How to calculate the dose rate to rock slices in a cobble?
by Svenja Riedesel (March 30, 2021)
Cobbles are a great alternative to traditional grains for luminescence dating. The use of cobbles simplifies estimations of water content changes over geological time scales and it enables investigations of bleaching profiles into the cobble, helping towards assessing the degree of signal resetting during sunlight exposure. However, calculations of beta and gamma dose rates of cobbles are not straight forward. The function calc_CobbleDoseRate()
available from the R package Luminescence enables user friendly calculations of beta and gamma dose rate profiles through the cobble based on the results by Riedesel and Autzen (2020).
Example
Input data
The function calc_CobbleDoseRate()
requires a user defined input (as a data.frame
), which contains information on the cut rock slices, mineral used (quartz or feldspar) radionuclide information, grain size and water content. An example data set is available. To see the example data set, simply load the luminescence package, import and view the example data set:
library(Luminescence)
data("ExampleData.CobbleData", envir = environment())
View(ExampleData.CobbleData)
Distance | DistanceError | Thickness | ThicknessError | Mineral | Cobble_K | C_K_SE | Cobble_Th | Cobble_Th_SE | Cobble_U | Cobble_U_SE | GrainSize | Density | CobbleDiameter | Sed_K | Sed_K_SE | Sed_Th | Sed_Th_SE | Sed_U | Sed_U_SE | GrainSize_Sed | Density_Sed | WaterContent | WaterContent_SE |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.0 | 0.05 | 0.4 | 0.05 | FS | 7.51 | 0.07 | 15.78 | 0.32 | 4.88 | 0.12 | 500 | 2.7 | 6 | 2.13 | 0.07 | 9.76 | 0.32 | 2.24 | 0.12 | 60 | 1.6 | 45 | 10 |
0.4 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
0.8 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
1.2 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
1.6 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
2.0 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
3.0 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
5.0 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
10.0 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
20.0 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
30.0 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
40.0 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
50.0 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
59.4 | 0.05 | 0.4 | 0.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
The example data set then shows a table with headers and all required columns. Headers for these columns to be used with your own data are also provided under the Help option of the function calc_CobbleDoseRate()
in RStudio.
Calculations
The function calc_CobbleDoseRate()
performs a set of calculation which includes:
- Dose rate conversions: Therefore, the function requires the user to define the set of dose rate conversion factors which should be used. The user can choose between conversion factors by Adamiec and Aitken (1998), Guérin et al. (2011), Liritzis et al. (2013) or Cresswell et al. (2018). For these calculations the function
calc_CobbleDoseRate()
links to the function `convert_Concentration2DoseRate´. - Dose rate correction for grain size: A grain size correction is necessary for the calculations. Here the function uses the correction factors by Guérin et al. (2012). The function automatically uses these grain size correction factors. Please remember that the grain size correction factors differ for quartz and feldspar. Thus, it is important that you provide the correct mineral in your data import table. The
ExampleData.CobbleData
