Correlation‐based modeling and separation of geomagnetic field components

Abstract 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.