A Compact Dual-Polarized Vivaldi Antenna with High Gain for Tree Radar Applications.

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna.

Variable data structures and customized deep learning surrogates for computationally efficient and reliable characterization of buried objects.

In this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model have been used. The task is to independently predict characteristic parameters of a buried object of diverse radii allocated at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable data structures (raw B-scans, extracted features, consecutive A-scans) with respect to computational cost and accuracy of surrogates. The usage of raw B-scan data and the applications for processing steps on B-scan profiles in the context of object characterization incur high computational cost so it can be a challenging issue. The proposed surrogate model referred to as the deep regression network (DRN) is utilized for time frequency spectrogram (TFS) of consecutive A-scans. DRN is developed with the main aim being computationally efficient (about 13 times acceleration) compared to conventional network models using B-scan images (2D data). DRN with TFS is favorably benchmarked to the state-of-the-art regression techniques. The experimental results obtained for the proposed model and second-best model, CNN-1D show mean absolute and relative error rates of 3.6 mm, 11.8 mm and 4.7%, 11.6% respectively. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources.

Imaging of Structural Timber Based on In Situ Radar and Ultrasonic Wave Measurements: A Review of the State-of-the-Art.

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced.

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task.

The S-transform is a fundamental time-frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0-3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.

GPR Mapping of Cavities in Complex Scenarios with a Combined Time-Depth Conversion.

The paper deals with a combined time-depth conversion strategy able to improve the reconstruction of voids embedded in an opaque medium, such as cavities, caves, empty hypogeal rooms, and similar targets. The combined time-depth conversion accounts for the propagation velocity of the electromagnetic waves both in free space and in the embedding medium, and it allows better imaging and interpretation of the underground scenario. To assess the strategy's effectiveness, ground penetrating radar (GPR) data referred to as an experimental test in controlled conditions are accounted for and processed by two different approaches to achieve focused images of the scenario under test. The first approach is based on a classical migration algorithm, while the second one faces the imaging as a linear inverse scattering approach. The results corroborate that the combined time-depth conversion improves the imaging in both cases.

Ground penetrating radar at work: A realistic perspective on utility surveying in the Netherlands through a comprehensive ground-truth dataset.

This dataset provides a comprehensive compilation of Ground Penetrating Radar (GPR) surveys across 125 utility surveying activities in the Netherlands. The dataset details the specific use of GPR in each authentic real-life utility surveying activity, whether employed independently or as a complementary tool alongside existing surveying methods, with or without post-processing. The dataset includes 959 radargrams, ground-truth information obtained from trial trenches, and an inventory of construction, geophysical, infrastructural, and technical features. The GPR utilised in all activities is an air-coupled radar with a 500 MHz frequency antenna, a GNSS RTK positioning system, and a measuring wheel encoder. This ground-truth dataset provides researchers with a valuable resource to further assess the practical efficacy of GPR as a utility surveying method, refine radargram processing algorithms and techniques, and explore the possibilities of predictive modelling.

Damage Detection in FRP-Reinforced Concrete Elements.

Fiber-Reinforced Polymer (FRP) composites have emerged as a promising alternative to conventional steel reinforcements in concrete structures owing to their benefits of corrosion resistance, higher strength-to-weight ratio, reduced maintenance cost, extended service life, and superior durability. However, there has been limited research on non-destructive testing (NDT) methods applicable for identifying damage in FRP-reinforced concrete (FRP-RC) elements. This knowledge gap has often limited its application in the construction industry. Engineers and owners often lack confidence in utilizing this relatively new construction material due to the challenge of assessing its condition. Thus, the main objective of this study is to determine the applicability of two of the most common NDT methods: the Ground-Penetrating Radar (GPR) and Phased Array Ultrasonic (PAU) methods for the detection of damage in FRP-RC elements. Three slab specimens with variations in FRP type (glass-, carbon- and basalt-FRP, i.e., GFRP, CFRP, and BFRP, respectively), bar diameter, bar depths, and defect types were investigated to determine the limitations and detection capabilities of these two NDT methods. The results show that GPR could detect damage in GFRP bars and CFRP strands, but PAU was limited to damage detection in CFRP strands. The findings of this study show the applicability of conventional NDT methods to FRP-RC and at the same time identify the areas with a need for further research.

