RDFM—RELATIVE-VALUE, DISCRIMINATION-FACTOR AND FRACTIONAL-RATIO METHODOLOGY: A GEOLOGICAL-STATISTICAL METHOD FOR DEFINING ANOMALIES IN GEOCHEMICAL DATA
RDFM—RELATIVE-VALUE, DISCRIMINATION-FACTOR AND FRACTIONAL-RATIO METHODOLOGY: A GEOLOGICAL-STATISTICAL METHOD FOR
DEFINING ANOMALIES IN GEOCHEMICAL DATA
Colin I. Godwin: cigodwin@yahoo.com
Professor Emeritus, Department of Earth, Ocean and Atmospheric Sciences,
The University of British Columbia, Vancouver, BC, Canada V6T 1Z4
Godwin Consultant Ltd., 2706 -660 Nootka Way, Port Moody, BC, Canada V3H 0B7
ABSTRACT
Relative-value (R-value), Discrimination-factor (D-factor) and Fractional-ratio (F-ratio) Methodology (RDFM) enhances discovery opportunities in geochemical analysis. RDFM is based on the statistical distinction between background and anomalous populations. Specifically, the technique uses: (i) all elements analyzed geochemically, (ii) statistical identification of anomalous and background populations, (iii) renumbering of anomalous and background populations to obtain additive Relative-values [R-values], (iv) addition of R-values to form geologically meaningful Discrimination-factors [D-factors], (v) application of geologically related multi-element D-factors to identify underlying rock types and anomalous signatures for different types of mineral deposits, and (vi) F-ratios to refine rock type identification, and tightly target and provide vectors to ore deposits.
Relative-values (R-values) are determined visually from log-probability plots. R-values are assigned odd numbers as follows: 1 = Low Background Population equal to or below the third quartile, 3 = High Background Population that is above the third quartile and up to the Mixed Population, 5 = Mixed Population of mixed High Background and Clearly Anomalous populations, 7 = Clearly Anomalous Population immediately above the Mixed Population, 9 = Highly Anomalous Population, and 11 = Outliers of Highly Anomalous Population with exceptional concentrations. Because element concentrations are changed to simple R-value numbers with genetic and population significance, those elements that occur in disparate amounts can be combined by adding R-values to make D-factors that are not overshadowed by any element.
Discrimination-factors (D-factors) are the sum of R-values divided by the number of R-values summed. The D-factor that is the average of the summed R-values of two related elements is more robust than the R-value for the single elements. For example, nickel and cobalt correlate closely and the D-factor that is the combination of the two using averaged R-values is stronger than either element by itself. Rock and mineral deposit signatures can be simulated with D-factors that average a number of R-values. For examples, mafic rocks (DMafic) and precious metal deposits (DPrec) are described by the following: DMafic = (RMg + RTi + RV + RCr + RFe + RCo + RNi)/7, and DPrec = (RAg + RAu + RHg + RTl + RAs + RSb + RBi + RTe)/8. In both of these cases, the associated R-values are for elements that provide a geologically well-known association-description of mafic rocks or precious metal deposits. Generally, a D-factor of 4 or above represents values that are almost certainly statistically anomalous.
Fractional-ratios (F-ratios)—ratios using element concentrations, R-values or D-factors—refine interpretations of rock type, and help pinpoint orebody locations within anomalous zones. F-ratios can also be used to define vectors related to zoning patterns related to ore deposits.
RDFM, a “geologists’ factor analysis”, allows geological interpretation of data in a way that has genetic significance. And more specifically, where anomalous values exist for two or more elements, D-factors from addition of R-values enhance the reliability of anomaly determination. F-ratios enhance rock identification, sharply target ore deposit location and provide vectors to ore bodies where zoning is present.
The great practical advantage of RDFM is that it provides a statistical, geological and genetic synthesis of features related to ore deposit discovery. This approach defines factors using probability statistics, and geological knowledge and experience. Consequently, it complements traditional approaches. RDFM will aid in discovery of mineral deposits from regional, detailed soil and lithology geochemical surveys, and indeed, any analytical data set—including assays from drill holes.
Keywords:
relative value, discrimination-factor, geochemistry, RDFM