Evaluation of the viability and robustness of an iterative deconvolution approach for estimating the source wavelet during waveform inversion of crosshole ground‐penetrating radar data

2009 ◽  
Author(s):  
Florian Belina ◽  
James Irving ◽  
Klaus Holliger ◽  
Jacques Ernst
Keyword(s):  
Ground Penetrating Radar ◽  
Waveform Inversion ◽  
Radar Data ◽  
Ground Penetrating ◽  
Iterative Deconvolution

Quantitative multi-layer electromagnetic induction inversion and full-waveform inversion of crosshole ground penetrating radar data

2015 ◽  
Vol 26 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Jan van der Kruk ◽  
Nils Gueting ◽  
Anja Klotzsche ◽  
Guowei He ◽  
Sebastian Rudolph ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Electromagnetic Induction ◽  
Waveform Inversion ◽  
Radar Data ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Ground Penetrating

Full-waveform inversion of cross-hole ground-penetrating radar data to characterize a gravel aquifer close to the Thur River, Switzerland

2010 ◽  
Vol 8 (6) ◽  
pp. 635-649 ◽  
Author(s):  
Anja Klotzsche ◽  
Jan van der Kruk ◽  
Giovanni Angelo Meles ◽  
Joseph Doetsch ◽  
Hansruedi Maurer ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Waveform Inversion ◽  
Radar Data ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Ground Penetrating ◽  
Gravel Aquifer

Radius estimation of subsurface cylindrical objects from ground-penetrating-radar data using full-waveform inversion

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. H43-H54 ◽  
Author(s):  
Tao Liu ◽  
Anja Klotzsche ◽  
Mukund Pondkule ◽  
Harry Vereecken ◽  
Yi Su ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Waveform Inversion ◽  
Radar Data ◽  
Full Waveform Inversion ◽  
Data Inversion ◽  
Full Waveform ◽  
Wide Range ◽  
Object Parameters ◽  
Ground Penetrating ◽  
Effective Source

Ray-based radius estimations of subsurface cylindrical objects such as rebars and pipes from ground-penetrating-radar (GPR) measurements are not accurate because of their approximations. We have developed a novel full-waveform inversion (FWI) approach that uses a full-waveform 3D finite-difference time-domain (FDTD) forward-modeling program to estimate the radius including other object parameters. By using the full waveform of the common-offset GPR data, the shuffled complex evolution (SCE) approach is able to reliably extract the radius of the subsurface cylindrical objects. A combined optimization of radius, medium properties, and the effective source wavelet is necessary. Synthetic and experimental data inversion returns an accurate reconstruction of the cylinder properties, medium properties, and the effective source wavelet. Combining FWI of GPR data using SCE and a 3D FDTD forward model makes the approach easily adaptable for a wide range of other GPR FWI approaches.

scholarly journals Mapping shallow soil moisture profiles at the field scale using full-waveform inversion of ground penetrating radar data

Geoderma ◽  
2011 ◽  
Vol 161 (3-4) ◽  
pp. 225-237 ◽  
Author(s):  
Julien Minet ◽  
Agung Wahyudi ◽  
Patrick Bogaert ◽  
Marnik Vanclooster ◽  
Sébastien Lambot
Keyword(s):  
Soil Moisture ◽  
Ground Penetrating Radar ◽  
Waveform Inversion ◽  
Radar Data ◽  
Full Waveform Inversion ◽  
Field Scale ◽  
Full Waveform ◽  
Shallow Soil ◽  
Ground Penetrating ◽  
Moisture Profiles

scholarly journals Targeted reflection-waveform inversion of experimental ground-penetrating radar data for quantification of oil spills under sea ice

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA59-WA70 ◽  
Author(s):  
John H. Bradford ◽  
Esther L. Babcock ◽  
Hans-Peter Marshall ◽  
David F. Dickins
Keyword(s):  
Sea Ice ◽  
Ground Penetrating Radar ◽  
Oil Spills ◽  
Waveform Inversion ◽  
Radar Data ◽  
The Arctic ◽  
Ice Thickness ◽  
Inversion Algorithm ◽  
Control Value ◽  
Ground Penetrating

Rapid spill detection and mapping are needed with increasing levels of oil exploration and production in the Arctic. Previous work has found that ground-penetrating radar (GPR) is effective for qualitative identification of oil spills under, and encapsulated within, sea ice. Quantifying the spill distribution will aid effective spill response. To this end, we have developed a targeted GPR reflection-waveform inversion algorithm to quantify the geometry of oil spills under and within sea ice. With known electric properties of the ice and oil, we have inverted for oil thickness and variations in ice thickness. We have tested the algorithm with data collected during a controlled spill experiment using 500-MHz radar reflection data. The algorithm simultaneously recovered the thickness of a 5-cm-thick oil layer at the base of the ice to within 8% of the control value, estimated the thickness of a 1-cm-thick oil layer encapsulated within the ice to within 30% of the control value, and accurately mapped centimeter-scale variations in ice thickness.

Full-waveform inversion of Ground Penetrating Radar data for target characterization in multilayer environments

2020 ◽  
Author(s):  
Alessandro Fedeli ◽  
Matteo Pastorino ◽  
Andrea Randazzo
Keyword(s):  
Ground Penetrating Radar ◽  
Waveform Inversion ◽  
Region Of Interest ◽  
Mathematical Structure ◽  
Radar Data ◽  
Mathematical Framework ◽  
Qualitative Properties ◽  
Dielectric Characterization ◽  
Ground Penetrating

<p>In the last decades, ever growing efforts have been devoted to the development of techniques for extracting information from Ground Penetrating Radar (GPR) measurements. In particular, the processed data are used to retrieve two different kinds of features. The first kind includes the so-called qualitative properties of buried targets, which are typically related to the location and/or the shape of the objects. The second kind of features is related to the quantitative dielectric characterization of the underground targets. Both strengths and weaknesses of qualitative and quantitative approaches are well known in the scientific community. Despite the more complex mathematical structure of quantitative techniques, their use is attracting an increasing attention in multiple geophysical and engineering applications.</p><p>In this contribution, the full dielectric characterization of the region of interest is retrieved by a quantitative inversion approach that works in the mathematical framework of Lebesgue spaces with variable exponents. The most important parameter of this algorithm is represented by the map of the exponent function inside the investigation domain. Here, different strategies for obtaining and refining this map in an adaptive fashion iteration-by-iteration are proposed. Numerical results are presented to check the effectiveness of the inversion approach.</p>

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