Ground penetrating radar and electrical resistivity tomography surveys with a subsequent intrusive investigation in search for the missing Beaumont children in Adelaide, South Australia.

Jane, Arnna, and Grant Beaumont went missing from Glenelg Beach in Adelaide, South Australia on 26 January (Australia Day) 1966. Despite multiple land and sea searches over nearly 60 years, the children have not been found. New credible eyewitness testimony led to a site of interest at the now disused New Castalloy factory in North Plympton, Adelaide. This site has a complex stratigraphy of anthropogenic fill, which made ground penetrating radar (GPR) investigations unpromising. Electrical resistivity tomography (ERT), while not commonly used in a forensic capacity, provided an alternative approach that allowed suitable depth penetration to resolve a feature of interest, which was subsequently excavated by the South Australia Police. This feature did contain organic, and animal remains but, sadly, not the grave of Jane, Arnna, and Grant Beaumont. However, this investigation highlights the potential to use ERT in a forensic capacity, as well as the limitations of using geophysical techniques for covert burial detection.

A Radar Echo Simulator for the Synthesis of Randomized Training Data Sets in the Context of AI-Based Applications.

Supervised machine learning algorithms usually require huge labeled data sets to produce sufficiently good results. For many applications, these data sets are still not available today, and the reasons for this can be manifold. As a solution, the missing training data can be generated by fast simulators. This procedure is well studied and allows filling possible gaps in the training data, which can further improve the results of a machine learning model. For this reason, this article deals with the development of a two-dimensional electromagnetic field simulator for modeling the response of a radar sensor in an imaging system based on the synthetic aperture radar principle. The creation of completely random scenes is essential to achieve data sets with large variance. Therefore, special emphasis is placed on the development of methods that allow creating random objects, which can then be assembled into an entire scene. In the context of this contribution, we focus on humanitarian demining with regard to improvised explosive devices using a ground-penetrating radar system. This is an area where the use of trained classifiers is of great importance, but in practice, there are little to no labeled datasets for the training process. The simulation results show good agreement with the measurement results obtained in a previous contribution, demonstrating the possibility of enhancing sparse training data sets with synthetic data.

A simple method for estimating the coarse lateral root biomass of shrubs using ground-penetrating radar: Validation by Caragana microphylla Lam. in Inner Mongolia.

The potential increases in carbon stocks in arid regions due to recent shrub encroachment have attracted extensive interest among both ecologists and carbon policy analysts. Quantifying the shrub root biomass amount in these ecosystems is essential to understanding the ecological changes occurring. In this paper, we proposed a simple nondestructive method for estimating the coarse lateral root biomass of shrubs based on the root counts obtained from ground-penetrating radar (GPR) radargrams. Root data were gathered via field experiments using GPR with antenna center frequencies of 900 MHz and 400 MHz. Five Caragana microphylla Lam. shrubs of different sizes were selected for measuring objects, and a total of 40 GPR survey lines were established for GPR data acquisition. The soil profile wall excavation method was used to obtain the total root biomass from each radargram. A model for estimating the root biomass was built by establishing the relationship between the root biomass in each profile and the root counts interpreted from the radargrams. According to the mathematical relationship between the root diameter and root biomass, the proxy root radius was derived, which could explain the rationality of the proposed model from the biological mechanism. The established model provided high confidence in estimating the root dry biomass using the GPR data obtained at the two antenna frequencies (R2= 0.73 for 900 MHz and R2= 0.71 for 400 MHz). The leave-one-out cross-validation results showed that the model exhibits satisfactory performance. This study expands the application of geophysical methods in root research and offers a new simplified method for estimating the root biomass from GPR data under field conditions.

The suitability of using domestic pigs (Sus spp.) as human proxies in the geophysical detection of clandestine graves.

