Page 187 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Geochemical Anomaly and Mineral Prospectivity Mapping in GIS
by E.J.M. Carranza
Handbook of Exploration and Environmental Geochemistry, Vol. 11 (M. Hale, Editor)
© 2009 Elsevier B.V. All rights reserved. 189

Chapter 7

KNOWLEDGE-DRIVEN MODELING OF MINERAL PROSPECTIVITY

INTRODUCTION
Knowledge-driven mineral prospectivity mapping is appropriate in frontier or less-
explored (or so-called ‘greenfields’) geologically permissive areas where no or very few
mineral deposits of the type sought are known to occur. Knowledge of empirical spatial
associations between the mineral deposits and indicative geological features in
moderately- to well-explored areas is the basis of knowledge-driven mineral
prospectivity mapping in frontier geologically permissive areas with similar, if not the
same, geological settings as the former. This means that a conceptual model of mineral
prospectivity developed in moderately- to well-explored areas is applied to mineral
prospectivity mapping in frontier geologically permissive areas. This conceptual model
of mineral prospectivity is considered in the creation of evidential maps (i.e., estimation
of evidential map weights and evidential class scores) and the integration of these
evidential maps according to the proposition that “this location is prospective for
mineral deposits of the type sought”. Thus, the term ‘knowledge-driven’ refers to the
qualitative assessment or weighting of evidence with respect to a proposition. The
estimates of weights for every evidential map and estimates of scores for every class in
an evidential map reflect one’s ‘expert’ judgment of the spatial association between
mineral deposits of the type sought and every set of indicative geologic features.
Accordingly, knowledge-driven mineral prospectivity mapping is also known as expert-
driven mineral prospectivity mapping.
The ‘expert’ knowledge one applies in knowledge-driven mineral prospectivity
mapping may have been obtained via substantial field experiences in mineral exploration
or via substantial experiences in the application of spatial analytical techniques to study
spatial distributions of mineral deposits of the type sought and their spatial associations
with certain geological features (Chapter 6). Alternatively, one may elicit knowledge
from other experts, who have profound expertise in exploration of mineral deposits of
the type sought. The process of eliciting expert knowledge for GIS-based mineral
prospectivity mapping is not well established and is not further treated in this volume. In
this regard, readers are referred to Schuenemeyer (2002) for elicitation of expert
knowledge needed in assessment of fossil fuel resources or to Hodge et al. (2001) for
elicitation of knowledge for engineering applications.
Knowledge-driven mineral prospectivity in frontier geologically permissive areas
may employ either binary or multi-class evidential maps depending on the (a) degree of