Tectonic discrimination of basalts with classification trees

Traditionally, geochemical classification of basaltic rocks of unknown tectonic affinity has been performed by discrimination diagrams. Although easy to use, this method is fairly inaccurate because it only uses bi- or trivariate data. Furthermore, many popular discrimination diagrams are statistically not very rigorous because the decision boundaries are drawn by eye, and they ignore closure, thus violating the rules of compositional data analysis. Classification trees approximate the data space by a stepwise constant function, and are a more rigorous and potentially more effective way to determine tectonic affinity. Trees allow the simultaneous use of an unlimited number of geochemical features, while still permitting visualization by an easy-to-use, two-dimensional graph. Two classification trees are presented for the discrimination of basalts of mid-ocean ridge, ocean island, and island arc affinities. The first tree uses 51 major, minor, and trace elements and isotopic ratios and should be used for the classification of fresh basalt samples. A second tree only uses high field strength element analyses and isotopic ratios, and can also be used for basalts that have undergone alteration. The probability of successful classification is 89% for the first and 84% for the second tree, as determined by 10-fold cross-validation. Even though the trees presented in this paper use many geochemical features, it is not a problem if some of these are missing in the unknown sample. Classification trees solve this problem with surrogate variables, which give more or less the same decision as the primary variables. The advantages of the classification tree approach over discrimination diagrams are illustrated by a comparative test on a sample dataset of known tectonic affinities. Although arguably better than discrimination diagrams, classification trees are not perfect, and the limitations of the method are illustrated on a published dataset of basalts from the Pindos Basin (Greece).

[1]  A. Woronow,et al.  Quantifying and testing differences among means of compositional data suites , 1990 .

[2]  F. Chayes.  Ratio Correlation: A Manual for Students of Petrology and Geochemistry , 1971 .

[3]  P. Robinson,et al.  Ophiolites in Earth History , 2004 .

[4]  I. Kushiro,et al.  Origin of Primary Basalt Magmas and Classification of Basaltic Rocks , 1963 .

[5]  John W. Shervais,et al.  Ti-V plots and the petrogenesis of modern and ophiolitic lavas , 1982 .

[6]  John Aitchison,et al.  The Statistical Analysis of Compositional Data , 1986 .

[7]  David A. Wood,et al.  The application of a ThHfTa diagram to problems of tectonomagmatic classification and to establishing the nature of crustal contamination of basaltic lavas of the British Tertiary Volcanic Province , 1980 .

[8]  A. Photiades,et al.  Triassic mid-ocean ridge basalts from the Argolis Peninsula (Greece): new constraints for the early oceanization phases of the Neo-Tethyan Pindos basin , 2003, Geological Society, London, Special Publications.

[9]  Julian A. Pearce,et al.  Tectonic setting of basic volcanic rocks determined using trace element analyses , 1973 .

[10]  Lluís Màrquez i Villodre,et al.  Boosting Trees for Anti-Spam Email Filtering , 2001, ArXiv.

[11]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[12]  F. Chayes,et al.  On distinguishing basaltic lavas of circum-oceanic and oceanic-island type by means of discriminant functions , 1965 .

[13]  J. Pearce.  Statistical Analysis of Major Element Patterns in Basalts , 1976 .

[14]  Janick F Artiola,et al.  Using Geochemical Data: Evaluation, Presentation, Interpretation , 1994 .

[15]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[16]  J. Cann,et al.  Ophiolite origin investigated by discriminant analysis using Ti, Zr and Y , 1971 .

[17]  Pieter Vermeesch,et al.  Tectonic discrimination diagrams revisited , 2006 .