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000866395 1001_ $$0P:(DE-Juel1)129483$$aKlotzsche, Anja$$b0$$eCorresponding author
000866395 245__ $$aReview of crosshole ground-penetrating radar full-waveform inversion of experimental data: Recent developments, challenges, and pitfalls
000866395 260__ $$aAlexandria, Va.$$bGeoScienceWorld$$c2019
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000866395 520__ $$aHeterogeneous small-scale high-contrast layers and spatial variabilities of soil properties can have a large impact on flow and transport processes in the critical zone. Because their characterization is difficult and critical, high-resolution methods are required. Standard ray-based approaches for imaging the subsurface consider only a small amount of the measured data and suffer from limited resolution. In contrast, full-waveform inversion (FWI) considers the full information content of the measured data and could yield higher resolution images in the subwavelength scale. In the past few decades, ground-penetrating radar (GPR) FWI and its application to experimental data have matured, which makes GPR FWI an established approach to significantly improve resolution. Several theoretical developments were achieved to improve the application to experimental data from crosshole GPR FWI. We have determined the necessary steps to perform FWI for experimental data to obtain reliable and reproducible high-resolution images. We concentrate on experimental crosshole GPR data from a test site in Switzerland to illustrate the challenges of applying FWI to experimental data and discuss the obtained results for different development steps including possible pitfalls. Thereby, we acknowledge out the importance of a correct time-zero correction of the data, the estimation of the effective source wavelet, and the effect of the choice of starting models. The reliability of the FWI results is investigated by analyzing the fit of the measured and modeled traces, the remaining gradients of the final models, and validating with independently measured logging data. Thereby, we found that special care needs to be taken to define the optimal inversion parameters to avoid overshooting of the inversion or truncation errors.
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000866395 536__ $$0G:(DE-Juel1)jicg41_20100501$$aBetter predictions with environmental simulation models: optimally integrating new data sources (jicg41_20100501)$$cjicg41_20100501$$fBetter predictions with environmental simulation models: optimally integrating new data sources$$x1
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000866395 7001_ $$0P:(DE-Juel1)129549$$aVereecken, Harry$$b1$$ufzj
000866395 7001_ $$0P:(DE-Juel1)129561$$avan der Kruk, Jan$$b2$$ufzj
000866395 773__ $$0PERI:(DE-600)2033021-2$$a10.1190/geo2018-0597.1$$gVol. 84, no. 6, p. H13 - H28$$n6$$pH13 - H28$$tGeophysics$$v84$$x1942-2156$$y2019
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