Is there a link between geomagnetic reversal frequency and paleointensity? A Bayesian approach

Abstract

Over the last 25 years, several studies have tested for a link between geomagnetic field intensity and reversal frequency. However, despite a large increase in the number of absolute paleointensity determinations, and improved methods for obtaining such data, two competing models have arisen. Here we employ a new tool for objectively analyzing paleomagnetic time series to investigate the possibility of a link between reversal frequency and paleointensity. Transdimensional Markov chain Monte Carlo techniques are applied to a quality-filtered version of the global paleointensity (PINT) database for the last 202 Myr to model long-term paleointensity behavior. A large ensemble of models is sampled, from which a final representative mean model is extracted. The resulting paleointensity model confirms published conclusions that the single-silicate crystal method gives significantly different results from more conventional whole rock paleointensity methods; this makes it difficult to jointly model the two data types in the same analysis. When the much larger whole rock data set is considered, a stable paleointensity of 5.46 ± 0.28 × 10 22 A/m 2 for the last 202 Myr is consistent with the 95% confidence interval of the paleointensity model. Statistical tests indicate no significant correlation between reversal frequency and field intensity at the 0.05 level. However, this result is likely due to the characteristics of the PINT database rather than being a genuine, physically representative conclusion. Given the paucity of data and general state of the global paleointensity database, concerted efforts to increase the number of high-quality, well-dated paleointensity data are required before conclusions about a link between geomagnetic field intensity and reversal frequency can be confidently drawn.

Analysis of an Updated Paleointensity Database (QPI‐PINT) for 65–200 Ma: Implications for the Long‐Term History of Dipole Moment Through the Mesozoic

“…We started our analysis with an exhaustive literature search, which yielded 796 site mean paleointensity estimates from 75 individual studies, expressed as virtual or virtual axial dipole moments (collectively referred to as VDMs hereinafter; supporting information Table S1 and Figures and ). Compiled VDMs for the chosen time interval include 61 data points from six studies published since the release of 2012 Q PI ‐PINT database, used in the most recent analysis of paleointensity data for the Mesozoic (Ingham et al, ). The majority of the selected VDMs ( n = 586) are obtained using variants of the double‐heating Thellier method (Thellier & Thellier, ), whereas the remaining VDMs are obtained with the van Zijl method ( n = 2; van Zijl et al, , ), different modifications of the Wilson method ( n = 77; Wilson, ), variants of the Shaw method ( n = 179; Shaw, ; Tsunakawa et al, , Yamamoto et al, ), the microwave method ( n = 36; Hill & Shaw, ), or the multispecimen parallel differential method ( n = 15; Dekkers & Bohnel, ).…”

Mantle superplumes induce geomagnetic superchrons

“…Controversy surrounds the overall significance of the MDL, with some studies finding a general inverse correlation between paleointensity and reversal frequency (Channell et al, 1982;Tarduno and Cottrell, 2005), while other studies questioning whether such a correlation exists (Ingham et al, 2014). Nevertheless, the best available paleointensity data indicate a Jurassic median dipole moment of 29 ZAm 2 , compared to 78 ZAm 2 at present-day, ∼72 ZAm 2 at CNS onset, and ∼42 ZAm 2 for the long-term (0-140 Ma) time average (Tauxe et al, 2013).…”

Trans‐Dimensional Surface Reconstruction With Different Classes of Parameterization

“…Here we extend the Voronoi cell parameterization to Delaunay triangles with linear and cubic interpolants giving C 0 and C 1 continuous fields for 2‐D problems. These extensions complement other extensions to Voronoi cell parameterizations such as the Johnson‐Mehl tessellation (Belhadj et al, ) and have analogs in 1‐D trans‐dimensional parameterizations used in geophysical problems where “change points” are modelled with step functions (Ingham et al, ) or changes in gradient are modelled with piece wise linear functions (Hopcroft et al, ). We show that compared to the two alternatives, the Voronoi cell parameterization poorly recovers features in the inversion of smooth models and introduces multimodal posteriors that complicate the interpretation of uncertainties.…”