A Multidisciplinary Analysis of Frequency Domain Metal Detectors for Humanitarian Demining

This thesis details an analysis of metal detectors (low frequency electromagnetic induction devices) with emphasis on Frequency Domain (FD) systems and the operational conditions of interest to humanitarian demining. After an initial look at humanitarian demining and a review of their basic principles we turn our attention to electromagnetic induction modelling and to analytical solutions to some basic FD direct (forward) problems. The second half of the thesis focuses then on the analysis of an extensive amount of experimental data. The possibility of target classification is first discussed on a qualitative basis, then quantitatively. Finally, we discuss shape and size determination via near field imaging

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

Apports de l'ultra large bande et de la diversité de polarisation du radar de sol pour l'auscultation des ouvrages du génie civil

The Ground Penetrating Radar technique (GPR) is now widely used as a non destructive probing and imaging tool in several civil engineering applications mainly concerning inspection of construction materials and structures, mapping of underground utilities and voids, characterization of sub-structures, foundations and soil and estimation of sub-surface volumetric moisture content. GPR belongs to a continuously evolving field due to electronic integration, high-performance computing, and advanced signal processing. The promotion of this technology relies on the development of new system configurations and data processing tools for the interpretation of sub-surface images. In this context, the work presents first the dual polarization UWB ground coupled GPR system which has been developed recently. Then, the data processing has focalized on the development of analysis tools to transform the raw images in a more user-readable image in order to improve the GPR data interpretation especially within the scope of detection of urban pipes and soil characterization. The processing means used concern clutter removal in the pre-processing step using adaptations and extensions of the PCA and ICA algorithms. Moreover, a template matching image processing technique is presented to help the detection of hyperbola within GPR raw B-scan images. The dual polarization is finally shown to bring additional information and to improve the detection of buried dielectric objects or medium discontinuities. The performances of our analysis approaches are illustrated using synthetic data (3D FDTD simulations) and field-measurement data in controlled environments. Different polarization configurations and dielectric characteristics of objects have been considered. The potential for target discrimination has been quantified using statistical criteria such as ROCLa technique de Georadar (GPR) est actuellement largement utilisée comme une technique non-destructive de sondage et d'imagerie dans plusieurs applications du génie civil qui concernent principalement: l'inspection des structures et des matériaux de construction, la cartographie des réseaux enterrés et des cavités, la caractérisation des fondations souterraines et du sol ainsi que l'estimation de la teneur en eau volumique du sous-sol. Le radar GPR est une technique en continuelle évolution en raison de l'intégration toujours plus poussée des équipements électroniques, des performances des calculateurs numériques, et des traitements du signal avancés. La promotion de cette technologie repose sur le développement de nouvelles configurations de systèmes et d'outils de traitement des données en vue de l'interprétation des images du sous-sol. Dans ce contexte, les travaux de cette thèse présentent tout d'abord le système GPR ULB (Ultra large bande) à double polarisation couplé au sol, lequel a été développé récemment au laboratoire. Par la suite, les traitement des données ont été focalisés sur le développement d'outils d'analyse en vue d'obtenir à partir des images brutes des images plus facilement lisibles par l'utilisateur afin d'améliorer l'interprétation des données GPR, en particulier dans le cadre de la détection de canalisations urbaines et la caractérisation des sols. Les moyens de traitement utilisés concernent l'élimination du clutter au cours d'une étape de prétraitement en utilisant des adaptations et des extensions des algorithmes fondés sur les techniques PCA et ICA. De plus, une technique de traitement d'image ‘'template matching” a été proposée pour faciliter la détection d'hyperbole dans une image Bscan de GPR. La diversité de polarisation est enfin abordée, dans le but de fournir des informations supplémentaires pour la détection d'objets diélectriques et des discontinuités du sous-sol. Les performances de nos outils d'analyse sont évaluées sur de données synthétiques (simulations 3D FDTD) et des données de mesures obtenues dans des environnements contrôlés. Pour cela, nous avons considéré différentes configurations de polarisation et des objets à caractéristiques diélectriques variées. Le potentiel de discrimination des cibles a été quantifié en utilisant le critère statistique fondé sur les courbes RO

Deep learning processing and interpretation of ground penetrating radar data using a numerical equivalent of a real GPR transducer

