Page 23 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

18 Chapter 1

prospectivity modeling are therefore necessary in order to derive reliable mineral
prospectivity maps, upon which mineral exploration decisions or plans can be based.

PREDICTIVE MODELING WITH A GIS
A GIS aims to provide pertinent and reliable pieces of spatial geo-information to
support decision-making in many fields of endeavour, including mineral exploration. At
every scale, from region-scale to local-scale, of exploration target generation, GIS has
become a decision-making tool since about the late 1980s (Bonham-Carter, 1994). A
major basis for a ‘go’ or a ‘no-go’ decision to proceed into the next higher scale of
exploration target generation or phase of mineral exploration is a set of geochemical
anomaly models or a mineral prospectivity model derived in the previous lower scale(s)
of target generation. To derive and visualise such pieces of spatial geo-information, a
GIS can be used for efficient capture, storage, organisation, query, manipulation,
transformation, analysis and integration of substantial multi-source geoscience data sets
collected in the different scales of exploration target generation. The tasks involved in
modeling of geochemical anomalies and/or prospective areas at any scale of target
generation are therefore numerous, tedious and complex. A GIS does not reduce but
facilitates those tasks to allow rapid yet efficient accomplishment of the pieces of spatial
geo-information of interest. Of the different GIS functionalities mentioned, this volume
is mainly concerned with analysis and integration of data sets to derive and visualise
predictive models of geochemical anomalies and prospective areas. Such functionalities
are briefly discussed below and demonstrated further in the succeeding chapters.

Data analysis
Predictive modeling of geochemical anomalies and/or mineral prospectivity involves
characterisations of the statistical and spatial properties of variables and the spatial
relationships among variables. Such operations are supported by query, manipulation
and transformation of certain data. In a GIS, these operations can be performed using
data attribute tables or data attribute maps. On the one hand, data attribute tables are
useful for summarising the statistical properties of univariate data and for characterising
statistical relationships among univariate data sets. For example, a data attribute table
would be useful in predictive modeling of significant geochemical anomalies via logistic
regression by using binary (i.e., presence or absence of) mineral deposit occurrence as
the target variable and concentrations of various elements as predictor variables. The
predicted values of mineral deposit occurrence ranging from [0] to [1] represent multi-
element geochemical anomalies, which can then be visualised by creating the
corresponding attribute map. On the other hand, data attribute maps are useful for
characterising the spatial properties of univariate data and for characterising spatial
relationships among univariate data sets. For example, an overlay or cross operation
using a map of classified interpolated point-data Cu values and a map of mineral deposit
occurrences would result in a cross-table indicating which class/classes of Cu values
represent significant geochemical anomalies (Fig. 1-5).