The global continent-ocean magnetization contrast

Counil, J. L.; Cohen, Y.; Achache, J.

doi:10.1016/0012-821x(91)90172-e

Cited by 48 publications

(31 citation statements)

References 28 publications

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“…Thus, the amplitude of DBe provides a reliable estimate of the amplitude of Be for models based on scalar data. Let us now consider a reference field model generated by Gauss coefficients up to degree and order 30 to avoid any significant truncation effect (Counil et al, 1991). This model, referenced to hereafter as MODO, is built by putting together the 1975's IGRF model up to degree and order 13 with the spherical harmonic expansion of the crustal anomalies derived from Magsat data for the degrees 14 to 30 (Hamoudi et al, 1995).…”

Section: Estimating the Noise Level In Pogs Datamentioning

confidence: 99%

Error Analysis of Total Field Models Derived from POGS Data

Ultré-Guérard

Achache

1997

J. geomagn. geoelec

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Scalar magnetic data from the POGS satellite are analysed for the 1991-1993 period. Models of the main geomagnetic field are computed using an iterative least-squares method. Vector components of the field derived from these models display large errors known as the Backus effect. A method for estimating the noise level in global data in the form of an equivalent gaussian noise is introduced and POGS data are shown to have a standard deviation of about a = 45 nT. Despite this high noise level, it is shown that POGS data can be used to improve models derived from observatory vector data. It is also shown that the Backus effect can be significantly reduced by using some accurate vector ground-based measurements of the main field at observatories located near the equator. Monthly models are computed using both POGS and observatory data for which monthly mean values are available at POGS period. Local corrections applied to observatory measurements are computed by comparing observatory monthly mean values with Magsat models. The results are shown to be improved when these corrections are applied to the observatory data, confirming that they correspond essentially to stationary short-wavelength crustal anomalies.

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Section: Estimating the Noise Level In Pogs Datamentioning

confidence: 99%

Error Analysis of Total Field Models Derived from POGS Data

Ultré-Guérard

Achache

1997

J. geomagn. geoelec

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“…The source magnetization may be remanent or induced (e.g. Counil et al 1991;Dyment & Arkani-Hamed 1998;Maus & Haak 2002). Magnetization contrasts can be modified or caused by heat flow anomalies (e.g.…”

Section: Introductionmentioning

confidence: 99%

A statistical spatial power spectrum of the Earth's lithospheric magnetic field

Thébault¹,

Vervelidou

2015

Geophysical Journal International

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S U M M A R YThe magnetic field of the Earth's lithosphere arises from rock magnetization contrasts that were shaped over geological times. The field can be described mathematically in spherical harmonics or with distributions of magnetization. We exploit this dual representation and assume that the lithospheric field is induced by spatially varying susceptibility values within a shell of constant thickness. By introducing a statistical assumption about the power spectrum of the susceptibility, we then derive a statistical expression for the spatial power spectrum of the crustal magnetic field for the spatial scales ranging from 60 to 2500 km. This expression depends on the mean induced magnetization, the thickness of the shell, and a power law exponent for the power spectrum of the susceptibility. We test the relevance of this form with a misfit analysis to the observational NGDC-720 lithospheric magnetic field model power spectrum. This allows us to estimate a mean global apparent induced magnetization value between 0.3 and 0.6 A m −1 , a mean magnetic crustal thickness value between 23 and 30 km, and a root mean square for the field value between 190 and 205 nT at 95 per cent. These estimates are in good agreement with independent models of the crustal magnetization and of the seismic crustal thickness. We carry out the same analysis in the continental and oceanic domains separately. We complement the misfit analyses with a Kolmogorov-Smirnov goodness-of-fit test and we conclude that the observed power spectrum can be each time a sample of the statistical one.

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“…The data are at present too limited for us to quantify the magnitude of this contribution or to critically evaluate the magnetization contrast arising from juxtaposition of continental and oceanic crust with differing magnetization and thickness, as modeled by Counil et al [ 1991].…”

Section: Discussionmentioning

confidence: 99%