Page 246 - 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. 249

Chapter 8

DATA-DRIVEN MODELING OF MINERAL PROSPECTIVITY

INTRODUCTION
Data-driven mineral prospectivity mapping is appropriate in areas representing
moderately- to well-sampled (or so-called ‘brownfields’) mineralised landscapes, where
the objective is to demarcate new targets for further exploration of undiscovered deposit-
type locations based on the following suppositions. Known deposit-type locations are a
sample set of locations with high likelihood of mineral deposit occurrence in a
mineralised landscape. This sample set embodies or provides a collection of geological
knowledge that there are certain combinations of spatial evidential features (e.g.,
proximity to faults/fractures, geochemical anomalies, etc.) associated with every deposit-
type location in a mineralised landscape. This collection of geological knowledge, which
constitutes a conceptual model of prospectivity recognition criteria for undiscovered
deposit-type locations, indicates that mineral deposit occurrence is a function of the
degrees of presence and relative importance of individual pieces of spatial evidential
features (see Fig. 1-2). Thus, if more important evidential features are present in one
location than in another location in a mineralised landscape, then the former location has
higher mineral prospectivity than the latter location. The conceptual model of mineral
prospectivity recognition criteria provides the framework for establishing or quantifying
empirical spatial associations between a set of known deposit-type locations and
individual sets of spatial evidential data in most, if not all, locations in a mineralised
landscape under investigation (see Fig. 1-3). The quantified empirical spatial
associations between known deposit-type locations and individual sets of spatial
evidential data depict relative indices of likelihood of deposit-type occurrence portrayed
in predictor maps. These predictor maps are then integrated in order to delineate, in a
mineralised landscape, new targets for further exploration of undiscovered deposit-type
locations. The quantified empirical spatial associations of known deposit-type locations
and individual sets of spatial evidential data could also be used in re-defining previously
established conceptual models of prospectivity recognition criteria for mineral deposits
of the type sought in moderately- to well-sampled mineralised landscapes.
In a study area, the relative index of prospectivity (P D) for mineral deposits (D, the
target variable) of the type sought, based on known D deposit-type locations, can be
defined as a function (f) of combinations of a number of X i (i=1,2,…,n) spatial evidential
features (i.e., explanatory/predictor variables), thus: