The influence of correlated crustal signals in modelling the main geomagnetic field

SUMMARY Algorithms used in geomagnetic main‐field modelling have for the most part treated the noise in the field measurements as if it were white. A major component of the noise consists of the field due to magnetization in the crust and it has been realized for some time that such signals are highly correlated at satellite altitude. Hence approximation by white noise, while of undoubted utility, is of unknown validity. Langel, Estes & Sabaka (1989) were the first to evaluate the influence of correlations in the crustal magnetic field on main‐field models. In this paper we study two plausible statistical models for the crustal magnetization described by Jackson (1994), in which the magnetization is a realization of a stationary, isotropic, random process. At a typical satellite altitude the associated fields exhibit significant correlation over ranges as great as 15° or more, which introduces off‐diagonal elements into the covariance matrix, elements that have usually been neglected in modelling procedures. Dealing with a full covariance matrix for a large data set would present a formidable computational challenge, but fortunately most of the entries in the covariance matrix are so small that they can be replaced by zeros. The resultant matrix comprises only about 3 per cent non‐zero entries and thus we can take advantage of efficient sparse matrix techniques to solve the numerical system. We construct several main‐field models based on vertical‐component data from a selected 5° by 5° data set derived from the Magsat mission. Models with and without off‐diagonal terms are compared. For one of the two Jackson crustal models, k 3 , we find significant changes in the main‐field coefficients, with maximum discrepancies near degree 11 of about 27 per cent. The second crustal spectrum gives rise to much smaller effects for the data set used here, because the correlation lengths are typically shorter than the data spacing. k 4 also significantly underpredicts the observed magnetic spectrum around degree 15. We conclude that there is no difficulty in computing mainfield models that include off‐diagonal terms in the covariance matrix when sparse matrix techniques are employed; we find that there may be important effects in the computed models, particularly if we wish to make full use of dense data sets. Until a definitive crustal field spectrum has been determined, the precise size of the effect remains uncertain. Obtaining such a statistical model should be a high priority in preparation for the analysis of future low‐noise satellite data.