Research in many forensic science fields commonly uses domestic pigs (Sus spp.) as proxies for human remains, due to their physiological and anatomical similarities, as well as being more readily available. Unfortunately, previous research, especially that which compares the decompositional process, has shown that pigs are not appropriate proxies for humans. To date, there has not been any published research that specifically addresses whether domestic pigs are adequate human proxies for the geophysical detection of clandestine graves. As such, the aim of this paper was to compare the geophysical responses of pig cadavers and human donor graves, in order to determine if pigs can indeed be used as adequate human proxies. To accomplish this, ground penetrating radar (GPR) and electrical resistivity tomography (ERT) responses on single and multiple pig cadaver graves were compared to single and multiple human donor graves, all of which are in known locations within the same geological environment. The results showed that under field conditions, both GPR and ERT were successful at observing human and pig burials, with no obvious differences between the detected geophysical responses. The results also showed that there were no differences in the geophysical responses of those who were clothed and unclothed. The similarity of the responses may reflect that the geophysical techniques can detect graves despite what their contents are. The study implications suggest that experimental studies in other soil and climate conditions can be easily replicated, benefiting law enforcement with missing persons cases.

Sparsity enhanced MRF algorithm for automatic object detection in GPR imagery.

This study addressed the problem of automated object detection from ground penetrating radar imaging (GPR), using the concept of sparse representation. The detection task is first formulated as a Markov random field (MRF) process. Then, we propose a novel detection algorithm by introducing the sparsity constraint to the standard MRF model. Specifically, the traditional approach finds it difficult to determine the central target due to the influence of different neighbors from the imaging area. As such, we introduce a domain search algorithm to overcome this issue and increase the accuracy of target detection. Additionally, in the standard MRF model, the Gibbs parameters are empirically predetermined and fixed during the detection process, yet those hyperparameters may have a significant effect on the performance of the detection. Accordingly, in this paper, Gibbs parameters are self-adaptive and fine-tuned using an iterative updating strategy followed the concept of sparse representation. Furthermore, the proposed algorithm has then been proven to have a strong convergence property theoretically. Finally, we verify the proposed method using a real-world dataset, with a set of ground penetrating radar antennas in three different transmitted frequencies (50 MHz, 200 MHz and 300 MHz). Experimental evaluations demonstrate the advantages of utilizing the proposed algorithm to detect objects in ground penetrating radar imagery, in comparison with four traditional detection algorithms.

A Novel, Efficient Algorithm for Subsurface Radar Imaging below a Non-Planar Surface.

In classical radar imaging, such as in Earth remote sensing, electromagnetic waves are usually assumed to propagate in free space. However, in numerous applications, such as ground penetrating radar or non-destructive testing, this assumption no longer holds. When there is a multi-material background, the subsurface image reconstruction becomes considerably more complex. Imaging can be performed in the spatial domain or, equivalently, in the wavenumber domain (k-space). In subsurface imaging, to date, objects with a non-planar surface are commonly reconstructed in the spatial domain, by the Backprojection algorithm combined with ray tracing, which is computationally demanding. On the other hand, objects with a planar surface can be reconstructed more efficiently in k-space. However, many non-planar surfaces are partly planar. Therefore, in this paper, a novel concept is introduced that makes use of the efficient k-space-based reconstruction algorithms for partly planar scenarios, too. The proposed algorithm forms an image from superposing sub-images where as many image parts as possible are reconstructed in the wavenumber domain, and only as many as necessary are reconstructed in the spatial domain. For this, a segmentation scheme is developed to determine which parts of the image volume can be reconstructed in the wavenumber domain. The novel concept is verified by measurements, both from monostatic synthetic aperture radar data and multiple-input-multiple-output radar data. It is shown that the computational efficiency for imaging irregularly shaped geometries can be significantly augmented when applying the proposed concept.

A Multimodal Research Approach to Assessing the Karst Structural Conditions of the Ceiling of a Cave with Palaeolithic Cave Art Paintings: Polychrome Hall at Altamira Cave (Spain).

