Tectonic discrimination diagrams revisited

[1] The decision boundaries of most tectonic discrimination diagrams are drawn by eye. Discriminant analysis is a statistically more rigorous way to determine the tectonic affinity of oceanic basalts based on their bulk-rock chemistry. This method was applied to a database of 756 oceanic basalts of known tectonic affinity (ocean island, mid-ocean ridge, or island arc). For each of these training data, up to 45 major, minor, and trace elements were measured. Discriminant analysis assumes multivariate normality. If the same covariance structure is shared by all the classes (i.e., tectonic affinities), the decision boundaries are linear, hence the term linear discriminant analysis (LDA). In contrast with this, quadratic discriminant analysis (QDA) allows the classes to have different covariance structures. To solve the statistical problems associated with the constant-sum constraint of geochemical data, the training data must be transformed to log-ratio space before performing a discriminant analysis. The results can be mapped back to the compositional data space using the inverse log-ratio transformation. An exhaustive exploration of 14,190 possible ternary discrimination diagrams yields the Ti-Si-Sr system as the best linear discrimination diagram and the Na-Nb-Sr system as the best quadratic discrimination diagram. The best linear and quadratic discrimination diagrams using only immobile elements are Ti-V-Sc and Ti-V-Sm, respectively. As little as 5% of the training data are misclassified by these discrimination diagrams. Testing them on a second database of 182 samples that were not part of the training data yields a more reliable estimate of future performance. Although QDA misclassifies fewer training data than LDA, the opposite is generally true for the test data. Therefore LDA is a cruder but more robust classifier than QDA. Another advantage of LDA is that it provides a powerful way to reduce the dimensionality of the multivariate geochemical data in a similar way to principal component analysis. This procedure yields a small number of ‘‘discriminant functions,’’ which are linear combinations of the original variables that maximize the between-class variance relative to the within-class variance.