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Correlation‐based modeling and separation of geomagnetic field components
Author(s) -
Holschneider Matthias,
Lesur Vincent,
Mauerberger Stefan,
Baerenzung Julien
Publication year - 2016
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1002/2015jb012629
Subject(s) - earth's magnetic field , computation , inversion (geology) , correlation , algorithm , computer science , bayesian probability , field (mathematics) , simple (philosophy) , a priori and a posteriori , approximate bayesian computation , separation (statistics) , geophysics , geology , magnetic field , mathematics , physics , artificial intelligence , machine learning , geometry , paleontology , philosophy , epistemology , quantum mechanics , structural basin , pure mathematics , inference
We introduce a technique for the modeling and separation of geomagnetic field components that is based on an analysis of their correlation structures alone. The inversion is based on a Bayesian formulation, which allows the computation of uncertainties. The technique allows the incorporation of complex measurement geometries like observatory data in a simple way. We show how our technique is linked to other well‐known inversion techniques. A case study based on observational data is given.