Volume 10 Issue 4
Jan.  2021
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Daniel S. Coutts, William A. Matthews, Stephen M. Hubbard. Assessment of widely used methods to derive depositional ages from detrital zircon populations[J]. Geoscience Frontiers, 2019, 10(4): 1421-1435. doi: 10.1016/j.gsf.2018.11.002
Citation: Daniel S. Coutts, William A. Matthews, Stephen M. Hubbard. Assessment of widely used methods to derive depositional ages from detrital zircon populations[J]. Geoscience Frontiers, 2019, 10(4): 1421-1435. doi: 10.1016/j.gsf.2018.11.002

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

doi: 10.1016/j.gsf.2018.11.002

Funding for this research was provided by a NSERC Discovery Grant (No. RGPIN/341715-2013) to S. Hubbard and a Queen Elizabeth II scholarship from the University of Calgary to D. Coutts.

  • Received Date: 2018-05-14
  • Rev Recd Date: 2018-09-25
  • The calculation of a maximum depositional age (MDA) from a detrital zircon sample can provide insight into avariety 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 neardepositional-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 < n < 600). This maximizes the number of near-depositional-age grains that are dated. Ideally, a lowuncertainty (1%-2%, 2σ), large-n (n > 300) approach is used if the calculation of accurate MDAs are key to research goals. Other acquisition methods, such as high-to moderate-precision measurement methods (e.g., 1%-5%, 2σ) acquiring low-to moderate-n datasets (50 < n < 300), will typically calculate MDAs with larger residual error and higher variance between samples.
    In general, the most successful and accurate methods tested are:the youngest single grain (YSG), youngest detrital zircon (YDZ), and the weighted average of the youngest three grains (Y3Z). These methods, however, are liable to calculate MDAs younger than the true depositional age if derived from populations with abundant near-depositional ages, or from large-n datasets (n > 300). Additionally, they are most susceptible to producing erroneous MDAs due to contamination in the field or laboratory, or through disturbances of the youngest zircon's U-Pb systematics (e.g., lead loss). More conservative methods that still produce accurate MDAs and are less susceptible to contamination or lead loss include:youngest grain cluster at 1σ uncertainty (YGC 1σ), youngest grain cluster at 2σ uncertainty (YGC 2σ), and youngest statistical population (YSP). The ages calculated by these methods may be more useful and appealing when fitting calculated MDAs in to pre-existing chronostratigraphic frameworks, as they are less likely to be younger than the true depositional age. From the results of our numerical models we illustrate what geologic processes (i.e., tectonic or sedimentary) can be resolved using MDAs derived from strata of different ages.
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