Ground-Penetrating Radar (GPR) is a popular non-destructive electromagnetic (EM) technique that is used in diverse applications across different fields, most commonly geophysics and civil engineering. One of the most common applications of GPR is concrete scanning, where it is used to detect structural elements and support the assessment of its condition. However, in any GPR application, the data have no resemblance to the characteristics of targets of interest and a means of extracting information from the data regarding the targets is required. Interpreting the GPR data, to infer key properties of the subsurface and to locate the targets is a difficult and challenging task and is highly dependent on the processing of the data and the experience of the user. Traditional processing techniques have some drawbacks, which can lead to misinterpretations of the data in addition to the interpretation being subjective to the user. Machine learning (ML) has proven its ability to solve a variety of problems and map complex relationships and in recent years, is becoming an increasingly attractive option for solving GPR and other EM problems regarding processing and interpretation. Numerical modelling has been extensively used to understand the EM wave propagation and assist in the interpretation of GPR responses. If ML is combined with numerical modelling, efficient solutions to GPR problems can be acquired. This research focuses on developing a numerical equivalent of a commercial GPR transducer and utilising this model to produce realistic synthetic training data sets for deep learning applications. The numerical model is based on the high-frequency 2000 MHz "palm" antenna from Geophysical Survey Systems, Inc. (GSSI). This GPR system is mainly used for concrete scanning, where the targets are located close to the surface. Unknown antenna parameters were found using global optimisation by minimising the mismatch between synthetic and real responses. A very good match was achieved, demonstrating that the model can accurately replicate the behaviour of the real antenna which was further validated using a number of laboratory experiments. Real data were acquired using the GSSI transducer over a sandbox and reinforced concrete slabs and the same scenarios were replicated in the simulations using the antenna model, showing excellent agreement. The developed antenna model was used to generate synthetic data, which are similar to the true data, for two deep learning applications, trained entirely using synthetic data. The first deep learning application suggested in the present thesis is background response and properties prediction. Two coupled neural networks are trained to predict the background response given as input total GPR responses, perform background removal and subsequently use the predicted background response to predict its dielectric properties. The suggested scheme not only performs the background removal processing step, but also enables the velocity calculation of the EM wave propagating in a medium using the predicted permittivity value. The ML algorithm is evaluated using a number of synthetic and measured data demonstrating its efficiency and higher accuracy compared to traditional methods. Predicting a permittivity value per A-scan included in a B-scan results in a permittivity distribution, which is used along with background removal to perform reverse-time migration (RTM). The proposed RTM scheme proved to be superior when compared with the commonly used RTM schemes. The second application was a deep learning-based forward solver, which is used as part of a full-waveform inversion (FWI) framework. A neural network is trained to predict entire B-scans given certain model parameters as input for reinforced concrete slab scenarios. The network makes predictions in real time, reducing by orders of magnitude the computational time of FWI, which is usually coupled with an FDTD forward solver. Therefore, making FWI applicable to commercial computers without the need of high-performance computing (HPC). The results clearly illustrate that ML schemes can be implemented to solve GPR problems and highlight the importance of having a digital representation of a real transducer in the simulations

Modelling, Simulation and Data Analysis in Acoustical Problems

Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about “Modelling, Simulation and Data Analysis in Acoustical Problems”, as we believe in the importance of these topics in modern acoustics’ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years

GPR Antipersonnel Mine Detection Based on Tensor Robust Principal Analysis

The ground Penetrating Radar (GPR) is a promising remote sensing modality for Antipersonnel Mine (APM) detection. However, detection of the buried APMs are impaired by strong clutter, especially the reflection caused by rough ground surfaces. In this paper, we propose a novel clutter suppression method taking advantage of the low-rank and sparse structure in multidimensional data, based on which an efficient target detection can be accomplished. We firstly created a multidimensional image tensor using sub-band GPR images that are computed from the band-pass filtered GPR signals, such that differences of the target response between sub-bands can be captured. Then, exploiting the low-rank and sparse property of the image tensor, we use the recently proposed Tensor Robust Principal Analysis to remove clutter by decomposing the image tensor into three components: a low-rank component containing clutter, a sparse component capturing target response, and noise. Finally, target detection is accomplished by applying thresholds to the extracted target image. Numerical simulations and experiments with different GPR systems are conducted. The results show that the proposed method effectively improves signal-to-clutter ratio by more than 20 dB and yields satisfactory results with high probability of detection and low false alarm rates