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    A comprehensive model for the kyr and Myr timescales of Earth's axial magnetic dipole field

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    Author
    Morzfeld, Matthias
    Buffett, Bruce A.
    Affiliation
    Univ Arizona, Dept Math
    Issue Date
    2019-07-08
    
    Metadata
    Show full item record
    Publisher
    COPERNICUS GESELLSCHAFT MBH
    Citation
    Morzfeld, M., & Buffett, B. A. (2019). A comprehensive model for the kyr and Myr timescales of Earth's axial magnetic dipole field. Nonlinear Processes in Geophysics, 26(3), 123-142.
    Journal
    NONLINEAR PROCESSES IN GEOPHYSICS
    Rights
    © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    We consider a stochastic differential equation model for Earth's axial magnetic dipole field. Our goal is to estimate the model's parameters using diverse and independent data sources that had previously been treated separately, so that the model is a valid representation of an expanded paleomagnetic record on kyr to Myr timescales. We formulate the estimation problem within the Bayesian framework and define a feature-based posterior distribution that describes probabilities of model parameters given a set of features derived from the data. Numerically, we use Markov chain Monte Carlo (MCMC) to obtain a sample-based representation of the posterior distribution. The Bayesian problem formulation and its MCMC solution allow us to study the model's limitations and remaining posterior uncertainties. Another important aspect of our overall approach is that it reveals inconsistencies between model and data or within the various data sets. Identifying these shortcomings is a first and necessary step towards building more sophisticated models or towards resolving inconsistencies within the data. The stochastic model we derive represents selected aspects of the long-term behavior of the geomagnetic dipole field with limitations and errors that are well defined. We believe that such a model is useful (besides its limitations) for hypothesis testing and give a few examples of how the model can be used in this context.
    Note
    Open access article
    ISSN
    1023-5809
    DOI
    10.5194/npg-26-123-2019
    Version
    Final published version
    Sponsors
    National Science Foundation [EAR-164464]; Alfred P. Sloan Foundation
    ae974a485f413a2113503eed53cd6c53
    10.5194/npg-26-123-2019
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    UA Faculty Publications

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