Ground-penetrating radar (GPR) facilitates the detection and localization of subsurface structural anomalies in critical transport infrastructure (e.g., tunnels), better informing targeted maintenance strategies. However, conventional fixed-directional systems suffer from limited coverage—especially of less-accessible structural aspects (e.g., crowns)—alongside the unclear visual output of anomaly spatial profiles, both for physical and simulated datasets. To tackle these limitations, new hybrid-rotational GPR utilizes novel 360° orientable air-launched antennas to increase subsurface coverage, principally in tunnels. Prototype systems currently lack a versatile workflow to generate practical visual output for surveyors. This study develops a versatile visualization workflow based on entirely open-access tools, returning 3-D spatial profiles of subsurface anomalies in: 1) simulated; 2) fixed-directional; and 3) hybrid-rotational GPR datasets. Work includes the development of two unique hybrid-rotational GPR systems, for laboratory and in-field data collection, respectively. Following initial 3-D grid alignment and smoothing, data undergo 3-D Stolt migration, normalization, and proximal clustering via Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). This returns segmented point subsets associated with suspected structural anomalies. Finally, 3-D convex hulls are recovered using the QuickHull method. Detection and localization performance is first appraised through numerical simulation in open-source software gprMax. Practical laboratory experimentation follows, with both commercial fixed-directional systems and developed hybrid-rotational GPR, before in-field demonstration on a large-scale, tunnel subsurface analog. In each experiment, all targets were successfully identified within returned 3-D visualizations of hybrid-rotational GPR datasets. Moreover, the spatial profiles were consistently observed to be accurately localized within decimeter-length scales of known target locations. Overall, the advances presented in this work both facilitate and demonstrate the significant practical potential of new hybrid-rotational GPR technology as a basis for future subsurface surveying of critical transport infrastructure.

Radio-echo sounding (RES) reveals patches of high backscatter in basal ice units, which represent distinct englacial features in the bottom parts of glaciers and ice sheets. Their material composition and physical properties are largely unknown due to their direct inaccessibility but could provide significant information on the physical state as well as on present and past processes at the ice-sheet base. Here, we investigate the material properties of basal ice units by comparing measured airborne radar data with synthetic radar responses generated using electromagnetic (EM) forward modeling. The observations were acquired at the onset of the Jutulstraumen Ice Stream in western Dronning Maud Land (DML) (East Antarctica) and show strong continuous near-basal reflections of up to 200-m thickness in the normally echo-free zone (EFZ). Based on our modeling, we suggest that these high-backscatter units are most likely composed of point reflectors with low dielectric properties, suggesting thick packages of englacial entrained debris. We further investigate the effects of entrained particle size, and concentration in combination with different dielectric properties, which provide useful information to constrain the material composition of radar-detected units of high backscatter. The capability and application of radar wave modeling in complex englacial environments is therefore a valuable tool to further constrain the composition of basal ice and the physical conditions at the ice base.

Hyperbola fitting is a mainstream interpretation technique used in ground penetrating radar (GPR) due to its simplicity and relatively low computational requirements. Conventional hyperbola fitting is based on the assumption that the investigated medium is a homogeneous half-space, and that the target is an ideal reflector with zero radius. However, the zero-radius assumption can be easily removed by formulating the problem in a more generalized way that considers targets with arbitrary size. Such approaches were recently investigated in the literature, suggesting that hyperbola fitting can be used not only for estimating the velocity of the medium, but also for estimating the radius of subsurface cylinders, a very challenging problem with no conclusive solution to this day. In this paper, through a series of synthetic and laboratory experiments, we demonstrate that for practical GPR survey, hyperbola fitting is not suitable for simultaneously estimating both the velocity of the medium and the size of the target, due to its inherent non-uniqueness, making the results unreliable and sensitive to noise.

Full-waveform inversion (FWI) is considered one of the most promising interpretation tools for hydrogeological applications using ground-penetrating radar. However, FWI has had limited practical uptake for several reasons: large computational requirements, an inability to reconstruct loss mechanisms of soil, and the need for a good initial starting model. We aim to address these issues via a novel FWI subject to a fractally correlated distribution of water. Initially, the dispersive properties of the soil are expressed as a function of the water fraction using a semiempirical model. This approach means that the permittivity, conductivity, and relaxation mechanisms are all correlated, and therefore, sensitivity problems between the permittivity and loss mechanisms no longer affect the performance of FWI. Subsequently, the distribution of the water fraction is constrained to follow a fractal geometry. Fractal-correlated noise is then compressed using a principal component analysis (PCA) in order to further reduce the number of the system’s unknowns and accelerate FWI. PCA reduces the volume and dimensions of the optimization space, and thus, initialization is no longer necessary. Finally, a novel measurement configuration is suggested that uses superposition with all the individual measurements in order to reduce the number of forward models that need to be executed for every iteration of FWI. These enhancements substantially reduce the computational requirements of FWI and therefore eliminate the need for high-performance computers and time-consuming algorithms. The proposed scheme has been successfully tested with several numerical case-studies, which indicates the potential of this approach to become a commercially appealing interpretation tool for hydrogeology.

Ground penetrating radar (GPR) is a well-established tool for detecting and locating reinforcing bars (rebars) in concrete structures. However, using GPR to quantify the diameter of rebars is a challenging problem that current processing approaches fail to tackle. To that extent, we have developed a novel machine learning framework that can estimate the diameter of the investigated rebar within the resolution range of the employed antenna. The suggested approach combines neural networks and a random forest regression and has been trained entirely using synthetic data. Although the training process relied only on numerical training sets, nonetheless, the suggested scheme is successfully evaluated with real data indicating the generalization capabilities of the resulting regression. The only required input of the proposed technique is a single A-scan, avoiding laborious measurement configurations and multisensor approaches. In addition, the results are provided in real time and making this method practical and commercially appealing.

