Heavy‐Mineral Grain Counting: Counting Techniques, Error Estimation, and the Number of Grains to be Counted

Abstract

AbstractHeavy‐mineral assemblages of sediments and sedimentary rocks record information regarding provenance, including the source rocks involved, tectonic setting, climatic conditions, and modifications from source to sink. Drawing conclusions on provenance and provenance changes requires robust quantification of individual heavy‐mineral species contents, including error estimates. Nevertheless, it is common practice to count sub‐quantities of grains from aliquots and not considering the bias introduced by (a) counting similar numbers of grains from aliquots containing different total numbers of grains, and (b) using variable counting methods. Consequently, reported heavy‐mineral contents estimated from counting sub‐quantities are affected by errors of unknown extent, making it infeasible to determine whether intra‐ or intersample variations are statistically significant. Based on 65 heavy‐mineral aliquots of variable grain size, mineral species contents, total number of grains, and known composition determined by counting all grains (n = 80,393), here >31 million countings of heavy‐mineral sub‐quantities are simulated using (a) ribbon counting with varying ribbon size, ribbon position, ribbon orientation, total number of counts, and ways of aggregating counts from multiple ribbons, and (b) a newly proposed counting technique called cluster counting. I show that (a) error estimation for a specific aliquot requires a finite population correction; (b) compared to adjacent ribbons, aggregating counts of spatially distant ribbons reduces the error; (c) cluster counting further reduces the error, showing the best fit with theory; and (d) the Wilson score interval enables error calculation as well as the number of grains to be counted to achieve an operator‐specific aim.