Integrating geomatics remote sensing technologies, including 3D terrestrial laser scanning, unmanned aerial vehicles, and ground penetrating radar enables the generation of comprehensive 2D, 2.5D, and 3D documentation for caves and their surroundings. This study focuses on the Altamira Cave's karst system in Spain, resulting in a thorough 3D mapping encompassing both cave interior and exterior topography along with significant discontinuities and karst features in the vicinity. Crucially, GPR mapping confirms that primary vertical discontinuities extend from the near-surface (Upper Layer) to the base of the Polychrome layer housing prehistoric paintings. This discovery signifies direct interconnections helping with fluid exchange between the cave's interior and exterior, a groundbreaking revelation. Such fluid movement has profound implications for site conservation. The utilization of various GPR antennas corroborates the initial hypothesis regarding fluid exchanges and provides concrete proof of their occurrence. This study underscores the indispensability of integrated 3D mapping and GPR techniques for monitoring fluid dynamics within the cave. These tools are vital for safeguarding Altamira, a site of exceptional significance due to its invaluable prehistoric cave paintings.

An Approach for Easy Detection of Buried FRP Composite/Non-Metallic Pipes Using Ground-Penetrating Radar.

Pipelines remain the safest means of transporting natural gas and petroleum products. Nonetheless, the pipeline infrastructure in the US is facing major challenges, especially in terms of corrosion of steel/metallic pipes and excavation damage of onshore pipelines (leading to oil spills, explosions, and deaths). Corrosion of metallic pipelines can be avoided by using non-corrosive materials such as plastic pipes for low-pressure applications and glass-fiber-reinforced polymer (GFRP) composite pipes for transporting high-pressure oil and natural gas. However, buried non-metallic pipelines are not easily detectable, which can lead to increased excavation damage during construction and rehabilitation work. Alternative strategies for making buried non-metallic pipes easily locatable using ground-penetrating radar (GPR) were investigated in this study. Results from this study have shown that using carbon fabric or an aluminum foil overlay on non-metallic pipes before burying in soil significantly increases the reflected GPR signal amplitude, thereby making it easier to locate such pipelines. The reflected GPR signal amplitude for pipe sections with carbon fabric or aluminum foil overlays was found to have increased by a factor of up to 4.5 over the control samples. The results also highlight the importance of selecting the appropriate antenna frequency for GPR surveys, since wet silt loam soil and clay significantly reduce the penetration depths of the radar signals produced by the GPR antennae.

Time-Lapse GPR Measurements to Monitor Resin Injection in Fractures of Marble Blocks.

The objective of this study is to test the feasibility of time-lapse GPR measurements for the quality control of repairing operations (i.e., injections) on marble blocks. For the experimental activities, we used one of the preferred repairing fillers (epoxy resin) and some blocks from one of the world's most famous marble production area (Carrara quarries in Italy). The selected blocks were paired in a laboratory by overlapping one over the other after inserting very thin spacers in order to simulate air-filled fractures. Fractures were investigated with a 3 GHz ground-penetrating radar (GPR) before and after the resin injections to measure the amplitude reduction expected when the resin substitutes the air. The results were compared with theoretical predictions based on the reflection coefficient predicted according to the thin bed theory. A field test was also performed on a naturally fractured marble block selected along the Carrara shore. Both laboratory and field tests validate the GPR as an effective tool for the quality control of resin injections, provided that measurements include proper calibration tests to control the amplitude instabilities and drift effects of the GPR equipment. The method is accurate enough to distinguish the unfilled fractures from the partially filled fractures and from the totally filled fractures. An automatic algorithm was developed and successfully tested for the rapid quantitative analysis of the time-lapse GPR profiles collected before and after the injections. The whole procedure is mature enough to be proposed to the marble industry to improve the effectiveness of repair interventions and to reduce the waste of natural stone reserves.

Comparison of Imaging Radar Configurations for Roadway Inspection and Characterization.

