Comparability of heavy mineral data – the first interlaboratory round robin test

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  • István Dunkl
  • Hilmar von Eynatten
  • Sergio Andó
  • Keno Lünsdorf
  • Andrew Morton
  • Bruce Alexander
  • Lászlò Aradi
  • Carita Augustsson
  • Heinrich Bahlburg
  • Marta Barbarano
  • Aukje Benedictus
  • Jasper Berndt
  • Irene Blitz
  • Flora Boekhout
  • Tim Breitfeld
  • Joao Cascalho
  • Pedro J.M. Costa
  • Ogechi Ekwenye
  • Kristóf Fehér
  • Valentina Flores-Aqueveque
  • Philipp Führing
  • Paulo Giannini
  • Walter Goetz
  • Carlos Guedes
  • György Gyurica
  • Juliane Hennig-Breitfeld
  • Julian Hülscher
  • Mahdi Jafarzadeh
  • Robert Jagodziński
  • Sándor Józsa
  • Péter Kelemen
  • Nynke Keulen
  • Marijan Kovacic
  • Christof Liebermann
  • Mara Limonta
  • Borna Lužar-Oberiter
  • Frane Markovic
  • Dóra Georgina Miklós
  • Ogechukwu Moghalu
  • Ian Mounteney
  • Daniel Nascimento
  • Tea Novaković
  • Gabriella Obbágy
  • Mathias Oehlke
  • Jenny Omma
  • Sandra Passchier
  • Katharina Pfaff
  • Luisa Pinto Lincoñir
  • Matthew Power
  • Ivan Razum
  • Alberto Resentini
  • Tamás Sági
  • Dorota Salata
  • Rute Salgueiro
  • Jan Schönig
  • Maria Sitnikova
  • Beata Sternal
  • György Szakmány
  • Monika Szokaluk
  • Edit Thamó-Bozsó
  • Ágoston Tóth
  • Jonathan Tremblay
  • Jasper Verhaegen
  • Tania Villaseñor
  • Michael Wagreich
  • Anna Wolf
  • Kohki Yoshida

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  • Georg-August-Universität Göttingen
  • University of Milano
  • HM Research Associates
  • University of Greenwich
  • Eötvös University Budapest
  • Institutt for Energiressurser
  • Westfälische Wilhelms-Universität Münster
  • Faculty of Science Shinshu University
  • Rocktype Ltd
  • Universität Wien
  • Universidad de Chile
  • Landesamt für Bergbau, Energie und Geologie Geozentrum
  • University of Nigeria
  • Universidade de Lisboa
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  • ARQMAR Centre for Maritime Archaeology Research of the Southeastern Pacific
  • University of São Paulo Rua do Lago
  • Max-Planck-Institut für Sonnensystemforschung
  • Universidade Federal do Paraná
  • Department of Applied and Environmental Geology, Mining and Geological Survey of Hungary
  • Royal Holloway University of London
  • Freie Universität Berlin
  • Faculty of Earth Sciences
  • Adam Mickiewicz University in Poznań
  • Geological Survey of Denmark and Greenland (GEUS)
  • University of Zagreb
  • University of Milano Bicocca
  • University of Zagreb Geološko-paleontološki zavod
  • British Geological Survey
  • Universidade Federal do Ceará
  • Hungarian Academy of Sciences, Debrecen
  • Montclair State University
  • Hungarian Academy of Sciences
  • Oil, Gas & Chemicals Services, SGS Canada Inc.
  • Croatian Natural History Museum
  • Jagiellonian University
  • LNEG-Laboratório Nacional de Energia e Geologia
  • University of Göttingen
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  • Katholieke Universiteit Leuven


Heavy minerals are typically rare but important components of siliciclastic sediments and rocks. Their abundance, proportions, and variability carry valuable information on source rocks, climatic, environmental and transport conditions between source to sink, and diagenetic processes. They are important for practical purposes such as prospecting for mineral resources or the correlation and interpretation of geologic reservoirs. Despite the extensive use of heavy mineral analysis in sedimentary petrography and quite diverse methods for quantifying heavy mineral assemblages, there has never been a systematic comparison of results obtained by different methods and/or operators. This study provides the first interlaboratory test of heavy mineral analysis. Two synthetic heavy mineral samples were prepared with considerably contrasting compositions intended to resemble natural samples. The contributors were requested to provide (i) metadata describing methods, measurement conditions and experience of the operators and (ii) results tables with mineral species and grain counts. One hundred thirty analyses of the two samples were performed by 67 contributors, encompassing both classical microscopic analyses and data obtained by emerging automated techniques based on electron-beam chemical analysis or Raman spectroscopy. Because relatively low numbers of mineral counts (N) are typical for optical analyses while automated techniques allow for high N, the results vary considerably with respect to the Poisson uncertainty of the counting statistics. Therefore, standard methods used in evaluation of round robin tests are not feasible. In our case the ‘true’ compositions of the test samples are not known. Three methods have been applied to determine possible reference values: (i) the initially measured weight percentages, (ii) calculation of grain percentages using estimates of grain volumes and densities, and (iii) the best-match average calculated from the most reliable analyses following multiple, pragmatic and robust criteria. The range of these three values is taken as best approximation of the ‘true’ composition. The reported grain percentages were evaluated according to (i) their overall scatter relative to the most likely composition, (ii) the number of identified components that were part of the test samples, (iii) the total amount of mistakenly identified mineral grains that were actually not added to the samples, and (iv) the number of major components, which match the reference values with 95% confidence. Results indicate that the overall comparability of the analyses is reasonable. However, there are several issues with respect to methods and/or operators. Optical methods yield the poorest results with respect to the scatter of the data. This, however, is not considered inherent to the method as demonstrated by a significant number of optical analyses fulfilling the criteria for the best-match average. Training of the operators is thus considered paramount for optical analyses. Electron-beam methods yield satisfactory results, but problems in the identification of polymorphs and the discrimination of chain silicates are evident. Labs refining their electron-beam results by optical analysis practically tackle this issue. Raman methods yield the best results as indicated by the highest number of major components correctly quantified with 95% confidence and the fact that all laboratories and operators fulfil the criteria for the best-match average. However, a number of problems must be solved before the full potential of the automated high-throughput techniques in heavy mineral analysis can be achieved.


Original languageEnglish
Article number103210
Number of pages27
JournalEarth Science Reviews
Issue numberDecember
Publication statusPublished - 16 Jun 2020