Finite-difference time-domain forward modeling of ground-penetrating radar (GPR) is becoming regularly used in model-based interpretation methods, such as full-waveform inversion (FWI) and machine learning schemes. Oversimplifications in such forward models can compromise the accuracy and realism with which real GPR responses can be simulated, which degrades the overall performance of interpretation techniques. A forward model must be able to accurately simulate every part of the GPR problem that affects the resulting scattered field. A key element, especially for near-field applications, is the antenna system. Therefore, the model must contain a complete description of the antenna, including the excitation source and waveform, the geometry, and the dielectric properties of any materials in the antenna. The challenge is that some of these parameters are not known or cannot be easily measured, especially for commercial GPR antennas that are used in practice. We present a novel hybrid linear/nonlinear FWI approach that can be used, with only knowledge of the basic antenna geometry, to simultaneously optimize the dielectric properties and excitation waveform of the antenna and minimize the error between real and synthetic data. The accuracy and stability of our proposed methodology are demonstrated by successfully modeling a 1.5-GHz commercial antenna from Geophysical Survey Systems, Inc. Our framework allows accurate models of GPR antennas to be developed without requiring detailed knowledge of every component of the antenna. This is significant because it allows commercial GPR antennas, regularly used in GPR surveys, to be more readily simulated.

The simulation, or forward modeling, of ground penetrating radar (GPR) is becoming a more frequently used approach to facilitate the interpretation of complex real GPR data, and as an essential component of full-waveform inversion (FWI). However, general full-wave 3-D electromagnetic (EM) solvers, such as the ones based on the finite-difference time-domain (FDTD) method, are still computationally demanding for simulating realistic GPR problems. We have developed a novel near-real-time, forward modeling approach for GPR that is based on a machine learning (ML) architecture. The ML framework uses an innovative training method that combines a predictive principal component analysis technique, a detailed model of the GPR transducer, and a large data set of modeled GPR responses from our FDTD simulation software. The ML-based forward solver is parameterized for a specific GPR application, but the framework can be applied to many different classes of GPR problems. To demonstrate the novelty and computational efficiency of our ML-based GPR forward solver, we used it to carry out FWI for a common infrastructure assessment application-determining the location and diameter of reinforcement bars in concrete. We tested our FWI with synthetic and real data and found a good level of accuracy in determining the rebar location, size, and surrounding material properties from both data sets. The combination of the near-real-time computation, which is orders of magnitude less than what is achievable by traditional full-wave 3-D EM solvers, and the accuracy of our ML-based forward model is a significant step toward commercially viable applications of FWI of GPR.

Horizontally stratified media are commonly used to represent naturally occurring and man-made structures, such as soils, roads, and pavements, when probed by ground-penetrating radar (GPR). Electromagnetic (EM) wave scattering from such multilayered media is dependent on the roughness of the interfaces. In this paper, we developed a closed-form asymptotic EM model considering random rough layers based on the scalar Kirchhoff-tangent plane approximation (SKA) model that we combined with planar multilayered media Green's functions. In order to validate our extended SKA model, we conducted simulations using a numerical EM solver based on the finite-difference time-domain (FDTD) method. We modeled a medium with three layers-a base layer of perfect electric conductor (PEC) overlaid by two layers of different materials with rough interfaces. The reflections at the first and at the second interface were both well reproduced by the SKA model for each roughness condition. For the reflection at the PEC surface, the extended SKA model slightly overestimated the reflection, and this overestimation increased with the roughness amplitude. Good agreement was also obtained between the FDTD simulation input values and the inverted root mean square (rms) height estimates of the top interface, while the inverted rms heights of the second interface were slightly overestimated. The accuracy and the performances of our asymptotic forward model demonstrate the promising perspectives for simulating rough multilayered media and, hence, for the full waveform inversion of GPR data to noninvasively characterize soils and materials.

Directly measuring the radiation characteristics of Ground Penetrating Radar (GPR) antennas in environments typically encountered in GPR surveys, presents many practical difficulties. However it is very important to understand how energy is being transmitted and received by the antenna, especially for areas of research such as antenna design, signal processing, and inversion methodologies. To overcome the difficulties of experimental measurements, we used an advanced modelling toolset to simulate detailed three-dimensional Finite-Difference Time-Domain (FDTD) models of GPR antennas in realistic environments. A semi-empirical soil model was utilised, which relates the relative permittivity of the soil to the bulk density, sand particle density, sand fraction, clay fraction and volumetric fraction of water. The radiated energy from the antenna was studied in lossless homogeneous dielectrics as well as, for the first time, in lossy heterogeneous environments. Significant variations in the magnitude and pattern shape were observed between the lossless homogeneous and lossy heterogeneous environments. Also, despite clear differences in time domain responses from simulations that included only an infinitesimal dipole source model and those that used the full antenna model, there were strong similarities in the radiated energy distributions.

The Reuse of code artefacts can make development quicker, cheaper and more robust, but is complex and has many pitfalls: Code artefacts must exist, be available, be found, be understood, be valid and finally be integrated. Many software developers try to ensure that artefacts meet these requirements through a process of making code "reusable." If the generation of reusable artefacts from developed code could be automated development time could be reduced. Code artefacts must be gathered and indexed automatically with no extra work. Our review of requirement management and version control tools identified a way to generate reuse artefacts through traceability, using information from the requirement management system and code stored in the version control system. A prototype to search these indexed artefacts showed that this tool could substantially reduce development time for simple tasks, by up to 63%.