000834749 001__ 834749 000834749 005__ 20220930130126.0 000834749 0247_ $$2Handle$$a2128/15713 000834749 0247_ $$2URN$$aurn:nbn:de:0001-2017120719 000834749 0247_ $$2ISSN$$a1866-1793 000834749 020__ $$a978-3-95806-253-5 000834749 037__ $$aFZJ-2017-04645 000834749 041__ $$aEnglish 000834749 1001_ $$0P:(DE-Juel1)158035$$aGüting, Nils$$b0$$eCorresponding author$$gmale$$ufzj 000834749 245__ $$aHigh resolution imaging and modeling of aquifer structure$$f- 2017-07-03 000834749 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2017 000834749 300__ $$aviii, 107 S. 000834749 3367_ $$2DataCite$$aOutput Types/Dissertation 000834749 3367_ $$0PUB:(DE-HGF)3$$2PUB:(DE-HGF)$$aBook$$mbook 000834749 3367_ $$2ORCID$$aDISSERTATION 000834749 3367_ $$2BibTeX$$aPHDTHESIS 000834749 3367_ $$02$$2EndNote$$aThesis 000834749 3367_ $$0PUB:(DE-HGF)11$$2PUB:(DE-HGF)$$aDissertation / PhD Thesis$$bphd$$mphd$$s1509082453_9973 000834749 3367_ $$2DRIVER$$adoctoralThesis 000834749 4900_ $$aSchriften des Forschungszentrums Jülich Reihe Energie & Umwelt / Energy & Environment$$v383 000834749 502__ $$aRuhr-Universität Bochum, Diss., 2017$$bDr.$$cRuhr-Universität Bochum$$d2017$$o2017-07-03 000834749 520__ $$aPredictive modeling of groundwater flow and solute transport can help to protect groundwater resources and to remediate contaminated sites. It is challenging, however,to develop realistic groundwater models because it is difficult to characterize and model the complex heterogeneity of geologic media. In this work, we propose an approach that combines high resolution geophysical imaging and multiple-point statistical modeling to estimate the 3-D structure of aquifers. Our study is carried out at the Krauthausen site, Germany, where 15 cross-boreholeground-penetrating radar (GPR) data sets were acquired in an all uvial sand and gravel aquifer. To analyze the GPR data, we apply a recently developed full-waveform inversion approach that is preferable, in terms of spatial resolution, over traditional ray based inversion approaches. By stitching together the inverted tomograms from adjacent crosshole planes, we are able to image, with a decimeter-scale resolution, the aquifer’s electrical properties (dielectric permittivity and electrical conductivity) along vertical cross-sections up to 50 m long and 10 m deep. Comparison of the GPR results with co-located direct-push profiles shows a strong correlation between the porosity derived from GPR dielectric permittivity and the porosity derived from direct-push neutron logs. We can show that the dielectric permittivity obtained from full-wave form inversion more accurately reconstructs sharp contrasts and fine-scale variations in porositythan the dielectric permittivity obtained from traditional ray-based inversion. One problem with using GPR for hydrogeological site characterization is that GPR yields electrical properties which are only indirectly linked to hydraulic properties. We present two approaches to estimate hydrogeological facies from the GPR results. The first approach, based on k-means cluster analysis, is applied to GPR data from five adjacent crosshole planes. Cluster analysis of the GPR results suggests three facies. Densely spaced cone penetration tests, located along the GPR transect, confirm the number of facies and their spatial distribution in the aquifer cross-section. Grain size distributions and flowmeter data, available from one of the boreholes, show that the derived facies boundaries correlate with changes in grain size and porosity, and to a lesser extent with changes in hydraulic conductivity.[...] 000834749 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x0 000834749 650_7 $$xDiss. 000834749 8564_ $$uhttps://juser.fz-juelich.de/record/834749/files/Energie_Umwelt_383.pdf$$yOpenAccess 000834749 8564_ $$uhttps://juser.fz-juelich.de/record/834749/files/Energie_Umwelt_383.gif?subformat=icon$$xicon$$yOpenAccess 000834749 8564_ $$uhttps://juser.fz-juelich.de/record/834749/files/Energie_Umwelt_383.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess 000834749 8564_ $$uhttps://juser.fz-juelich.de/record/834749/files/Energie_Umwelt_383.jpg?subformat=icon-180$$xicon-180$$yOpenAccess 000834749 8564_ $$uhttps://juser.fz-juelich.de/record/834749/files/Energie_Umwelt_383.jpg?subformat=icon-640$$xicon-640$$yOpenAccess 000834749 8564_ $$uhttps://juser.fz-juelich.de/record/834749/files/Energie_Umwelt_383.pdf?subformat=pdfa$$xpdfa$$yOpenAccess 000834749 909CO $$ooai:juser.fz-juelich.de:834749$$pVDB$$pVDB:Earth_Environment$$pdriver$$purn$$popen_access$$popenaire$$pdnbdelivery 000834749 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess 000834749 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0 000834749 9141_ $$y2017 000834749 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)158035$$aForschungszentrum Jülich$$b0$$kFZJ 000834749 9131_ $$0G:(DE-HGF)POF3-255$$1G:(DE-HGF)POF3-250$$2G:(DE-HGF)POF3-200$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bErde und Umwelt$$lTerrestrische Umwelt$$vTerrestrial Systems: From Observation to Prediction$$x0 000834749 920__ $$lyes 000834749 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0 000834749 980__ $$aphd 000834749 980__ $$aVDB 000834749 980__ $$aUNRESTRICTED 000834749 980__ $$abook 000834749 980__ $$aI:(DE-Juel1)IBG-3-20101118 000834749 9801_ $$aFullTexts