TY - JOUR

T1 - Comparability of heavy mineral data – the first interlaboratory round robin test

AU - Dunkl, István

AU - von Eynatten, Hilmar

AU - Andó, Sergio

AU - Lünsdorf, Keno

AU - Morton, Andrew

AU - Alexander, Bruce

AU - Aradi, Lászlò

AU - Augustsson, Carita

AU - Bahlburg, Heinrich

AU - Barbarano, Marta

AU - Benedictus, Aukje

AU - Berndt, Jasper

AU - Blitz, Irene

AU - Boekhout, Flora

AU - Breitfeld, Tim

AU - Cascalho, Joao

AU - Costa, Pedro J.M.

AU - Ekwenye, Ogechi

AU - Fehér, Kristóf

AU - Flores-Aqueveque, Valentina

AU - Führing, Philipp

AU - Giannini, Paulo

AU - Goetz, Walter

AU - Guedes, Carlos

AU - Gyurica, György

AU - Hennig-Breitfeld, Juliane

AU - Hülscher, Julian

AU - Jafarzadeh, Mahdi

AU - Jagodziński, Robert

AU - Józsa, Sándor

AU - Kelemen, Péter

AU - Keulen, Nynke

AU - Kovacic, Marijan

AU - Liebermann, Christof

AU - Limonta, Mara

AU - Lužar-Oberiter, Borna

AU - Markovic, Frane

AU - Melcher, Frank

AU - Miklós, Dóra Georgina

AU - Moghalu, Ogechukwu

AU - Mounteney, Ian

AU - Nascimento, Daniel

AU - Novaković, Tea

AU - Obbágy, Gabriella

AU - Oehlke, Mathias

AU - Omma, Jenny

AU - Onuk, Peter

AU - Passchier, Sandra

AU - Pfaff, Katharina

AU - Lincoñir, Luisa Pinto

AU - Power, Matthew

AU - Razum, Ivan

AU - Resentini, Alberto

AU - Sági, Tamás

AU - Salata, Dorota

AU - Salgueiro, Rute

AU - Schönig, Jan

AU - Sitnikova, Maria

AU - Sternal, Beata

AU - Szakmány, György

AU - Szokaluk, Monika

AU - Thamó-Bozsó, Edit

AU - Tóth, Ágoston

AU - Tremblay, Jonathan

AU - Verhaegen, Jasper

AU - Villaseñor, Tania

AU - Wagreich, Michael

AU - Wolf, Anna

AU - Yoshida, Kohki

PY - 2020/6/16

Y1 - 2020/6/16

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85096001650&partnerID=8YFLogxK

U2 - 10.1016/j.earscirev.2020.103210

DO - 10.1016/j.earscirev.2020.103210

M3 - Article

VL - 211.2020

JO - Earth Science Reviews

JF - Earth Science Reviews

SN - 1872-6828

IS - December

M1 - 103210

ER -