An optimization method for paleomagnetic Euler pole analysis. (September 2022)

• Record Type: Journal Article
• Authors: Gallo, Leandro C.; Sapienza, Facundo; Domeier, Mathew
• Is Part Of: Computers & geosciences. Volume 166(2022)
• Publication Date: 2022-09
• Subjects: Paleomagnetism -- Euler pole -- Apparent polar wander path -- Optimization; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2022.105150
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Owing to the axial symmetry of the Earth's magnetic field, paleomagnetic data only directly record the latitudinal and azimuthal positions of crustal blocks in the past, and paleolongitude cannot be constrained. An ability to overcome this obstacle is thus of fundamental importance to paleogeographic reconstruction. Paleomagnetic Euler pole (PEP) analysis presents a unique means to recover such information, but prior implementations of the PEP method have incorporated subjective decisions into its execution, undercutting its fidelity and rigor. Here we present an optimization approach to PEP analysis that addresses some of these deficiencies—namely the objective identification of change-points and small-circle arcs that together approximate an apparent polar wander path. We elaborate on our novel methodology and conduct some experiments with synthetic data to demonstrate its performance. We furthermore present implementations of our methods both as adaptable, stand-alone scripts in Python and as a streamlined interactive workflow that can be operated through a web browser. Highlights: PEP analysis presents a unique means to recover paleolongitude in deep time. Prior implementation have incorporated subjective decisions undercutting its fidelity. We present an unsupervised learning method to conduct PEP analysis. A graph-based representation of an APWP allows the inference of kinematic changes.