Ground Penetrating Radar Simulation Research Articles

A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection

Ground penetrating radar (GPR) systems are effective sensors for discovering various types of objects buried underground, such as military mines, metal objects, and pieces of underground infrastructures. A GPR system can be manually operated by a human or can be an integral part of a host platform. The host platform may be semi- or fully autonomous and may operate in different environments such as land vehicles or more recently air-borne drones. One challenge for the fully or semi-autonomous host platforms in particular is to find an efficient search procedure that would reduce the operation time and optimize resource utilization. Most of the current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper, we introduce a method that combines a coarse and therefore relatively low cost initial search pattern with a Reinforcement Learning (RL) driven efficient navigation path for eventual target detection, by exploiting the signal processing pipeline of the onboard GPR. We illustrate the applicability of the method using a well-known, high fidelity GPR simulation environment and a novel RL framework. Our results suggest that combination of a coarse navigation scheme and an RL-based training procedure based on GPR scan returns can lead to a more efficient target discovery procedure for host platforms.

Research on reinforcement corrosion detection method based on the numerical simulation of ground-penetrating radar

Both the reinforcement depth and the electromagnetic properties of the concrete cover dramatically affect the steel reinforcement reflection wave amplitude in ground-penetrating radar (GPR), resulting in significant errors when evaluating the corrosion status of the reinforcement using the reflection wave amplitude. In response to this issue, this paper proposes a normalized amplitude–two-way travel time–relative permittivity (A-TWTT-εr) method, which, compared to traditional single-shot detection methods, enhances the effectiveness of steel reinforcement corrosion detection through 3D space. The amplitude and TWTT parameters are extracted using the A-scan waveform at the vertex of the steel reinforcement, while εr is determined through hyperbolic feature scatter fitting. Finally, the feasibility of the proposed method was verified using COMSOL numerical simulation data. The results showed that the proposed A-TWTT-εr amplitude-correction method could simultaneously correct for the attenuation effect of differences in the reinforcement depth and concrete electromagnetic properties on the amplitude. The proposed method could enable single-shot corrosion detection by GPR, yet further validation is needed in complex experimental and on-site environments.

Mutual coupling effects in 2×2 antenna array for ground penetrating radar on multilayered soil

<span>Caral stands as the Americas’ oldest city, boasting a heritage spanning 5,000 years. Over time, various natural forces have woven a complex geological stratum. To gain a deeper understanding of the Caral civilization, non-intrusive exploration methodologies like ground penetrating radar (GPR) are beginning to be used. This method safeguards the integrity of ancient subterranean remains. A GPR system is in development, tailored to the <br /> [200-500] MHz range, employing a 2×2 antenna array with dual polarization. These features enhance resolution without compromising penetration depth. However, using multiple antennas within complex, multi-layered environments introduce impedance band constraints and exacerbates antenna coupling issues. This study assesses the coupling of two antenna candidates: the Vivaldi with defected ground structures (DGS) and the log periodic dipole array (LPDA). The scattering parameters show that the LPDA antenna performed better considering measured and simulated data. Cross-polarization exhibited a broader bandwidth in the LPDA antenna, evident in both simulated and measured data. Additionally, a comprehensive comparison of GPR simulations for each antenna type within an 11-level multilayer medium, with different electromagnetic properties, further highlights LPDA. This antenna boasts a 209 MHz bandwidth and a coupling better than -23 dB for the cross-polarization configuration, firmly showing its best performance.</span>

TunGPR: Enhancing data-driven maintenance for tunnel linings through synthetic datasets, deep learning and BIM