This paper investigates the performance of a wide variety of radar imaging modes, such as nadir-looking B-scan, or side-looking synthetic aperture radar tomographic acquisitions, performed in both back- and forward-scattering geometries, for the inspection and characterization of roadways. Nadir-looking B-scan corresponds to a low-complexity mode exploiting the direct return from the response, whereas side-looking configurations allow the utilization of angular and polarimetric diversity in order to analyze advanced features. The main objective of this paper is to evaluate the ability of each configuration, independently of aspects related to operational implementation, to discriminate and localize shallow underground defects in the wearing course of roadways, and to estimate key geophysical parameters, such as roughness and dielectric permittivity. Campaign measurements are conducted using short-range radar stepped-frequency continuous-waveform (SFCW) devices operated in the C and X bands, at the pavement fatigue carousel of Université Gustave Eiffel, over debonded areas with artificial defects. The results indicate the great potential of the newly proposed forward-scattering tomographic configuration for detecting slight defects and characterizing roadways. Case studies, performed in the presence of narrow horizontal heterogeneities which cannot be detected using classical B-scan, show that both the coherent integration along an aperture using the back-projection algorithm, and the exploitation of scattering mechanisms specific to the forward-looking bistatic geometry, allows anomalous echoes to be detected and further characterized, confirming the efficacy of radar imaging techniques in such applications.

Utilizing Drone-Based Ground-Penetrating Radar for Crime Investigations in Localizing and Identifying Clandestine Graves.

The decomposition of a body is influenced by burial conditions, making it crucial to understand the impact of different conditions for accurate grave detection. Geophysical techniques using drones have gained popularity in locating clandestine graves, offering non-invasive methods for detecting surface and subsurface irregularities. Ground-penetrating radar (GPR) is an effective technology for identifying potential grave locations without disturbance. This research aimed to prototype a drone system integrating GPR to assist in grave localization and to develop software for data management. Initial experiments compared GPR with other technologies, demonstrating its valuable applicability. It is suitable for various decomposition stages and soil types, although certain soil compositions have limitations. The research used the DJI M600 Pro drone and a drone-based GPR system enhanced by the real-time kinematic (RTK) global positioning system (GPS) for precision and autonomy. Tests with simulated graves and cadavers validated the system's performance, evaluating optimal altitude, speed, and obstacle avoidance techniques. Furthermore, global and local planning algorithms ensured efficient and obstacle-free flight paths. The results highlighted the potential of the drone-based GPR system in locating clandestine graves while minimizing disturbance, contributing to the development of effective tools for forensic investigations and crime scene analysis.

Automatic Life Detection Based on Efficient Features of Ground-Penetrating Rescue Radar Signals.

Good feature engineering is a prerequisite for accurate classification, especially in challenging scenarios such as detecting the breathing of living persons trapped under building rubble using bioradar. Unlike monitoring patients' breathing through the air, the measuring conditions of a rescue bioradar are very complex. The ultimate goal of search and rescue is to determine the presence of a living person, which requires extracting representative features that can distinguish measurements with the presence of a person and without. To address this challenge, we conducted a bioradar test scenario under laboratory conditions and decomposed the radar signal into different range intervals to derive multiple virtual scenes from the real one. We then extracted physical and statistical quantitative features that represent a measurement, aiming to find those features that are robust to the complexity of rescue-radar measuring conditions, including different rubble sites, breathing rates, signal strengths, and short-duration disturbances. To this end, we utilized two methods, Analysis of Variance (ANOVA), and Minimum Redundancy Maximum Relevance (MRMR), to analyze the significance of the extracted features. We then trained the classification model using a linear kernel support vector machine (SVM). As the main result of this work, we identified an optimal feature set of four features based on the feature ranking and the improvement in the classification accuracy of the SVM model. These four features are related to four different physical quantities and independent from different rubble sites.

Quantification of Humidity and Salt Detection in Historical Building Materials via Broadband Radar Measurement.

For the investigation of moisture and salt content in historic masonry, destructive drilling samples followed by a gravimetric investigation is still the preferred method. In order to prevent the destructive intrusion into the building substance and to enable a large-area measurement, a nondestructive and easy-to-use measuring principle is needed. Previous systems for moisture measurement usually fail due to a strong dependence on contained salts. In this work, a ground penetrating radar (GPR) system was used to determine the frequency-dependent complex permittivity in the range between 1 and 3 GHz on salt-loaded samples of historical building materials. By choosing this frequency range, it was possible to determine the moisture in the samples independently of the salt content. In addition, it was possible to make a quantitative statement about the salt level. The applied method demonstrates that with ground penetrating radar measurements in the frequency range selected here, a salt-independent moisture determination can be carried out.