Figure 7. Effect of distributing
100 paleomagnetic samples over a varying number of paleomagnetic sites
on the a) angular distance of the acquired paleopole to the reference
pole with all sites with n=7 (or 6, in case of Antarctica), and the b)
A95, c) K, and d) S of the acquired paleopole. Every
datapoint represents 1000 runs of a distribution, with error bars
indicating the 95% confidence interval of these 1000 runs.
5 Discussion and conclusions
We first evaluate whether measuring multiple samples per site to
constrain within-site scatter allows to objectively filter unreliable
VGPs. In our reinterpretation, we closed our ‘expert eye’ and included
sites that were excluded in the original studies because of subjective
criteria: for instance, our dataset contains sites with erratic
demagnetization behavior or very high intensities that were originally
interpreted as lightning struck. Our analysis illustrates that objective
statistical filters, such as a k-cutoff, a minimum number of samples per
site, or eliminating the farthest outliers per site, are incapable of
filtering these unreliable VGPs. The subjective judgement of the expert
thus determines which samples and sites are included in the calculation
of the paleopole (and makes it important that all data are made
available following the FAIR principles (Wilkinson et al., 2016)).
Our analysis next shows that for the three datasets with high
between-site scatter and low within-site scatter (i.e., Turkey,
Mongolia, and Antarctica), applying the within-site scatter-based
criteria leads to marginal increase in data clustering (i.e., an
increase in K and decrease in S). For the Norwegian dataset, which has
the rare combination of high within-site scatter and low between-site
scatter, these criteria even lead to significantly higher clustering of
the data. This lends support to using these data filters for PSV
studies.
But applying these filters when calculating paleopoles in all cases
leads to a decrease in pole precision (i.e., an increase in
A95). This is the result of the decrease in N that is
caused by applying data filters. Our experiments thus show that the
effect of between-site scatter on pole precision is far dominant over
within-site scatter. And because the accuracy of paleopoles also
decreases with decreasing N (Vaes et al., 2021), applying within-site
scatter filters systematically decreases pole accuracy as well as
precision (see also Figure 7).
Averaging multiple samples per site, without applying filters, does not
negatively influence pole accuracy or precision. In case only few sites
are available, or there is reason to question data quality, collecting
multiple samples per site, as recommended by Meert et al. (2021), may
still be a useful strategy. It may also be useful to demonstrate for a
selection of lava sites in a study that within-site precision (k )
is high, as a field test equivalent to a fold, reversal, or conglomerate
test. But, our analysis shows that within-site scatter does not
significantly influence paleopole accuracy or precision. The extra
efforts put into collecting multiple samples per site are thus more
effectively spent collecting more single-sample sites.
Acknowledgements
BV and DJJvH acknowledge NWO Vici grant 865.17.001 to DJJvH.
Data Availability Statement
All uninterpreted paleomagnetic data are publicly available on
Paleomagnetism.org for the Mongolia, Norway, and Turkey dataset and in
the MagIC database for the Antarctica dataset:
https://earthref.org/MagIC/17076. The Python code used for our
analysis will be made public on GitHub upon acceptance of this
manuscript.