Non-destructive Testing (NDT) techniques and data-driven technologies are increasingly applied in underground infrastructure maintenance, which can facilitate predictive monitoring for informed decision-making. Ground Penetrating Radar (GPR) is extensively utilised in rapid condition assessment of tunnel linings, particularly for detecting defects that lead to unforeseen changes of dielectric properties of materials. In this paper, a prototyped framework is proposed, namely TunGPR, for GPR-based tunnel lining assessment by incorporating Building Information Modelling (BIM), synthetic database and deep learning-enabled interpretation. The first module integrates laser-scanned point clouds and GPR Scan-to-BIM of tunnel lining with geological model. Subsequently, interoperability is achieved between the geo-integrated BIM and GPR simulation software. From the dielectric model retrieved from the BIM model, a database is established, considering a variety of condition combinations (i.e., voids, cavities, delamination, and water intrusion) leveraging domain randomisation and Finite-Difference Time-Domain (FDTD) modelling, as well as monitored field data. The dataset is then fed into the diagnostic module underpinned by a dual-rotational Convolutional Neural Network (CNN) that is customised to enhance accuracy and automation of hyperbola detection. Lastly, a preliminary risk assessment matrix is implemented into the BIM model for data management and action prioritisation. These efforts serve as an initial step to validate the feasibility and effectiveness of the GPR-enabled data-driven maintenance for tunnel linings in a BIM-centred framework.

Automatic detection and location of pavement internal distresses from ground penetrating radar images based on deep learning

Ground penetrating radar (GPR) has been prevailingly applied in nondestructive testing of internal distresses within asphalt pavement. However, the interpretation of abnormal echo signals in massive pavement GPR images is time-consuming and labor-intensive, and prone to misjudgment. To address this issue, a three-step method was proposed for the automatic detection and location of the internal distresses (eg: crack and debonding) echo features from GPR images. The on-site and numerical simulated GPR images of the asphalt pavement together formed the dataset required for the subsequent deep learning models. The first step is that You Only Look Once version 3 (YOLOv3) model predicted rectangular boxes for enclosing the internal distress echo features from GPR images. The second step involves developing the U-net models to segment the internal distress echo feature pixels in the cropped rectangular boxes. The last step is that the median points of the segmentation of the internal distress echo feature were fitted with a theoretical curve equation, to estimate the location of the internal distress. The proposed method has shown that the comprehensive detection accuracy of the internal distress echo features can reach 96.99%, the semantic segmentation accuracies of the internal distress echo feature pixels are not less than 0.856, and the average deviation of the estimated depths of the internal distresses is 3.25 cm. The research method makes further advances in accurately and automatically detecting and locating the internal distresses of asphalt pavement.

A Simple Model Generator for Radar Sounding Applications Based on Stochastic Media Model and gprMax

Ground penetrating radar (GPR), or more generically called radar sounding, is an established technique for probing subsurface objects and sublayer structures. Simulating pre-constructed models and comparing with the real-world observations can contribute to retrieve the geometrical and physical properties of subsurface objects. However, most GPR simulations are limited to geometrically simple models with homogeneous physical properties of subsurface media which are generally unable to represent the real-world situations. In this paper, we develop a simple model generator by combining the stochastic media model and gprMax, an open-source software for electromagnetic simulations, to jointly generate models with geometrically random fragments, which are forward models with minimum human intervention. These easily prepared and simulated models can enrich the model training datasets which can be further utilized in machine learning for GPR retrieval algorithms.

Three Dimension Ground Penetrating Radar and Data Processing for Pavement Collapse Detection

Ground-penetrating radar (GPR) is a non-destructive testing exploration technology with the advantages of high efficiency and high resolution, and is widely used in roadbed detection and disease detection. With the increasing demand for road detection, traditional handheld two-dimensional GPR detection instruments can no longer meet the normal urban road detection requirements. Therefore, new efficient vehicle-mounted three-dimensional GPR systems have emerged and been put into use. This article mainly introduces some three-dimensional GPR instruments and systems that have been put into use, analyzes their composition structure, usage characteristics, compares their similarities and differences, and data processing methods, which can provide a certain reference for the forward simulation and data processing of three-dimensional GPR.

Unsupervised deep learning-based ground penetrating radar image translation for internal defect recognition of underground engineering structures

Anomaly detection of internal defects in underground engineering structures is critical. This paper proposes an unsupervised deep learning image-to-image translation method tailored for ground penetrating radar (GPR) images. The proposed model can translate real-world GPR images to simulated ones. In this manner, labeling real GPR images is not necessary, and only the target detection model trained on simulated GPR images is required to directly identify defects in real GPR images. The unsupervised deep learning network introduces geometry-consistency constraints into the CycleGAN, which largely prevents the problem of semantic distortion in translation. Validation of the proposed method was performed using GPR data collected in various scenarios using GPR of different center frequencies and manufacturers. Moreover, to verify its adaptability and feasibility for defect recognition, commonly used deep learning-based defect recognition methods, which were trained only on simulated GPR images, were used to detect the translated GPR images. The findings indicate that the type and location of internal defects in translated GPR images can be accurately identified using the proposed method.

