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Estimate the amount of grains on an aliquot

Source: R/calc_AliquotSize.R
calc_AliquotSize.Rd

Estimate the number of grains on an aliquot. Alternatively, the packing density of an aliquot is computed.

Usage

calc_AliquotSize(
  grain.size,
  sample.diameter,
  packing.density = 0.65,
  MC = TRUE,
  grains.counted,
  plot = TRUE,
  ...
)

Arguments

grain.size

numeric (required): mean grain size (microns) or a range of grain sizes from which the mean grain size is computed (e.g. c(100,200)).

sample.diameter

numeric (required): diameter (mm) of the targeted area on the sample carrier.

packing.density

numeric (with default): empirical value for mean packing density.
If packing.density = "Inf" a hexagonal structure on an infinite plane with a packing density of \(0.906\ldots\) is assumed.

MC

logical (optional): if TRUE the function performs a Monte Carlo simulation for estimating the amount of grains on the sample carrier and assumes random errors in grain size distribution and packing density. Requires a vector with min and max grain size for grain.size. For more information see details.

grains.counted

numeric (optional): grains counted on a sample carrier. If a non-zero positive integer is provided this function will calculate the packing density of the aliquot. If more than one value is provided the mean packing density and its standard deviation is calculated. Note that this overrides packing.density.

plot

logical (with default): plot output (TRUE/FALSE)

...

further arguments to pass (main, xlab, MC.iter).

Value

Returns a terminal output. In addition an RLum.Results object is returned containing the following element:

.$summary

data.frame summary of all relevant calculation results.

.$args

list used arguments

.$call

call the function call

.$MC

list results of the Monte Carlo simulation

The output should be accessed using the function get_RLum.

Details

This function can be used to either estimate the number of grains on an aliquot or to compute the packing density depending on the the arguments provided.

The following function is used to estimate the number of grains n:

$$n = (\pi*x^2)/(\pi*y^2)*d$$

where x is the radius of the aliquot size (microns), y is the mean radius of the mineral grains (mm) and d is the packing density (value between 0 and 1).

Packing density

The default value for packing.density is 0.65, which is the mean of empirical values determined by Heer et al. (2012) and unpublished data from the Cologne luminescence laboratory. If packing.density = "Inf" a maximum density of \(\pi/\sqrt12 = 0.9068\ldots\) is used. However, note that this value is not appropriate as the standard preparation procedure of aliquots resembles a PECC ("Packing Equal Circles in a Circle") problem where the maximum packing density is asymptotic to about 0.87.

Monte Carlo simulation

The number of grains on an aliquot can be estimated by Monte Carlo simulation when setting MC = TRUE. Each of the parameters necessary to calculate n (x, y, d) are assumed to be normally distributed with means \(\mu_x, \mu_y, \mu_d\) and standard deviations \(\sigma_x, \sigma_y, \sigma_d\).

For the mean grain size random samples are taken first from \(N(\mu_y, \sigma_y)\), where \(\mu_y = mean.grain.size\) and \(\sigma_y = (max.grain.size-min.grain.size)/4\) so that 95\ grains are within the provided the grain size range. This effectively takes into account that after sieving the sample there is still a small chance of having grains smaller or larger than the used mesh sizes. For each random sample the mean grain size is calculated, from which random subsamples are drawn for the Monte Carlo simulation.

The packing density is assumed to be normally distributed with an empirically determined \(\mu = 0.65\) (or provided value) and \(\sigma = 0.18\). The normal distribution is truncated at d = 0.87 as this is approximately the maximum packing density that can be achieved in PECC problem.

The sample diameter has \(\mu = sample.diameter\) and \(\sigma = 0.2\) to take into account variations in sample disc preparation (i.e. applying silicon spray to the disc). A lower truncation point at x = 0.5 is used, which assumes that aliquots with smaller sample diameters of 0.5 mm are discarded. Likewise, the normal distribution is truncated at 9.8 mm, which is the diameter of the sample disc.

For each random sample drawn from the normal distributions the amount of grains on the aliquot is calculated. By default, 10^5 iterations are used, but can be reduced/increased with MC.iter (see ...). The results are visualised in a bar- and boxplot together with a statistical summary.

Function version

0.31

How to cite

Burow, C., 2024. calc_AliquotSize(): Estimate the amount of grains on an aliquot. Function version 0.31. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.26. https://r-lum.github.io/Luminescence/

References

Duller, G.A.T., 2008. Single-grain optical dating of Quaternary sediments: why aliquot size matters in luminescence dating. Boreas 37, 589-612.

Heer, A.J., Adamiec, G., Moska, P., 2012. How many grains are there on a single aliquot?. Ancient TL 30, 9-16.

Further reading

Chang, H.-C., Wang, L.-C., 2010. A simple proof of Thue's Theorem on Circle Packing. https://arxiv.org/pdf/1009.4322v1, 2013-09-13.

Graham, R.L., Lubachevsky, B.D., Nurmela, K.J., Oestergard, P.R.J., 1998. Dense packings of congruent circles in a circle. Discrete Mathematics 181, 139-154.

Huang, W., Ye, T., 2011. Global optimization method for finding dense packings of equal circles in a circle. European Journal of Operational Research 210, 474-481.

Author

Christoph Burow, University of Cologne (Germany) , RLum Developer Team

Examples


## Estimate the amount of grains on a small aliquot
calc_AliquotSize(grain.size = c(100,150), sample.diameter = 1, MC.iter = 100)
#> 
#>  [calc_AliquotSize]
#> 
#>  ---------------------------------------------------------
#>  mean grain size (microns)  : 125
#>  sample diameter (mm)       : 1
#>  packing density            : 0.65
#>  number of grains           : 42
#> 
#>  --------------- Monte Carlo Estimates -------------------
#>  number of iterations (n)     : 100
#>  median                       : 40
#>  mean                         : 42
#>  standard deviation (mean)    : 20
#>  standard error (mean)        : 2
#>  95% CI from t-test (mean)    : 38 - 46
#>  standard error from CI (mean): 2
#>  ---------------------------------------------------------


## Calculate the mean packing density of large aliquots
calc_AliquotSize(grain.size = c(100,200), sample.diameter = 8,
                 grains.counted = c(2525,2312,2880), MC.iter = 100)
#> 
#> Monte Carlo simulation is only available for estimating the amount of grains on the sample disc. Automatically set to FALSE.
#> 
#>  [calc_AliquotSize]
#> 
#>  ---------------------------------------------------------
#>  mean grain size (microns)  : 150
#>  sample diameter (mm)       : 8
#>  mean counted grains        : 2572
#>  mean packing density       : 0.904
#>  standard deviation         : 0.101
#>  ---------------------------------------------------------