We investigated how the noise in satellite magnetic data affects magnetic lithospheric field models derived from these data in the special case where this noise is correlated along satellite orbit tracks. For this we describe the satellite data noise as a perturbation magnetic field scaled independently for each orbit, where the scaling factor is a random variable, normally distributed with zero mean. Under this assumption, we have been able to derive a model for errors in lithospheric models generated by the correlated satellite data noise. Unless the perturbation field is known, estimating the noise in the lithospheric field model is a non-linear inverse problem. We therefore proposed an iterative post-processing technique to estimate both the lithospheric field model and its associated noise model. The technique has been successfully applied to derive a lithospheric field model from CHAMP satellite data up to spherical harmonic degree 120. The model is in agreement with other existing models. The technique can, in principle, be extended to all sorts of potential field data with "along-track" correlated errors.

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nonlinear inverse iterative postprocessing technique correlated satellite data magnetic lithospheric field models random variable spherical harmonic degree potential field data

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