2013
DOI: 10.5194/se-4-105-2013
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Post-processing scheme for modelling the lithospheric magnetic field

Abstract: Abstract. 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 generat… Show more

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Cited by 28 publications
(24 citation statements)
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“…Neither filtering of the data nor regularization is applied during the model estimation procedure, in contrast to other recent models of the high degree lithospheric field (Maus et al 2008;Lesur et al 2013;Olsen et al 2014). This helps highlight the differences between models constructed using only vector field data and models constructed using along-track differences of vector field data.…”
Section: I+1mentioning
confidence: 99%
See 1 more Smart Citation
“…Neither filtering of the data nor regularization is applied during the model estimation procedure, in contrast to other recent models of the high degree lithospheric field (Maus et al 2008;Lesur et al 2013;Olsen et al 2014). This helps highlight the differences between models constructed using only vector field data and models constructed using along-track differences of vector field data.…”
Section: I+1mentioning
confidence: 99%
“…Such models are the magnetic field (MF) model series, for example MF7 1 (Maus et al 2008), and the comprehensive model (CM) series, for example CM5 , the CHAMP, Ørsted and SAC-C model series of Earth's magnetic field (CHAOS), for example CHAOS-4 , and the GFZ reference internal magnetic model (GRIMM) series (e.g. Lesur et al 2013). Detailed discussion of recent progress in lithospheric field modelling using satellites can also be found in the reviews by Thébault et al (2010) and Olsen & Stolle (2012).…”
Section: Introductionmentioning
confidence: 99%
“…The lithospheric field also varies in time, but on time scales that are so much longer than the time span of our data set that we can neglect them (Hulot et al 2009). The maximum SH degree used for modelling the field of internal origin is 30, although a constant field covering all SH degrees from 20 to 60 (taken from Lesur et al 2013) is subtracted from the data so that only very small contributions from the lithospheric field remain unmodelled. Therefore the estimated Gauss coefficients from SH degrees 20 to 30 are effectively only a correction to those of the subtracted model.…”
Section: O D E L Pa R a M E T R I Z At I O Nmentioning
confidence: 99%
“…In addition, several issues surrounding SIVW should be explored, such as the optimal SH order m that delineates between the nominal and nuisance lithospheric fields, which could easily lead to better models. In addition, SIVW could be used to account for dayside bias when modeling the lithosphere, which would reflect the current best methods for crustal field modeling (Thomson and Lesur, 2007;Maus et al, 2007Maus et al, , 2008Lesur et al, 2008Lesur et al, , 2013Olsen et al, 2011). Finally, SIVW could be applied to high degree SV modeling by mitigating the bias due to the poor distribution of ground observatories.…”
Section: Discussionmentioning
confidence: 99%