China’s first Mars mission (Tianwen-1) successfully touched down in the Utopia Planitia of Mars with a rover subsurface penetrating radar (RoPeR) carried for exploring the regolith dielectric properties. Hyperbolic fitting is a conventional method to infer the subsurface material relative permittivity from ground penetrating radar data (GPR). However, it is difficult to directly extract valid hyperbolas from the RoPeR data. Inspired by the recently developed deep learning-based geophysical inversion method to estimate of the subsurface wave velocities through GPR data, an improved deep learning architecture is proposed to infer the Martian regolith relative permittivity from the RoPeR data, with self-attention and cascade modules are introduced into the network. The improved cascade and self-attention modules can improve the inversion efficiency and mitigate the scatter-diffraction effect of the predicted results. The inverted relative permittivity from the first 60 ns of the RoPeR data demonstrates an approximate line with a mean value of 4.73 in the regolith of interest. The very limited fluctuation of relative permittivity implies that no explicit stratification existing in the investigated regolith, agreeing with the previous studies.

The performance of Ground Penetrating Radar (GPR) is greatly influenced by the cross-coupling between the transmitter and the receiver, and the response from the background. Their combined effect often masks the weaker target signals, especially in cases where shallow buried targets are present. Moreover, errors in velocity estimation, results to over/under migrated images, which further compromises the reliability of GPR, especially in case of non-homogeneous media. Therefore, background clutter suppression and velocity estimation are both pivotal for effectively locating targets. For this purpose, a novel deep learning scheme for background clutter prediction was developed, where a two joint artificial neural networks architecture (ANNs) combined with principal component analysis (PCA) is implemented. In the suggested scheme, the first network predicts the background response, which is subsequently subtracted, while the second network estimates the background permittivity and conductivity. Subsequently, the permittivity profile along the measurement line is used as input in Reverse-Time Migration to focus the signal without the need of hyperbola fitting and homogeneity assumptions. The training data were generated synthetically using the Finite-difference time-domain (FDTD) method. A model of a real GPR antenna is used in the simulations, making the scheme applicable to real data. The efficiency of the proposed method is validated using both numerical and real data, with successful predictions in all cases, demonstrating its ability to perform well even when tested with previously unseen real complex scenarios. Via a series of examples, the proposed scheme was proven superior to commonly used background removal techniques and conventional migration.

Numerical modelling of Ground Penetrating Radar (GPR), such as the finite-difference time-domain (FDTD) method, has been extensively used to enhance the interpretation of GPR data and as a key component of full-waveform inversion (FWI). A major drawback of numerical solvers, especially within the context of FWI, is that they are still computationally expensive requiring often unattainable computational resources and access to high performance computing (HPC). In this work, we present a near real-time deep learning forward solver for GPR data that can generate entire B-scans, given certain model parameters as inputs. The machine learning (ML) model is tuned for reinforced concrete slab scenarios, but the same rational can be applied in a straightforward manner to other applications as well. Training was performed using entirely synthetic data, where a three-dimensional (3D) digital twin based on the 2000 MHz “palm” antenna from Geophysical Survey Systems, Inc. (GSSI) was included in FDTD simulations for the training set. The accuracy of the deep learning solver is demonstrated with both synthetic and real data from reinforced concrete slabs. The predicted ML responses were in a very good agreement with FDTD, showing a high degree of accuracy. The ML solver is then used as part of a FWI algorithm to characterize the concrete slab and estimate the depth and radius of the buried rebars. Coupled FWI with an ML-based forward solver results in significantly less execution times compared to conventional FWI using numerical solvers. The high accuracy of the proposed FWI, combined with the efficiency and speed of the ML-based forward solver, make the proposed scheme an ideal tool for characterizing concrete structures in non-destructive testing.

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.

