A simple test for the presence of multidomain behavior during paleointensity experiments

Abstract

[1] Detecting and excluding non-ideal behavior during paleointensity experiments is critical to asserting the reliability of data. Our knowledge of detecting non-ideal behavior, in particular the influence of multidomain (MD) grains, has expanded considerably over the past decade and experimental procedures now commonly incorporate checks to detect the effects of MD behavior. However, many older studies were carried out before these checks were devised and provide no quantifiable means of testing for the presence of MD grains. An estimated one third of all entries in the most recent paleointensity database do not include some form of check for MD behavior. The reliability of these results is therefore questionable and can only hinder efforts to understand the evolution of the geomagnetic field and the geodynamo. I propose a simple phenomenological check that can be applied to previous studies, provided that the raw data are available, that will allow the exclusion of MD behavior and provide a means of identifying reliable data. The check is a quantification of the curvature, k, of data points on an Arai plot, a feature commonly associated with MD behavior. Analysis of paleointensity data from samples with known grain size indicates that this new parameter is significantly correlated with grain size and with the accuracy of the paleointensity estimates made from both limbs of the curved data. Analysis of 181 samples from five historical data sets indicates that k is significantly correlated with experimentally obtained MD and alteration check parameters, and the accuracy of the paleointensity estimate. A threshold selection value of k ≤ 0.164 can be defined using the samples with known grain sizes. Applying this cut-off value, combined with a threshold on the quality of the circle fit and a commonly used alteration check, to the historical data yields an accurate result with low scatter. When compared with previously published selection criteria that incorporate experimental checks for non-ideal behavior, the result of applying the criteria proposed here is an improvement. The application of these three criteria rejects over 65% of all inaccurate results and has the highest concentration of accurate results when compared with the other criteria sets tested. Other selection criteria can be subsequently used to improve on this result. While modern studies should always include experimental checks to identify MD behavior, this new criterion will provide a useful tool for future studies and, importantly, a method to assess the reliability of previously published data.

Paleointensity estimates from historic and modern Hawaiian lava flows using glassy basalt as a primary source material

“…In their evaluation of Arai plot curvature and historic paleointensity estimates, Paterson (2011) observed that specimens with jkj < 0:164 yielded more accurate results with lower scatter. In Fig.…”

“…MAD free (Kirschvink, 1980) (maximum angle of deviation) is a measure of scatter about the best-fit line through the NRM steps in an Arai plot. We also calculated the curvature statistic,k, of Paterson (2011). Curvature is calculated by using a least squares approach to fit the data in an Arai plot with a best-fit circle of the form ðx À aÞ 2 þ ðy À bÞ 2 ¼ r 2 (Taubin, 1991;Chernov and Lesort, 2005), where x is the TRM gained and y is the NRM remaining.k is the inverse of the calculated radius, 1=r.…”

Paleointensity determination of Late Cretaceous basalts in northwest South Korea: implications for low and stable paleofield strength in the Late Cretaceous

“…There are a number of parameters to describe the behavior of experimental paleointensity data (e.g., Coe et al, 1978;Kirschvink, 1980;Leonhardt et al, 2000;Tauxe and Staudigel, 2004;Paterson, 2011;Yu, 2012). Of these, seven paleointensity selection criteria are used in this study (Table 1) to produce reliable paleointensity estimates.…”

Recent Advances in Chinese Archeomagnetism

“…The definition of each parameter is described in Cromwell et al (2015) while the detailed explanation can be found in Shaar and Tauxe (2013) and Paterson et al (2014) and references therein. We only provide a brief reminder of each statistics here: β is the normalized standard deviation of the slope of selected data points; DANG is the angle of the best-fit line deviated from the origin; MAD free is the unanchored maximum angular deviation of selected NRM data points; FRAC is the fraction of remanence used for calculating the paleointensity; SCAT is a Boolean that defines the allowed degree of scatter of the selected data points (including pTRM checks); | − → k ′ | is the absolute value of curvature of the data points used for determining the best-fit line (Paterson, 2011;Paterson et al, 2015); Gap Max is the maximum length of the normalized vector differences between consecutive NRM steps along the chosen segment; N min is the minimum number of accepted specimens; σ is the one-sigma standard deviation of site-mean intensity.…”