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Workflow for analysis of compositional data in sedimentary petrology: provenance changes in sedimentary basins from spatio-temporal variation in heavy-mineral assemblages

Published online by Cambridge University Press:  22 August 2018

J. VERHAEGEN Open the ORCID record for J. VERHAEGEN [Opens in a new window] ,
G.J. WELTJE  and
D. MUNSTERMAN
J. VERHAEGEN*
Affiliation:
Department of Earth and Environmental Sciences, University of Leuven, Celestijnenlaan 200E, P.O. Box 2410, 3001 Leuven, Belgium
G.J. WELTJE
Affiliation:
Department of Earth and Environmental Sciences, University of Leuven, Celestijnenlaan 200E, P.O. Box 2410, 3001 Leuven, Belgium
D. MUNSTERMAN
Affiliation:
Netherlands Institute of Applied Geoscience TNO – The Geological Survey of the Netherlands, P.O. Box 80015, 3508TA Utrecht, The Netherlands
*
†Author for correspondence: [email protected].
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Abstract

The field of provenance analysis has seen a revival in the last decade as quantitative data-acquisition techniques continue to develop. In the 20th century, many heavy-mineral data were collected. These data were mostly used as qualitative indications for stratigraphy and provenance, and not incorporated in a quantitative provenance methodology. Even today, such data are mostly only used in classic data tables or cumulative heavy-mineral plots as a qualitative indication of variation. The main obstacle to rigorous statistical analysis is the compositional nature of these data which makes them unfit for standard multivariate statistics. To gain more information from legacy data, a straightforward workflow for quantitative analysis of compositional datasets is provided. First (1) a centred log-ratio transformation of the data is carried out to fix the constant-sum constraint and non-negativity of the compositional data. Next, (2) cluster analysis is followed by (3) principal component analysis and (4) bivariate log-ratio plots. Several (5) proxies for the effects of sorting and weathering are included to check the provenance significance of observed variations and finally a (6) spatial interpolation of a provenance proxy extracted from the dataset can be carried out. To test this methodology, available heavy-mineral data from the southern edge of the Miocene North Sea Basin are analysed. The results are compared with available information from literature and are used to gain improved insight into Miocene sediment input variations in the study area.

Keywords

Multivariate statisticsdata analysispalaeogeographyMioceneNeogeneNorth Sea Basin
Type
Original Article
Information
Geological Magazine , Volume 156 , Issue 7 , July 2019 , pp. 1111 - 1130
Copyright
Copyright © Cambridge University Press 2018 

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