Backcalculation of Thickness and Dielectric Properties of Asphalt Concrete Using Numerical Simulation of Ground Penetrating Radar

Ground penetrating radar (GPR) is an essential tool for the non-destructive evaluation of pavement conditions. The accuracy of GPR analysis depends on the correct prediction of the layer dielectric property. In this study, a finite-difference time domain simulation-based two-step backcalculation algorithm was developed to determine the layer thickness and dielectric constant from air-coupled GPR data. The two-step backcalculation algorithm was developed by combining a surrogate-based global optimizer and artificial neural network for efficiency and accuracy. The GPR backcalculated results were compared with the field conditions of three pavement sections. The results show that the backcalculation algorithm could predict the layer thicknesses with almost 95% to 99.7% accuracy, whereas the prediction accuracy of the dielectric constants was between 90% and 98.5%. Furthermore, it was observed that the variations of dielectric constants in each lift of the asphalt concrete layer could be used to predict the presence of probable distress in the field.

A tilting system design and simulation of ground penetrating radar

In this paper, a tilting system of ground penetrating radar by 5R1C 6-bar linkage mechanism is proposed. The kinematic function of hydraulic cylinder and the output angle is given in explicit format. The quasi-static formulation of the tilting system is proposed to calculate the shaft and revolute force. The simulation of mechanism via ADAMS and an experiment is made to verify the design. The maximum stress is exposed in curved shaft and the worst working condition is the tilt condition. It proves the validity of tilting system that both mechanism and hydraulic system work well in the loading condition and unloading condition.

Fast 3D ground penetrating radar simulations for glaciers

Modeling ground penetrating radar (GPR) reflection data on glaciers with methods that require the discretization of the full subsurface domain is extremely computationally expensive because of the combination of a large domain size and the comparatively short wavelength of the signal. To address this issue, we build on and extend a previously proposed approach based on the assumption of a homogeneous background medium (ice) in which various scattering objects (e.g., crevasses, channels, boulders) are embedded far from each other such that multiple scattering can be ignored. The glacier bed, below which no scatterers are assumed to exist, represents the lower limit of the modeling domain. With this method, the two-way propagation of the radar waves through the ice is simulated in a semi-analytical way, whereby scattering surfaces are represented with a set of planar elements of different electric and reflective properties, allowing a wide range of objects to be simulated. As we also take the antenna radiation pattern at the air-ice interface into account, this simple algorithm is able to produce realistic 3D GPR data in a fast and memory efficient way. In this study, we validate the presented algorithm with an analytical solution for a layered model, and we simulate radar data for a model of the Otemma glacier in Switzerland featuring a realistic topography of the glacier bed and a subglacial channel.

A High-Efficiency Spectral Element Method Based on CFS-PML for GPR Numerical Simulation and Reverse Time Migration

Improving the accuracy and efficiency of the numerical simulation of ground penetrating radar (GPR) becomes a pressing need with the rapidly increased amount of inversion data and the growing demand for migration imaging quality. In this article, we present a numerical spectral element time-domain (SETD) simulation procedure for GPR forward modeling and further apply it to the reverse time migration (RTM) with complex geoelectric models. This approach takes into account the flexibility of the finite element methods and the high precision of the spectral methods. Meanwhile, in this procedure, the complex frequency shifted perfectly matched layer (CFS-PML) is loaded to effectively suppress the echo at the truncated boundary, and the per-element GPU parallel framework used can achieve up to 5.7788 times the efficiency compared with the CPU calculation. The experiments on SETD spatial convergence and CFS-PML optimal parameter selection showed that, under the same degree of freedom, the SETD offered substantially better accuracy compared with the traditional FETD. The experiments on RTM of different profiles with different orders of SETD via a complex geoelectric model verify the universality of the algorithm. The results indicate that the RTM imaging effect has been significantly improved with the increase of SETD order. It fully proves the great potential of efficient and high-precision SETD simulation algorithm in the RTM imaging direction and shows certain guiding significance for underground target structure exploration.

