Page 52 - 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. 51

Chapter 3

EXPLORATORY ANALYSIS OF GEOCHEMICAL ANOMALIES

INTRODUCTION
Among the traditional methods for modeling of uni-element geochemical anomalies
(see Chapter 1), the estimation of threshold as the mean plus (or minus) twice the
standard deviation (hereafter denoted as mean±2SDEV) of a data set is based on classical
statistics and hypothesis testing. The application of classical statistics fundamentally
assumes that data consist of independent samples and have a normal distribution. These
assumptions also apply to probabilistic data analysis (e.g., testing significance or
probability levels of independence or normality). The assumptions in probabilistic and
classical statistical data analyses are rigorous and require that data have been collected
under rather carefully controlled conditions as in physical experiments. Whilst mineral
explorationists strive to collect precise and accurate geochemical data, there are several
uncontrollable factors that influence the values and variations of element contents in
Earth materials that they sample. Such factors include not only geogenic (e.g., metal-
scavenging by Fe-Mn oxides, lithology, etc.) and anthropogenic (i.e., man-induced)
processes but also sampling and analytical procedures. Thus, uni-element geochemical
data sets invariably contain more than one population, each of which represents a unique
process. In addition, because geogenic processes are spatially dependent on one another
and invariably explain the highest proportion of variations in uni-element contents in
geochemical samples, it follows that geochemical data are invariably not spatially
independent. Thus, many uni-element geochemical data sets invariably do not follow a
normal distribution model (e.g., Vistelius, 1960; Reimann and Filzmoser, 1999). Certain
transformations are usually applied to ‘normalise’ the values in a uni-element
geochemical data set (Miesch, 1977; Joseph and Bhaumik, 1997), but even then most, if
not all, transformed uni-element geochemical data sets only approximate a normal
distribution (e.g., McGrath and Loveland, 1992). If a geochemical data set contains more
than one population and does not follow a normal distribution model, then estimation of
threshold as the mean±2SDEV can lead to spurious models of geochemical anomalies.
As an example here, Fig. 3-1 shows that the distribution of Fe concentrations in soil
displayed in Fig. 1-1 clearly deviates from normality and consists of at least two
populations (Fig. 3-1). Based on the given statistics in Fig. 3-1, the threshold estimated
as mean+2SDEV of the data is greater than the maximum value in the data. The log e-
transformed soil Fe values also do not approximate a log-normal distribution model and
highlight the presence of at least two populations (Fig. 3-2). Based on the given statistics