Page 234 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

236 Chapter 7

when there is (a) adequate knowledge of geologic controls on mineral deposits of the
type sought and of spatial features indicative of the presence of the same type of mineral
deposits and (b) suitable and highly accurate geoscience data sets for representations of
spatial evidence of mineral prospectivity. A situation of knowledge-driven mineral
prospectivity mapping where the second requirement is available but the first
requirement is lacking is more challenging. For this kind of situation (say, in mineral
exploration of geologically permissive greenfields areas where mineral deposits of
interest are still undiscovered), we turn to a so-called wildcat methodology for
knowledge-driven modeling of mineral prospectivity.

WILDCAT MODELING OF MINERAL PROSPECTIVITY
In practise, it is difficult to develop, elicit or model quantitative knowledge of spatial
associations between mineral deposits of interest and indicative spatial geological
features especially during the early (i.e., reconnaissance) stages of grassroots mineral
exploration. The difficulty arises when only a geological map is available for a given
greenfields area in which no or very few mineral deposit occurrence are known. In
addition, reconnaissance exploration surveys are, in general, more focused on geological
‘permissivity’ of mineral deposit occurrence rather than on deposit-type prospectivity.
Hence, mapping of mineral prospectivity (as opposed to mineral deposit-type
prospectivity), which may be used in guiding further exploration, is faced with the
problem of how to create and integrate geologically meaningful evidential maps of
mineral prospectivity. To solve this problem, a ‘wildcat’ methodology of predictive
mapping of prospective areas can be devised (Carranza, 2002; Carranza and Hale,
2002d). The term ‘wildcat’, according to Whitten and Brooks (1972), means a “highly
speculative exploratory operation”. The term also refers to “a borehole (or more rarely a
mine) sunk in the hope of finding oil (or ore) in a region where deposits of oil (or
metallic ores) have not been recorded” (Whitten and Brooks, 1972). The wildcat
methodology is actually a knowledge-guided data-driven technique of modeling mineral
prospectivity. That is because the evidential class scores are calculated from data,
although certain kinds of general knowledge about mineralisation and relative
importance of pieces of spatial evidence are applied for meaningful calculation and
transformation of evidential class scores and for integration of evidential maps.
The wildcat methodology for modeling mineral prospectivity is built upon the
general qualitative knowledge about the characteristics of the geological environments of
mineral deposits. For example, hydrothermal mineral deposits generally occur in or near
the vicinity of geological features such as igneous intrusions (most often dikes and/or
stocks but seldom batholiths) and faults/fractures. In addition, areas containing
hydrothermal mineral deposits are usually characterised by surficial geochemical
anomalies. In the wildcat methodology, maps of proximity to geological features are first
created and integrated in order to represent a spatial evidence of geologic controls. Then,
spatial evidence of geologic controls are integrated with spatial evidence of geochemical
anomalies.