scholarly journals Full-Wave Modeling and Inversion of UWB Radar Data for Wave Propagation in Cylindrical Objects

2021 ◽  
Vol 13 (12) ◽  
pp. 2370
Author(s):  
Lan Gao ◽  
Chiara Dachena ◽  
Kaijun Wu ◽  
Alessandro Fedeli ◽  
Matteo Pastorino ◽  
...  
Keyword(s):  
Numerical Experiments ◽  
Wave Model ◽  
Horn Antenna ◽  
Radar Data ◽  
Medical Diagnostics ◽  
Ultra Wideband ◽  
Lookup Table ◽  
Full Wave ◽  
Inversion Procedure ◽  
Ground Penetrating

The nondestructive characterization of cylindrical objects is needed in many fields, such as medical diagnostics, tree trunk inspection, or concrete column testing. In this study, the radar equation of Lambot et al. is combined with cylindrical Green’s functions to fully model and invert ultra-wideband (UWB) ground-penetrating radar (GPR) data and retrieve the properties of cylindrical objects. Inversion is carried out using a lookup table (LUT) approach followed by local optimization to ensure retrieval of the global minimum of the objective function. Numerical experiments were conducted to analyze the capabilities of the developed inversion procedure to estimate the radius, permittivity, and conductivity of the cylinders. The full-wave model was validated in laboratory conditions on metallic and plastic pipes of different sizes. The adopted radar system consists of a lightweight vector network analyzer (VNA) connected to a single transmitting and receiving horn antenna. The numerical experiments highlighted the complexity of the inverse problem, mainly originating from the multiple propagation modes within cylindrical objects. The laboratory measurements demonstrated the accuracy of the forward modeling and reconstructions in far-field conditions.

Inference of multi-Gaussian relative permittivity fields by probabilistic inversion of crosshole ground-penetrating radar data

Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. H25-H40 ◽  
Author(s):  
Jürg Hunziker ◽  
Eric Laloy ◽  
Niklas Linde
Keyword(s):  
Measurement Errors ◽  
Real Data ◽  
Radar Data ◽  
Gaussian Field ◽  
Geostatistical Model ◽  
First Arrival ◽  
Inversion Procedure ◽  
Data Error ◽  
Ground Penetrating ◽  
Probabilistic Inversion

In contrast to deterministic inversion, probabilistic Bayesian inversion provides an ensemble of solutions that can be used to quantify model uncertainty. We have developed a probabilistic inversion approach that uses crosshole first-arrival traveltimes to estimate an underlying geostatistical model, the subsurface structure, and the standard deviation of the data error simultaneously. The subsurface is assumed to be represented by a multi-Gaussian field, which allows us to reduce the dimensionality of the problem significantly. Compared with previous applications in hydrogeology, novelties of this study include an improvement of the dimensionality reduction algorithm to avoid streaking artifacts, it is the first application to geophysics and the first application to field data. The results of a synthetic example show that the model domain enclosed by one borehole pair is generally too small to provide reliable estimates of geostatistical variables. A real-data example based on two borehole pairs confirms these findings and demonstrates that the inversion procedure also works under realistic conditions with, for example, unknown measurement errors.

Reconstructing Properties of Subsurface from Ground-Penetrating Radar Data

PIERS Online ◽  
2006 ◽  
Vol 2 (6) ◽  
pp. 567-572
Author(s):  
Hui Zhou ◽  
Dongling Qiu ◽  
Takashi Takenaka
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Data ◽  
Ground Penetrating

scholarly journals Ice thickness distribution of all Swiss glaciers based on extended ground-penetrating radar data and glaciological modeling

2021 ◽  
pp. 1-19
Author(s):  
Melchior Grab ◽  
Enrico Mattea ◽  
Andreas Bauder ◽  
Matthias Huss ◽  
Lasse Rabenstein ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Thickness Distribution ◽  
Radar Data ◽  
Tourism Industry ◽  
Swiss Alps ◽  
Ice Thickness ◽  
Hazard Management ◽  
Bed Topography ◽  
Ice Volume ◽  
Ground Penetrating

Abstract Accurate knowledge of the ice thickness distribution and glacier bed topography is essential for predicting dynamic glacier changes and the future developments of downstream hydrology, which are impacting the energy sector, tourism industry and natural hazard management. Using AIR-ETH, a new helicopter-borne ground-penetrating radar (GPR) platform, we measured the ice thickness of all large and most medium-sized glaciers in the Swiss Alps during the years 2016–20. Most of these had either never or only partially been surveyed before. With this new dataset, 251 glaciers – making up 81% of the glacierized area – are now covered by GPR surveys. For obtaining a comprehensive estimate of the overall glacier ice volume, ice thickness distribution and glacier bed topography, we combined this large amount of data with two independent modeling algorithms. This resulted in new maps of the glacier bed topography with unprecedented accuracy. The total glacier volume in the Swiss Alps was determined to be 58.7 ± 2.5 km3 in the year 2016. By projecting these results based on mass-balance data, we estimated a total ice volume of 52.9 ± 2.7 km3 for the year 2020. Data and modeling results are accessible in the form of the SwissGlacierThickness-R2020 data package.

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