Page 14 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Predictive Modeling of Mineral Exploration Targets 9

however, can be dynamic, especially modeling of drainage and soil geochemical
anomalies (e.g., Tardy et al., 2004). Predictive modeling of prospective areas, however,
is usually static. Finally, mechanistic and empirical modeling can be one-dimensional (1-
D), two-dimensional (2-D), three-dimensional (3-D) or four-dimensional (4-D). The
following sections review the concepts and practises of only 2-D mapping or predictive
modeling of significant geochemical anomalies and prospective areas, which comprise
the scope of this volume.

PREDICTIVE MODELING OF SIGNIFICANT GEOCHEMICAL ANOMALIES

Exploration geochemical data are generated in mineral exploration programmes
through systematic measurements of one or more chemical properties of samples of
certain Earth materials (sediment, water, soil, rock, vegetation, gas, etc.). Detailed
explanations of concepts and practises of sampling different Earth materials for mineral
exploration can be found in Levinson (1974), Rose et al. (1979), Govett (1983), Butt and
Zeegers (1992), Kauranne et al. (1992), Hale and Plant (1994) and Hale (2000). In most
cases, the chemical property determined from each sample is the concentration of one or
more elements. Such element content determinations aim to find areas with enriched
concentrations of one or more indicator or pathfinder elements, which could betray the
presence of mineral deposits of the type sought.
The normal concentration of an element in non-mineralised Earth materials is
referred to as background. It is more realistically viewed as a range of values rather than
an absolute value because the distribution of any element in any particular Earth material
is rarely uniform and varies considerably from one type of Earth material to another and
from one location to another. Therefore, uni-element background is determined by
stochastic, empirical or hybrid stochastic-empirical modeling techniques whenever a
new area is explored for certain types of mineral deposits. The upper limit of the range
of background values is called the threshold. Uni-element concentrations greater than the
threshold are collectively called anomaly. Anomalous uni-element concentrations that
indicate presence of mineral deposits are called significant anomalies.
The spatial variations of concentrations of any element can be called geochemical
landscape; therefore, background and anomaly both occupy space. Areas characterised
by normal concentrations of indicator or pathfinder elements are called background,
whereas areas characterised by concentrations of at least one indicator or pathfinder
element greater than the threshold are called anomalies. In an exploration area,
anomalies can be delineated once threshold values in individual uni-element data sets are
determined. The different methods used traditionally in modeling of geochemical
thresholds can be classified broadly into two categories of analysis, namely (Levinson,
1974; Rose et al., 1979; Howarth, 1983b): (1) analysis of frequency distributions of uni-
element concentrations; and (2) analysis of multi-element associations. In the latter, the
concept of threshold in uni-element concentration data is extended to data values derived
from analysis and synthesis of multi-element data sets by application of multivariate
statistical methods.