By G.J.S. Govett
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Extra resources for Rock Geochemistry in Mineral Exploration
Background values). It is therefore a self-defeating exercise in some types of data interpretation. , 1975; Chapman, 1976). Even on Ahrens' (1954) own data it can be shown that the distribution of Pb in Canadian granites can be "normalized" by taking the cube root of the values (Chayes, 1954); the distribution of Zr in Canadian granites could as well obey a square root law as a log law and, even more plausibly the distribution could belong to two dominantly normal distributions that overlap (Govett, 1974).
Background is a term that tends to be loosely used in exploration geochemistry; most commonly it implies the abundance of an element in a particular material. Conventionally abundance is represented by the average content of an element (see discussion in Chapter 2). This approach is reasonable for a normally distributed population; however, the frequency distribution of elements is commonly positively skewed, and the arithmetic average is clearly biased by scattered high numbers. Therefore, although the 31 arithmetic mean is a good estimate of the abundance of an element, it is not necessarily a good estimate of the most commonly occurring concentration of that element.
A great deal of Soviet rock geochemical work is characterized by the use of simple arithmetic manipulations of multi-element data based on a general zoning sequence of trace elements. Grigoryan (1974) has stated that despite large variations in the composition of hydrothermal orebodies and differences in geological conditions, there is a remarkably uniform zoning pattern from top (supra-ore elements) to bottom (sub-ore elements) as follows: Ba-(Sb,As,Hg)-Cd-Ag-Pb-Zn-Au-Cu-Bi-Ni-Co-Mo-U-SnBe—W. "Additive halos" (addition or subtraction of different element values standardized to respective background for each element), "multiplicative 41 halos" (multiplication or division of element values), and ratios of supra-ore to sub-ore elements are generally used rather than computer-based statistical techniques (such as discriminant analysis or factor analysis).