Page 17 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

12 Chapter 1

2007) and sometimes with topographic elevation (Grunsky and Smee, 1999; Grunsky,
2006). Subtle but significant geochemical anomalies can also be enhanced and
recognised by applications of selective or partial extraction geo-analytical techniques to
measure element concentrations (e.g., Smee, 1998); however, geo-analysis of element
contents in geochemical samples is a topic that is beyond the scope of this volume. We
now turn to modeling of mineral prospectivity by integrating mapped evidential features
such as significant geochemical anomalies.

PREDICTIVE MODELING OF MINERAL PROSPECTIVITY

The term mineral prospectivity refers to the chance or likelihood that mineral
deposits of the type sought can be found in a piece of land. It is similar to the terms
mineral potential and mineral favourability, which refer to the chance or likelihood that
mineral deposits of the type sought are contained in a piece of land. The terms mineral
prospectivity, mineral potential and mineral favourability are therefore synonymous and
can be used interchangeably. For consistency, the term mineral prospectivity is used in
this volume. Because the presence of mineral deposits of the type sought is betrayed by
the presence of certain evidential features (e.g., significant geochemical anomalies),
mineral prospectivity is thus related to the degree of presence of evidential features.
Thus, modeling mineral prospectivity intrinsically assumes, as illustrated in Fig. 1-2, that
(a) a specific location is prospective if it is characterised by the same or similar
evidential features as known locations of mineral deposits of the type sought and (b) if
more important evidential features are present in one location than in another location in
a mineralised landscape, then the former has higher mineral prospectivity than the latter..
Modeling of mineral prospectivity is involved at every scale, from regional-scale to
local-scale, of exploration target generation (cf. Hronsky and Groves, 2008). It is
concerned with the analysis and integration of evidential features derived from multi-
source geoscience spatial data sets in order to delineate and rank prospective areas for
further exploration of undiscovered mineral deposits of the type sought. In regional-scale
target generation, modeling of mineral prospectivity aims to delineate the most
prospective areas within large permissive regions. In district- to local-scale target
generation, modeling of mineral prospectivity aims to define the most prospective zones
or sites within regional-scale prospective areas. This means that from regional-scale to
local-scale mineral exploration, geoscience data sets used in modeling of mineral
prospectivity should have increasing detail and accuracy both in terms of spatial
resolution and information content.
A predictive model of mineral prospectivity must pertain to just one type of mineral
deposit. Thus, a mineral prospectivity model for epithermal Au deposits is not applicable
to guide exploration for porphyry Cu deposits, and vice versa. In any scale of target
generation, however, modeling of mineral prospectivity follows specific steps starting
with the definition of a conceptual model of mineral prospectivity for mineral deposits of
the type sought (Fig. 1-3). Such a conceptual model is prescriptive rather than predictive,
as it specifies in words and/or diagrams the theoretical relationships between various