scholarly journals A joint thermal and electromagnetic diagnostics approach for the inspection of thick walls

2016 ◽  
Author(s):  
Nicolas Le Touz ◽  
Jean Dumoulin ◽  
Gianluca Gennarelli ◽  
Francesco Soldovieri
Keyword(s):  
Ground Penetrating Radar ◽  
Preliminary Analysis ◽  
A Priori ◽  
Numerical Inversion ◽  
Reconstruction Method ◽  
A Priori Information ◽  
Priori Information ◽  
Ground Penetrating ◽  
Surface Temperature Field ◽  
Evolution With Time

Abstract. In this study, we present a numerical inversion approach to detect and localize inclusions in thick walls under natural solicitations. The approach is based on a preliminary analysis of the surface temperature field evolution with time (for instance acquired by infrared thermography); after, this analysis is improved by taking advantage of a priori information provided by ground penetrating radar reconstructions of the structure under investigation. In this way, it is possible to improve the accuracy of the images achievable with the stand-alone thermal reconstruction method in the case of quasi-periodic natural excitation.

scholarly journals A joint thermal and electromagnetic diagnostics approach for the inspection of thick walls

2017 ◽  
Vol 6 (1) ◽  
pp. 81-92 ◽  
Author(s):  
Nicolas Le Touz ◽  
Jean Dumoulin ◽  
Gianluca Gennarelli ◽  
Francesco Soldovieri
Keyword(s):  
Temperature Field ◽  
Ground Penetrating Radar ◽  
Preliminary Analysis ◽  
A Priori ◽  
Reconstruction Method ◽  
A Priori Information ◽  
Priori Information ◽  
Ground Penetrating ◽  
Surface Temperature Field ◽  
Evolution With Time

Abstract. In this study, we present an inversion approach to detect and localize inclusions in thick walls under natural solicitations. The approach is based on a preliminary analysis of surface temperature field evolution with time (for instance acquired by infrared thermography); subsequently, this analysis is improved by taking advantage of a priori information provided by ground-penetrating radar reconstruction of the structure under investigation. In this way, it is possible to improve the accuracy of the images achievable with the stand-alone thermal reconstruction method in the case of quasi-periodic natural excitation.

scholarly journals Multi-Frequency GPR Microwave Imaging of Sparse Targets through a Multi-Task Bayesian Compressive Sensing Approach

2021 ◽  
Vol 7 (11) ◽  
pp. 247
Author(s):  
Marco Salucci ◽  
Nicola Anselmi
Keyword(s):  
Compressive Sensing ◽  
Quantitative Imaging ◽  
A Priori ◽  
Wide Band ◽  
Frequency Diversity ◽  
A Priori Information ◽  
Spectral Components ◽  
Priori Information ◽  
The One ◽  
Ground Penetrating

An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data by jointly processing the multi-frequency (MF) spectral components of the collected radargrams. On the other hand, it enforces sparsity priors on the problem unknowns to yield regularized solutions of the fully non-linear scattering equations. Towards this end, a multi-task Bayesian compressive sensing (MT-BCS) methodology is adopted and suitably customized to take full advantage of the available frequency diversity and of the a-priori information on the class of imaged targets. Representative results are reported to assess the proposed MF-MT-BCS strategy also in comparison with competitive state-of-the-art alternatives.

scholarly journals A synthetic study to assess the applicability of full-waveform inversion to infer snow stratigraphy from upward-looking ground-penetrating radar data

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA213-WA223 ◽  
Author(s):  
Lino Schmid ◽  
Jürg Schweizer ◽  
John Bradford ◽  
Hansruedi Maurer
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
A Priori ◽  
Waveform Inversion ◽  
Liquid Water Content ◽  
A Priori Information ◽  
Full Waveform ◽  
Snow Stratigraphy ◽  
Priori Information ◽  
Ground Penetrating

Snow stratigraphy and liquid water content are key contributing factors to avalanche formation. Upward-looking ground-penetrating radar (upGPR) systems allow nondestructive monitoring of the snowpack, but deriving density and liquid water content profiles is not yet possible based on the direct analysis of the reflection response. We have investigated the feasibility of deducing these quantities using full-waveform inversion (FWI) techniques applied to upGPR data. For that purpose, we have developed a frequency-domain FWI algorithm in which we additionally took advantage of time-domain features such as the arrival times of reflected waves. Our results indicated that FWI applied to upGPR data is generally feasible. More specifically, we could show that in the case of a dry snowpack, it is possible to derive snow densities and layer thicknesses if sufficient a priori information is available. In case of a wet snowpack, in which it also needs to be inverted for the liquid water content, the algorithm might fail, even if sufficient a priori information is available, particularly in the presence of realistic noise. Finally, we have investigated the capability of FWI to resolve thin layers that play a key role in snow stability evaluation. Our simulations indicate that layers with thicknesses well below the GPR wavelengths can be identified, but in the presence of significant liquid water, the thin-layer properties may be prone to inaccuracies. These results are encouraging and motivate applications to field data, but significant issues remain to be resolved, such as the determination of the generally unknown upGPR source function and identifying the optimal number of layers in the inversion models. Furthermore, a relatively high level of prior knowledge is required to let the algorithm converge. However, we feel these are not insurmountable and the new technology has significant potential to improve field data analysis.

Reconstruction of dynamic objects with affine deformations in computerized tomography

2014 ◽  
Vol 22 (3) ◽  
Author(s):  
Bernadette Hahn
Keyword(s):  
Image Analysis ◽  
Computerized Tomography ◽  
A Priori ◽  
Special Focus ◽  
Data Sets ◽  
Reconstruction Method ◽  
A Priori Information ◽  
Motion Artefacts ◽  
X Ray ◽  
Priori Information

Abstract.The data acquisition in computerized tomography takes a certain amount of time since the x-ray source has to be rotated around the specimen. An object that changes during the scanning causes inconsistent data sets. To avoid the motion artefacts in reconstructions, the algorithm has to take the dynamic behavior of the specimen into account. In this context, some a priori information about the movement is required. A reconstruction method is proposed that compensates for the motion with a special focus on affine deformations. It also permits the combination of reconstruction and image analysis tools to extract features of the object without motion artefacts. The algorithm is validated with a numerical example from medical imaging.

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