Congratulations to Chenjian Fu for a successful MS thesis defense!

Congratulations to Chenjian Fu for successful defending his MS thesis, ‘Global Paleomagnetic Data Analysis: Improved Methods of Reconstructing Plate Motions Using Paleomagnetic Data‘.

Abstract

Paleomagnetic Apparent Polar Wander Paths (APWPs) are the principal means of describing plate motions through most of Earth history. However, there are limitations to paleomagnetic data such as the poorly-constrained longitudes of paleo-plates and the degrading quality and density of paleomagnetic data with increasing age. Yet comparing the spatio-temporal patterns and trends of APWPs between different tectonic plates is important for testing proposed paleogeographic reconstructions of past supercontinents. Similarity between paleomagnetic APWPs of different tectonic plates could indicate the plates were once part of a supercontinent. However, there is no clearly defined quantitative approach to determine the degree of similarity between APWPs. A new similarity measuring algorithm between two APWPs, that combines three separate difference metrics that assess both spatial separation of coeval poles, and similarities in the bearing and length of coeval segments, using a weighted linear summation, is proposed. Bootstrap tests are used to determine whether the differences between coeval poles and segments are significant for the given spatial uncertainties in pole positions. An additional Fit Quality metric is used to discriminate between low difference scores caused by comparing poorly constrained paths with large spatial uncertainties from those caused by a close fit between well-constrained paths. The individual and combined metrics are demonstrated using tests on synthetic pairs of APWPs with varying degrees of spatial and geometric difference. In a test on real paleomagnetic data, these metrics can quantify the effects of correction for inclination shallowing in sedimentary rocks on Gondwana and Laurussia’s 320–0 Ma APWPs. A Python package on GitHub is provided online, as open-source software, to allow automatic calculation of similarity scores. APWPs using 168 different methods are generated, and then the new APW path similarity measuring tool is applied to find the best APWP generating methods. Paleopole attributes are used to weigh their influence on mean poles, or to determine if they should be omitted for producing a ‘better’ mean pole. Different key attributes, that can be quantified, or their combination, are considered. In addition, when merging paleopoles to produce a smoothed mean path, choices are made not only about which data are included or excluded, but how data are combined. Moving averaging is used to combine data. The results indicate that our new Age Position Picking (APP) method (considering the whole age ranges of paleopoles) generates more reliable APWPs than the traditional Age Mean Picking (AMP) method (considering only middle points of age ranges) for making an APWP, when moving average is the core technique of the methodology. Additionally, weighting paleopoles, a traditional processing step for making a paleomagnetic APWP, is actually unnecessary. The APP method (without weighting paleopoles), which performs significantly better with modern (~120–0 Ma) paleomagnetic data than other methods, should be applied to older ‘deep-time’ datasets.