RT Journal Article
SR Electronic
T1 Bayesian Geochemical Discrimination of Mafic Volcanic Rocks
JF American Journal of Science
JO Am J Sci
FD American Journal of Science
SP 191
OP 209
DO 10.2475/ajs.306.3.191
VO 306
IS 3
A1 Jeffrey Shragge
A1 Cameron A. Snow
YR 2006
UL http://www.ajsonline.org/content/306/3/191.abstract
AB Determining the original tectonic setting of volcanic rocks via their geochemical signature has been a long-standing goal for petrologists. However, current visually based methods for geochemical discrimination afford only limited success. We develop a probabilistic method for geochemical discrimination based upon statistics generated from geochemical databases and Bayesian analysis. This method incorporates elemental covariance, accounts for data measurement and theoretical uncertainty, and is not restricted in dimensionality of analysis, which is inherent in visual systems of discrimination. Furthermore, the method provides a direct way to discern statistical outliers whose inclusion would otherwise lead to lower discrimination accuracy. Tests of the approach yielded successful classification rates for single analyses of over 90 percent for volcanic arc basalts, mid-ocean ridge basalts, and ocean island basalts.