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000011776 084__ $$2WoS$$aGeochemistry & Geophysics
000011776 1001_ $$0P:(DE-Juel1)VDB85548$$aMoghadas, D.$$b0$$uFZJ
000011776 245__ $$aJoint full-waveform analysis of off-ground zero-offset ground penetrating radar and electromagnetic induction synthetic data for estimating soil electrical properties
000011776 260__ $$aOxford . Wiley-Blackwell$$bWiley-Blackwell - STM$$c2010
000011776 300__ $$a1267 - 1278
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000011776 440_0 $$023081$$aGeophysical Journal International$$v182$$x0956-540X$$y3
000011776 500__ $$aThis research was supported by the Forschungszentrum Julich (Germany), Universite catholique de Louvain and FNRS (Belgium) in the framework of the DIGISOIL project, financed by the European Commission under the 7th Framework Programme for Research and Technological Development, Area 'Environment', Activity 6.3 'Environmental Technologies'.
000011776 520__ $$aA joint analysis of full-waveform information content in ground penetrating radar (GPR) and electromagnetic induction (EMI) synthetic data was investigated to reconstruct the electrical properties of multilayered media. The GPR and EMI systems operate in zero-offset, off-ground mode and are designed using vector network analyser technology. The inverse problem is formulated in the least-squares sense. We compared four approaches for GPR and EMI data fusion. The two first techniques consisted of defining a single objective function, applying different weighting methods. As a first approach, we weighted the EMI and GPR data using the inverse of the data variance. The ideal point method was also employed as a second weighting scenario. The third approach is the naive Bayesian method and the fourth technique corresponds to GPR–EMI and EMI–GPR sequential inversions. Synthetic GPR and EMI data were generated for the particular case of a two-layered medium. Analysis of the objective function response surfaces from the two first approaches demonstrated the benefit of combining the two sources of information. However, due to the variations of the GPR and EMI model sensitivities with respect to the medium electrical properties, the formulation of an optimal objective function based on the weighting methods is not straightforward. While the Bayesian method relies on assumptions with respect to the statistical distribution of the parameters, it may constitute a relevant alternative for GPR and EMI data fusion. Sequential inversions of different configurations for a two layered medium show that in the case of high conductivity or permittivity for the first layer, the inversion scheme can not fully retrieve the soil hydrogeophysical parameters. But in the case of low permittivity and conductivity for the first layer, GPR–EMI inversion provides proper estimation of values compared to the EMI–GPR inversion.
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000011776 65320 $$2Author$$aInverse theory
000011776 65320 $$2Author$$aGround penetrating radar
000011776 65320 $$2Author$$aMagnetic and electrical properties
000011776 65320 $$2Author$$aHydrogeophysics
000011776 7001_ $$0P:(DE-Juel1)VDB85547$$aAndré, F.$$b1$$uFZJ
000011776 7001_ $$0P:(DE-HGF)0$$aSlob, E.C.$$b2
000011776 7001_ $$0P:(DE-Juel1)129549$$aVereecken, H.$$b3$$uFZJ
000011776 7001_ $$0P:(DE-Juel1)VDB54976$$aLambot, S.$$b4$$uFZJ
000011776 773__ $$0PERI:(DE-600)2006420-2$$a10.1111/j.1365-246X.2010.04706.x$$gVol. 182, p. 1267 - 1278$$p1267 - 1278$$q182<1267 - 1278$$tGeophysical journal international$$v182$$x0956-540X$$y2010
000011776 8567_ $$uhttp://dx.doi.org/10.1111/j.1365-246X.2010.04706.x
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