is for feldspars (see “FS” input in column 5 of theExampleData.CobbleData
set). - Scaling of gamma dose rate for cobble diameter: Riedesel and Autzen (2020) showed that the gamma dose rate by the cobble itself is dependent on the size of the cobble. The function
calc_CobbleDoseRate()
assumes that the cobble is spherical and applies the relationship between gamma dose rate attenuation factor and diameter of the cobble presented by Riedesel and Autzen (2020). - Calculation of dose rate profiles: The dose rate profiles into the cobble are calculated for the cobble itself and the dose rate contribution from the adjacent sediment following the dose rate attenuation coefficients presented by Riedesel and Autzen (2020). Let’s now use the example data set with the latest available dose rate conversion factors by Cresswell et al. (2018) for the calculations and run the function:
results <-
calc_CobbleDoseRate(input = ExampleData.CobbleData, conversion = "Cresswelletal2018")
results$DataIndividual
Distance. | K Beta cobble | SE | Th Beta cobble | SE | U Beta cobble | SE | K Gamma cobble | SE | Th Gamma cobble | SE | U Gamma cobble | SE | K Beta sed. | SE | Th Beta sed. | SE | U Beta sed. | SE | K Gamma sed. | SE | Th Gamma sed. | SE | U Gamma sed. | SE |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.0 | 3.869142 | 0.1350169 | 0.2310453 | 0.0111680 | 0.3894845 | 0.0160343 | 0.2282634 | 0.0055075 | 0.0946398 | 0.0026525 | 0.0670570 | 0.0021322 | 0.2458662 | 0.0241495 | 0.0287979 | 0.0029235 | 0.0384017 | 0.0040537 | 0.2265731 | 0.0077908 | 0.2045001 | 0.0071415 | 0.1077683 | 0.0058429 |
0.4 | 4.924099 | 0.0706773 | 0.2786937 | 0.0111074 | 0.4767130 | 0.0139992 | 0.2809792 | 0.0051190 | 0.1164962 | 0.0026954 | 0.0825433 | 0.0022768 | 0.0544230 | 0.0053455 | 0.0099893 | 0.0007847 | 0.0127364 | 0.0010755 | 0.2256120 | 0.0077577 | 0.2036326 | 0.0071112 | 0.1073112 | 0.0058182 |
0.8 | 5.157616 | 0.0683978 | 0.2941541 | 0.0116267 | 0.5031584 | 0.0145500 | 0.3163505 | 0.0051372 | 0.1311614 | 0.0028348 | 0.0929343 | 0.0024458 | 0.0120467 | 0.0011832 | 0.0038865 | 0.0003053 | 0.0049553 | 0.0004184 | 0.2246654 | 0.0077252 | 0.2027783 | 0.0070814 | 0.1068609 | 0.0057937 |
1.2 | 5.209306 | 0.0688193 | 0.3001693 | 0.0118511 | 0.5134475 | 0.0148163 | 0.3400840 | 0.0052770 | 0.1410015 | 0.0029717 | 0.0999065 | 0.0025854 | 0.0026666 | 0.0002619 | 0.0015121 | 0.0001188 | 0.0019280 | 0.0001628 | 0.2237332 | 0.0076931 | 0.2019369 | 0.0070520 | 0.1064175 | 0.0057697 |
1.6 | 5.220747 | 0.0689577 | 0.3025096 | 0.0119415 | 0.5174506 | 0.0149273 | 0.3560086 | 0.0054242 | 0.1476040 | 0.0030805 | 0.1045847 | 0.0026890 | 0.0005902 | 0.0000580 | 0.0005883 | 0.0000462 | 0.0007501 | 0.0000633 | 0.2228153 | 0.0076615 | 0.2011084 | 0.0070231 | 0.1059809 | 0.0057460 |
2.0 | 5.223280 | 0.0689905 | 0.3034201 | 0.0119771 | 0.5190081 | 0.0149715 | 0.3666938 | 0.0055362 | 0.1520341 | 0.0031576 | 0.1077237 | 0.0027609 | 0.0001307 | 0.0000111 | 0.0002289 | 0.0000163 | 0.0002918 | 0.0000227 | 0.2219117 | 0.0076304 | 0.2002928 | 0.0069945 | 0.1055511 | 0.0057227 |
3.0 | 5.223983 | 0.0689998 | 0.3039452 | 0.0119978 | 0.5199063 | 0.0149973 | 0.3804483 | 0.0057251 | 0.1577369 | 0.0032704 | 0.1117643 | 0.0028612 | 0.0000030 | 0.0000003 | 0.0000216 | 0.0000017 | 0.0000276 | 0.0000023 | 0.2197146 | 0.0075549 | 0.1983097 | 0.0069253 | 0.1045061 | 0.0056661 |
5.0 | 5.224000 | 0.0690000 | 0.3039995 | 0.0120000 | 0.5199992 | 0.0150000 | 0.3873914 | 0.0058255 | 0.1606155 | 0.0033288 | 0.1138040 | 0.0029127 | 0.0000000 | 0.0000000 | 0.0000002 | 0.0000000 | 0.0000002 | 0.0000000 | 0.2155832 | 0.0074128 | 0.1945809 | 0.0067951 | 0.1025410 | 0.0055595 |
