Abstract
AbstractA commonly used family of statistical magnetic field models is based on a giant Gaussian process (GGP), which assumes each Gauss coefficient can be realized from an independent normal distribution. GGP models are capable of generating suites of plausible Gauss coefficients, allowing for palaeomagnetic data to be tested against the expected distribution arising from a time‐averaged geomagnetic field. However, existing GGP models do not simultaneously reproduce the distribution of field strength and palaeosecular variation estimates reported for the past 10 million years and tend to underpredict virtual geomagnetic pole (VGP) dispersion at high latitudes unless trade‐offs are made to the fit at lower latitudes. Here we introduce a new family of GGP models, BB18 and BB18.Z3 (the latter includes non‐zero‐mean zonal terms for spherical harmonic degrees 2 and 3). Our models are distinct from prior GGP models by simultaneously treating the axial dipole variance separately from higher degree terms, applying an odd‐even variance structure, and incorporating a covariance between certain Gauss coefficients. Covariance between Gauss coefficients, a property both expected from dynamo theory and observed in numerical dynamo simulations, has not previously been included in GGP models. Introducing covariance between certain Gauss coefficients inferred from an ensemble of “Earth‐like” dynamo simulations and predicted by theory yields a reduced misfit to VGP dispersion, allowing for GGP models which generate improved reproductions of the distribution of field strengths and palaeosecular variation observed for the last 10 million years.
Highlights
Palaeomagnetic statistical field models are descriptions of the time‐averaged magnetic field, typically presented as suites of spherical harmonic Gauss coefficients with assumed statistical properties
We introduce two new giant Gaussian process (GGP) models, BB18 and BB18.Z3, that yield improved fits to the PSV10 data set through the application of a prescribed covariance pattern inferred from dynamo simulations and theoretical considerations
The new GGP models presented in this study (BB18 and BB18.Z3) both yield improved fits to the virtual geomagnetic pole (VGP) dispersion estimates of PSV10 relative to existing GGP models, approaching what can be achieved with Model G‐style fits of Doubrovine et al (2019) while predicting field directions and intensities which cannot be done with Model G
Summary
Palaeomagnetic statistical field models are descriptions of the time‐averaged magnetic field, typically presented as suites of spherical harmonic Gauss coefficients with assumed statistical properties These models allow for straightforward determinations of the magnetic field and associated metrics, such as dispersion of magnetic directions or field strength distributions anywhere on the globe. The most common field models have previously assumed that the variation in Gauss coefficients can be described by a giant Gaussian process, where Gauss coefficients are normally distributed following a prescribed set of rules (e.g., Constable & Johnson, 1999; Constable & Parker, 1988; Quidelleur et al, 1994). Applications include assessing whether palaeomagnetic data from a given study record the expected amount of dispersion typical for the time‐averaged field (as estimated following, e.g., Cox, 1970) and estimating the degree of inclination shallowing recorded in a sedimentary record by examining the observed elongation of directions compared against the directional elongation predicted by TK03 (Tauxe & Kent, 2004)
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