map of distances to such faults and then discretization of
distances into proximity classes. Likewise, a prospectivity criterion of presence of
geochemical, say Cu, anomalies first requires suitable interpolation of Cu data measured
at discrete locations and then discretization of the interpolated Cu data (Fig. 1-4). The
purpose of manipulating or transforming spatial data or a map of evidential features to
represent certain prospectivity recognition criteria is to model and discretise (or classify
in order to create geo-objects representing) the degree of presence of evidential features
at every location. Methods of weighting of classes of individual prospectivity
recognition criteria in order to create predictor maps involve either knowledge-driven or
data-driven modeling of their spatial associations to mineral deposits of the type sought
(Bonham-Carter, 1994). Data-driven methods of quantifying spatial associations
between classes of prospectivity recognition criteria and mineral deposits of the type
sought are explained further in Chapters 6 and 8. Knowledge-driven methods of
weighting classes of prospectivity recognition criteria with respect to the mineral
deposits of the type sought are explained further in Chapter 7.
Not every data-driven method of quantifying spatial associations between classes of
prospectivity recognition criteria leads directly to creation and then integration of
predictor maps to obtain a predictive map of mineral prospectivity (see Chapter 6). In
addition, not every data-driven method that leads directly to creation of predictor maps
applies knowledge or a conceptual model of the inter-play of geologic controls of
mineral deposits of the type sought in integrating the predictor maps to obtain a
predictive map of mineral prospectivity (see Chapter 8). In contrast, every knowledge-
driven method of creating predictor maps can be used directly in integrating such
predictor maps, although not all such methods apply knowledge or a conceptual model
of the inter-play of geologic controls of mineral deposits of the type sought in integrating
the predictor maps to obtain a predictive map of mineral prospectivity ( see Chapter 7).
Therefore, every method for creating and/or integrating predictor maps has inherent
systemic (or procedural) errors with respect to interactions of geological processes
involved in mineral deposit formation. In addition, every input geoscience spatial data