2D electromagnetic simulation for ground penetrating radar with a topographic ground surface by the curvilinear collocated-grid finite-difference method combined with equivalent field method

In this paper, we extend the curvilinear collocated-grid finite-difference method, which has been developed in the study of seismology, to the 2D electromagnetic simulation of ground penetrating radar (GPR). The method combines curvilinear coordinate and collocated grid, so it can better describe the geometry of the irregular interfaces than traditional finite difference method and is more efficient than finite element method which is suitable for complex geometries. Due to the differences in interfacial condition between elastic waves and electromagnetic waves, a novel scheme of equivalent electromagnetic fields is introduced to explicitly ensure discontinuous interfacial boundary conditions with collocated grid. Furthermore, this approach eliminates the need for orthogonality of the grid at the interface, which reduces the complexity of mesh generation. The proposed method is verified for a series of ground penetrating radar application models, by comparing synthetic waveforms with independent reference solutions. The perfect consistency of the comparisons indicates the accuracy of our new method in the simulation of GPR. Compared with the second-order gprMax, the proposed method is proved to be more efficient in terms of computing time and memory. Combination of collocated grid and the novel equivalent field method leads to reliable and accurate solutions of EM fields even on the ground surface.

Deep-Learning-Based Method for Estimating Permittivity of Ground-Penetrating Radar Targets

Correctly estimating the relative permittivity of buried targets is crucial for accurately determining the target type, geometric size, and reconstruction of shallow surface geological structures. In order to effectively identify the dielectric properties of buried targets, on the basis of extracting the feature information of B-SCAN images, we propose an inversion method based on a deep neural network (DNN) to estimate the relative permittivity of targets. We first take the physical mechanism of ground-penetrating radar (GPR), working in the reflection measurement mode as the constrain condition, and then design a convolutional neural network (CNN) to extract the feature hyperbola of the underground target, which is used to calculate the buried depth of the target and the relative permittivity of the background medium. We further build a regression network and train the network model with the labeled sample set to estimate the relative permittivity of the target. Tests were carried out on the GPR simulation dataset and the field dataset of underground rainwater pipelines, respectively. The results show that the inversion method has high accuracy in estimating the relative permittivity of the target.

Second-order symplectic PRK scheme for ground-penetrating radar simulations in dispersive materials

An accurate and effective forward model is helpful in interpreting measured ground-penetrating radar (GPR) data. In this paper, an explicit second-order symplectic partitioned Runge–Kutta scheme (SPRK) with Higdon's absorbing boundary condition is developed to simulate GPR wave propagation in dispersive media. For this purpose, the first-order Debye dispersion model is successfully applied to the second-order SPRK method. A single-layer dispersive medium model and a three-layer pavement structure model are simulated, in order to verify the accuracy and efficiency of the second-order SPRK method, as well as to clarify the effect of dispersion on the GPR echo signal. The numerical simulation results demonstrate that the proposed algorithm not only can maintain the simulation accuracy, but can also save about 19% of the calculation time, when compared with the FDTD algorithm, in a dispersive medium. We also obtain the GPR profile of a pavement structure with a circular cavity disease and an underground pipe structure in a dispersive medium, and the simulation results show that the dispersion characteristics of the materials lead to high absorption of the GPR electromagnetic waves. Finally, field experiment data are used to further verify the applicability of the algorithm. The numerical simulations and experiments indicate that the dispersion of the medium must be considered in GPR numerical simulation, and the authenticity of the simulated image must be ensured.