Internal decays in trees can rapidly escalate into a full decomposition of the inner structural layer, i.e., the “heartwood” layer, due to the action of aggressive diseases and fungal infections. This process leads to the formation of big cavities and hollows, which remain surrounded by the sapwood layer only. Estimating the thickness of the sapwood layer with a high degree of accuracy is therefore crucial for correct assessment of the structural integrity of hollow trees, as well as an extremely challenging task. In this context, ground-penetrating radar (GPR) has proven effective in providing details of the internal structure of trees. Nevertheless, the existing GPR processing methods still offer limited information on their internal configuration. This study investigates the effectiveness of GPR enhanced by a microwave tomography inversion approach in the assessment of hollow trees. To this aim, a living hollow tree was investigated by performing a set of pseudocircular scans along the bark perimeter with a hand-held common-offset GPR system. The tree was then felled, and sections were cut for testing purposes. A dedicated data processing framework was developed and tested through numerical simulations of hollow tree sections. The internal structure of the real trunk was therefore reconstructed via a tomographic imaging approach and the outcomes were quantitatively analyzed by way of comparison with the real sections’ main geometric features. The tomographic approach has proven very accurate in locating the sapwood–cavity interface and in the evaluation of the sapwood layer thickness, with a centimeter prediction accuracy.

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.

Ash dieback, acute oak decline (AOD), and Xylella Fastidiosa are emerging infectious diseases (EIDs) that have spread rapidly in European forests during the last decade. Quarantine measurements have mostly failed to repress the outbreaks and millions of trees have already been infected. Identifying infected trees in a nondestructive manner is of high importance for monitoring, managing, and preventing EIDs. The aim of this article is to examine the capabilities of ground penetrating radar (GPR) on evaluating the internal structure of tree trunks and detecting tree decay associated with EIDs. Traditionally used processing schemes tuned for GPR line acquisitions are modified accordingly to be compatible with the new measurement configurations. In particular, a detection framework is presented based on a modified Kirchhoff and a reverse-time migration. Both of the aforementioned methodologies are compatible with measurements taken along closed irregular curves assuming a homogeneous permittivity distribution. To that extent, prior to migration, a novel focal criterion is used that estimates the bulk permittivity of the host medium from the measured B-scans. The suggested detection scheme is successfully tested on both numerical and laboratory measurements, indicating that GPR has the potential to become a coherent and practical tool for detecting tree decay associated with EIDs.

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.

Ground penetrating radar (GPR) is traditionally applied to smooth surfaces in which the assumption of half-space is an adequate approximation that does not deviate much from reality. Nonetheless, using GPR for internal structure characterization of tree trunks requires measurements on an irregularly shaped closed curve. A typical hyperbola fitting has no physical meaning in this new context since the reflection patterns are strongly associated with the shape of the tree trunk. Instead of a clinical hyperbola, the reflections give rise to complex-shaped patterns that are difficult to be analyzed even in the absence of clutter. In this paper, a novel processing scheme is described which can interpret complex reflection patterns assuming a circular target subject to any arbitrary shaped surface. The proposed methodology can be applied using commercial hand-held antennas in real time, avoiding computationally costly tomographic approaches that require the usage of custom-made bespoke antenna arrays. The validity of the current approach is illustrated both with numerical and real experiments.

Ground penetrating radar (GPR) using a 3D antenna array is employed for data acquisition over an area contaminated with landmine simulants. The performance of different processing methods applied in 1D, 2D and 3D radiograms are evaluated on real data obtained from two different test sites. Preliminary results are presented on the effectiveness of 3D GPR and on the applicability and limitations of widely used processing schemes.

A regression model is developed in order to estimate in real time the signal-to-clutter ratio (SCR) for landmine detection using ground-penetrating radar. Artificial neural networks are employed in order to express SCR with respect to the soil's properties, the depth of the target, and the central frequency of the pulse. The SCR is synthetically evaluated for a wide range of diverse and controlled scenarios using the finite-difference time-domain method. Fractals are used to describe the geometry of the soil's heterogeneities as well as the roughness of the surface. The dispersive dielectric properties of the soil are expressed with respect to traditionally used soil parameters, namely, sand fraction, clay fraction, water fraction, bulk density, and particle density. Through this approach, a coherent and uniformly distributed training set is created. The overall performance of the resulting nonlinear function is evaluated using scenarios which are not included in the training process. The calculated and the predicted SCR are in good agreement, indicating the validity and the generalization capabilities of the suggested framework.