000877845 001__ 877845
000877845 005__ 20220930130244.0
000877845 0247_ $$2doi$$a10.1190/geo2019-0600.1
000877845 0247_ $$2ISSN$$a0016-8033
000877845 0247_ $$2ISSN$$a1942-2156
000877845 0247_ $$2WOS$$aWOS:000583755100020
000877845 0247_ $$2altmetric$$aaltmetric:88627327
000877845 037__ $$aFZJ-2020-02469
000877845 082__ $$a550
000877845 1001_ $$0P:(DE-Juel1)166264$$aMozaffari, Amirpasha$$b0$$eCorresponding author
000877845 245__ $$a2.5D crosshole GPR full-waveform inversion with synthetic and measured data
000877845 260__ $$aAlexandria, Va.$$bGeoScienceWorld$$c2020
000877845 3367_ $$2DRIVER$$aarticle
000877845 3367_ $$2DataCite$$aOutput Types/Journal article
000877845 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1596542884_4967
000877845 3367_ $$2BibTeX$$aARTICLE
000877845 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000877845 3367_ $$00$$2EndNote$$aJournal Article
000877845 520__ $$aFull-waveform inversion (FWI) of cross-borehole ground-penetrating radar (GPR) data is a technique with the potential to investigate subsurface structures. Typical FWI applications transform 3D measurements into a 2D domain via an asymptotic 3D to 2D data transformation, widely known as a Bleistein filter. Despite the broad use of such a transformation, it requires some assumptions that make it prone to errors. Although the existence of the errors is known, previous studies have failed to quantify the inaccuracies introduced on permittivity and electrical conductivity estimation. Based on a comparison of 3D and 2D modeling, errors could reach up to 30% of the original amplitudes in layered structures with high-contrast zones. These inaccuracies can significantly affect the performance of crosshole GPR FWI in estimating permittivity and especially electrical conductivity. We have addressed these potential inaccuracies by introducing a novel 2.5D crosshole GPR FWI that uses a 3D finite-difference time-domain forward solver (gprMax3D). This allows us to model GPR data in 3D, whereas carrying out FWI in the 2D plane. Synthetic results showed that 2.5D crosshole GPR FWI outperformed 2D FWI by achieving higher resolution and lower average errors for permittivity and conductivity models. The average model errors in the whole domain were reduced by approximately 2% for permittivity and conductivity, whereas zone-specific errors in high-contrast layers were reduced by approximately 20%. We verified our approach using crosshole 2.5D FWI measured data, and the results showed good agreement with previous 2D FWI results and geologic studies. Moreover, we analyzed various approaches and found an adequate trade-off between computational complexity and accuracy of the results, i.e., reducing the computational effort while maintaining the superior performance of our 2.5D FWI scheme.
000877845 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x0
000877845 588__ $$aDataset connected to CrossRef
000877845 7001_ $$0P:(DE-Juel1)129483$$aKlotzsche, Anja$$b1
000877845 7001_ $$0P:(DE-HGF)0$$aWarren, Craig$$b2
000877845 7001_ $$0P:(DE-Juel1)161461$$aHe, Guowei$$b3
000877845 7001_ $$0P:(DE-HGF)0$$aGiannopoulos, Antonios$$b4
000877845 7001_ $$0P:(DE-Juel1)129549$$aVereecken, Harry$$b5$$ufzj
000877845 7001_ $$0P:(DE-Juel1)129561$$avan der Kruk, Jan$$b6
000877845 773__ $$0PERI:(DE-600)2033021-2$$a10.1190/geo2019-0600.1$$gVol. 85, no. 4, p. H71 - H82$$n4$$pH71 - H82$$tGeophysics$$v85$$x1942-2156$$y2020
000877845 8564_ $$uhttps://juser.fz-juelich.de/record/877845/files/Invoice_0001004070.pdf
000877845 8564_ $$uhttps://juser.fz-juelich.de/record/877845/files/Invoice_0001004070.pdf?subformat=pdfa$$xpdfa
000877845 8564_ $$uhttps://juser.fz-juelich.de/record/877845/files/geo2019-0600.1%20%28002%29.pdf$$yRestricted
000877845 8564_ $$uhttps://juser.fz-juelich.de/record/877845/files/geo2019-0600.1%20%28002%29.pdf?subformat=pdfa$$xpdfa$$yRestricted
000877845 8767_ $$80001004070$$92020-07-01$$d2020-07-16$$ePage charges$$jZahlung erfolgt$$p#2019-0600.R2$$zUSD 450,-
000877845 909CO $$ooai:juser.fz-juelich.de:877845$$pVDB:Earth_Environment$$pVDB$$pOpenAPC$$popenCost
000877845 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)166264$$aForschungszentrum Jülich$$b0$$kFZJ
000877845 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129483$$aForschungszentrum Jülich$$b1$$kFZJ
000877845 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161461$$aForschungszentrum Jülich$$b3$$kFZJ
000877845 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129549$$aForschungszentrum Jülich$$b5$$kFZJ
000877845 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129561$$aForschungszentrum Jülich$$b6$$kFZJ
000877845 9131_ $$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$$x0
000877845 9141_ $$y2020
000877845 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-01-16
000877845 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index$$d2020-01-16
000877845 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-01-16
000877845 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-01-16
000877845 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-01-16
000877845 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2020-01-16
000877845 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bGEOPHYSICS : 2018$$d2020-01-16
000877845 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-01-16
000877845 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2020-01-16
000877845 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000877845 980__ $$ajournal
000877845 980__ $$aVDB
000877845 980__ $$aI:(DE-Juel1)IBG-3-20101118
000877845 980__ $$aAPC
000877845 980__ $$aUNRESTRICTED
000877845 9801_ $$aAPC