Assessment of widely used methods to derive depositional ages from detrital zircon populations

• Published in: Di xue qian yuan. 2019-07, Vol.10 (4), p.1421-1435
• Main Authors: Coutts, Daniel S., Matthews, William A., Hubbard, Stephen M.
• Format: Article
• Language: English
• Subjects: Accuracy; Age; Basin chronology; Clusters; Computer simulation; Contamination; Datasets; Detrital zircon; Errors; Geochronology; Geological time; Grains; Impact analysis; LA-ICP-MS; Lead; Maximum depositional age; Measurement methods; Methods; Population; Population (statistical); Production methods; Samples; Standard deviation; Statistical methods; Tectonics; Uncertainty; Zircon
• ISSN: 1674-9871; 2588-9192
• DOI: 10.1016/j.gsf.2018.11.002

The calculation of a maximum depositional age (MDA) from a detrital zircon sample can provide insight into a variety of geological problems. However, the impact of sample size and calculation method on the accuracy of a resulting MDA has not been evaluated. We use large populations of synthetic zircon dates (N ≈ 25,000) to analyze the impact of varying sample size (n), measurement uncertainty, and the abundance of near-depositional-age zircons on the accuracy and uncertainty of 9 commonly used MDA calculation methods. Furthermore, a new method, the youngest statistical population is tested. For each method, 500 samples of n synthetic dates were drawn from the parent population and MDAs were calculated. The mean and standard deviation of each method over the 500 trials at each n-value (50–1000, in increments of 50) were compared to the known depositional age of the synthetic population and used to compare the methods quantitatively in two simulation scenarios. The first simulation scenario varied the proportion of near-depositional-age grains in the synthetic population. The second scenario varied the uncertainty of the dates used to calculate the MDAs. Increasing sample size initially decreased the mean residual error and standard deviation calculated by each method. At higher n-values (>∼300 grains), calculated MDAs changed more slowly and the mean residual error increased or decreased depending on the method used. Increasing the proportion of near-depositional-age grains and lowering measurement uncertainty decreased the number of measurements required for the calculated MDAs to stabilize and decreased the standard deviation in calculated MDAs of the 500 samples. Results of the two simulation scenarios show that the most successful way to increase the accuracy of a calculated MDA is by acquiring a large number of low-uncertainty measurements (300