MI2021 - 08 Brendan Cych: Curvature to predict paleointensity bias
Channel: MagNetZ
Date: 18/02/2021

Using curvature to predict bias in paleointensity estimates: Accurate paleointensity without discarding specimens

Brendan Cych, University of California, San Diego, USA

Estimates of the ancient Earth’s magnetic field strength are commonly made by performing Thellier type experiments (Thellier & Thellier, 1959) on rock and pottery specimens. These experiments compare the natural remanent magnetization (NRM) of a specimen acquired from cooling in the Earth’s magnetic field to a thermoremanent magnetization (TRM) imparted to the specimen by cooling in a known lab field.Thellier type experiments make several assumptions about the magnetic grains in a sample which are frequently violated, leading to a biased paleointensity estimate for that specimen. To avoid a site level bias, paleomagnetists exclude data using a set of “selection criteria”: statistics describing various ways in which an experiment can fail. If these criteria exceed threshold values, then the specimen is excluded from the analysis. There is no universally accepted set of selection criteria, so paleointensity databases contain results which have been analyzed using different methodsWe present a new method of analyzing paleointensity data which assumes a bias in the paleointensity estimates recorded by some specimens. The method uses the Arai plot (Nagata & Arai, 1963) to predict the amount of bias for each specimen as a function of the curvature parameter of Paterson (2011), and can then predict the intensity for a specimen with zero curvature (i.e. a straight line on the Arai plot). Doing this enables us to get an unbiased estimate of the paleointensity, without excluding specimens from the analysis. This avoids having to determine a specific threshold value to use.Our method has several advantages over selection criteria. We use Bayesian statistics to calculate the curvature and paleointensity for each specimen. This allows us to propagate the uncertainties in these specimen level parameters up to the site level estimate. It also means that the uncertainty in our site level estimate shrinks as the number of specimens increase, leading to a workflow where specimens are measured until a desired level of tolerance is reached (normally achieved by around 5 or 6 specimens for data of reasonable quality).We apply our method to a collection of 479 specimens from 30 sites where the ancient field is known because the specimens are from a historical eruption or because they were given a TRM in the lab. Our method results in a similar level of accuracy and higher level of precision than the CCRIT set of criteria (Cromwell et al, 2015) and represents an improvement over other selection criteria.