Geoinformatics
Online ISSN : 1347-541X
Print ISSN : 0388-502X
ISSN-L : 0388-502X
Article
STATISTICAL REVIEW OF MAJOR ELEMENT DATA FROM THE MIOCENE TUFFS IN HUNGARY
Lajos O. KOVACSGabor P. KOVACS
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2002 Volume 13 Issue 3 Pages 153-166

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Abstract

The Miocene volcanic activity in Hungary, the central part of the Carpatho-Pannonian Region, was remarkably heterogeneous in terms of eruption characteristics, structure, texture, mineral and chemical compositions of rock products, etc. Often, the corresponding complexes are classified as belonging to either an acidic, or a calc-alkaline type of volcanism, although the numerous spatial overlaps of the volcanic formations together with the published radiometric age and isotope data introduce uncertainties into any simple typification. In the given study, treated together all available, summing up to several hundred, major element chemical composition data determined in Miocene tuffs are analyzed with simple statistical methods. The major element composition of these tuffs reveals an essential univariate, bivariate and multivariate unimodality, as opposed to the statistical behavior of the corresponding effusives that have a well-known bimodal character. The tuffs have a significantly higher content of compositional water than the lavas. Part of the water in tuffs is of magmatic origin, part of it is due to hydration related to conditions after and/or during eruption. The distribution of Na is similar in both rock types, although a generally more intense surface weathering, leading to Na leaching, in tuffs is suggested. There is a discrepancy also in the potassium distributions which may incorporate a primary (i.e. related to the magmatic stage) difference, and traces of a potassium metasomatism having been able to better develop in some lavas. The bivariate distribution of Fe2O3 and FeO provides an estimate (being about 10%) for the maximum of total iron occurring in the primordial magmas of these rocks. The compositional unimodality of tuffs produced by the represented volcanisms (no matter how many types of them are assumed) suggests significant genetic relations.

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© Japan Society of Geoinformatics 2002
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