10.0 | 5.224000 | 0.0690000 | 0.3040000 | 0.0120000 | 0.5200000 | 0.0150000 | 0.3884768 | 0.0058418 | 0.1610656 | 0.0033381 | 0.1141229 | 0.0029209 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.2067510 | 0.0071091 | 0.1866091 | 0.0065166 | 0.0983400 | 0.0053317 |
20.0 | 5.224000 | 0.0690000 | 0.3040000 | 0.0120000 | 0.5200000 | 0.0150000 | 0.3884843 | 0.0058419 | 0.1610687 | 0.0033382 | 0.1141251 | 0.0029209 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.1952266 | 0.0067127 | 0.1762074 | 0.0061533 | 0.0928585 | 0.0050345 |
30.0 | 5.224000 | 0.0690000 | 0.3040000 | 0.0120000 | 0.5200000 | 0.0150000 | 0.3884843 | 0.0058419 | 0.1610687 | 0.0033382 | 0.1141251 | 0.0029209 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.1915373 | 0.0065858 | 0.1728775 | 0.0060370 | 0.0911037 | 0.0049394 |
40.0 | 5.224000 | 0.0690000 | 0.3040000 | 0.0120000 | 0.5200000 | 0.0150000 | 0.3884843 | 0.0058419 | 0.1610687 | 0.0033382 | 0.1141251 | 0.0029209 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.1955351 | 0.0067233 | 0.1764858 | 0.0061630 | 0.0930052 | 0.0050425 |
50.0 | 5.224000 | 0.0690000 | 0.3040000 | 0.0120000 | 0.5200000 | 0.0150000 | 0.3884732 | 0.0058417 | 0.1610641 | 0.0033381 | 0.1141218 | 0.0029209 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.2073804 | 0.0071307 | 0.1871772 | 0.0065365 | 0.0986394 | 0.0053480 |
59.4 | 4.586565 | 0.0893366 | 0.2634290 | 0.0106856 | 0.4506023 | 0.0137095 | 0.2572419 | 0.0050261 | 0.1066545 | 0.0025800 | 0.0755700 | 0.0021517 | 0.1156753 | 0.0125385 | 0.0160148 | 0.0012587 | 0.0204189 | 0.0017571 | 0.2260907 | 0.0077741 | 0.2040647 | 0.0071263 | 0.1075389 | 0.0058305 |
Results
Results of the calculations achieved using the function calc_CobbleDoseRate()
are provided as RLum Results. The RLum Results contain tables, which include (1) the input data, (2) the beta and gamma dose rates for each cobble slices delivered by the cobble itself or the adjacent sediment and (3) the total beta and total gamma dose rates delivered by the cobble and the sediment added for each rock slice of the cobble.
To view the respective RLum Results table type results$input
, results$DataIndividual
or results$DataComponent
in the R console:
And now?
These results can be used to calculated total dose rates, for example by using the function use_DRAC()
which also available in the R package Luminescence.
Here you are able to insert your user defined beta and gamma dose rates.
Should you be interested in your alpha dose rate, you can either use the function use_DRAC()
or the function convert_Concentration2DoseRate()
. If your cobble is located in a complex stratigraphy, where multiple sedimentary layers might influence your gamma dose rate the R function scale_GammaDose()
might be useful for you.
References
Adamiec, G., Aitken, M.J., 1998. Dose-rate conversion factors: Update. Ancient TL 16, 37-46.
Cresswell., A.J., Carter, J., Sanderson, D.C.W., 2018. Dose rate conversion parameters: Assessment of nuclear data. Radiation Measurements 120, 195-201.
Guérin, G., Mercier, N., Adamiec, G., 2011. Dose-rate conversion factors: update. Ancient TL, 29, 5-8.
Guérin, G., Mercier, N., Nathan, R., Adamiec, G., Lefrais, Y., 2012. On the use of the infinite matrix assumption and associated concepts: A critical review. Radiation Measurements 47, 778-785.
Liritzis, I., Stamoulis, K., Papachristodoulou, C., Ioannides, K., 2013. A re-evaluation of radiation dose-rate conversion factors. Mediterranean Archaeology and Archaeometry 13, 1-15.
Riedesel, S., Autzen, M., 2020. Beta and gamma dose rate attenuation in rocks and sediment. Radiation Measurements 133, 106295.