Application of GPR System With Convolutional Neural Network Algorithm Based on Attention Mechanism to Oil Pipeline Leakage Detection

High-efficiency and high-quality detection of oil pipeline will significantly reduce environmental pollution and economic loss, so an unconventional oil pipeline anomaly detection convolutional neural network (CNN) algorithm based on attention mechanism is proposed in this article. By taking the simulated ground-penetrating radar (GPR) data as prior knowledge, the structure of the convolutional neural network based on the attention mechanism is constructed, and finally, the location and working condition of the underground oil pipeline are recognized in the simulation data and measured data. The simulation results show that after using the new optimized convolutional neural network, the accuracy rates of the leakage discrimination from horizontal data acquired along the oil pipeline and the classification of the target from longitudinal data acquired perpendicular to the oil pipeline are 94.5% and 84.6%, respectively. Compared with the original convolutional neural network without an attention mechanism, the accuracy rates of the leakage discrimination and the classification of the target are improved by 6.2% and 7.8%, respectively. We further train measured data with an optimized convolutional neural network, results show that compared with a conventional network, the new network can increase the corresponding accuracy rates of the leakage discrimination and the targets classification by 5.4% and 6.9%, reaching 92.3% and 84.4%, respectively. According to our study, the ground-penetrating radar oil pipeline recognition algorithm based on an attention mechanism can well accomplish the identification of underground oil pipelines.

High-order GPU-DGTD method based on unstructured grids for GPR simulation

The finite element time-domain (FETD) method is an appealing electromagnetic (EM) field-solving procedure for the derivation of high-resolution field distribution of complex geological models. However, the arising computational burden has become a main bottleneck restricting the efficient implementation of ground penetrating radar (GPR) simulation at a high speed and large scale. In this research, we developed a high-efficiency EM solver to discretize the partial differential equations on unstructured triangular meshes, using a high-order graphics processing unit (GPU)-finite element discontinuous Galerkin time-domain (DGTD) method. By introducing a semi-discrete strong format instead of solving large stiffness matrices, and by employing the Runge-Kutta temporal integration scheme, the DGTD method can effectively overcome the problem of memory shortage and thereby solves the issue of instability. Besides, the uniaxial perfectly matched layer (UPML) is extended to match the lossy medium and used as an absorbing boundary condition to simulate an open space. Three models were manufactured to compare the DGTD method with the state-of-art methods (FDTD, FETD) of different grids in terms of computing accuracy, the dispersion degree of the rough interface, and memory occupied. To be specific, we explored the detailed effect of both the grid sizes and the order of basis functions on the modeling accuracy for the proposed GPU-DGTD method. We finally verified the numerical solution and demonstrated its applications by simulating a complex model for tunnel geological forecast. The experimental results reveal the order of basic function N and the size of the grid d are closely concerned with the wavelength λ of EM waves, for example, an appropriate definition is d/N ≈ λ/15.

Underground Pipeline Identification into a Non-Destructive Case Study Based on Ground-Penetrating Radar Imaging

Ground-penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in nondestructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR has proven its ability to work in electromagnetic frequency range for subsoil investigations, and it is a risk-reduction strategy for surveying underground various targets and their identification and detection. This paper presents the results of a case study which exceeds the laboratory level being realized in the field in a real case where the scanning conditions are much more difficult using GPR signals for detecting and assessing underground drainage metallic pipes which cross an area with large buildings parallel to the riverbed. The two urban drainage pipes are detected based on GPR imaging. This provides an approximation of their location and depth which are convenient to find from the reconstructed profiles of both simulated and practical GPR signals. The processing of data recorded with GPR tools requires appropriate software for this type of measurement to detect between different reflections at multiple interfaces located at different depths below the surface. In addition to the radargrams recorded and processed with the software corresponding to a GPR device, the paper contains significant results obtained using techniques and algorithms of the processing and post-processing of the signals (background removal and migration) that gave us the opportunity to estimate the location, depth, and profile of pipes, placed into a concrete duct bank, under a structure with different layers, including pavement, with good accuracy.

First-order symplectic Euler method for ground penetrating radar forward simulations in dispersive medium

In recent years, the dispersion characteristics of materials are not considered in the numerical simulation of underground structure damages by ground penetrating radar, which leads to errors in the numerical simulation. To solve this proplem, a novel first-order explicit symplectic Euler method with debye model is provided for the electromagnetic simulation of ground penetrating radar (GPR) systems to the road quality detection in two-dimensional pavement structure. The incident wave is considered to be line source. Two numerical examples are bulit to verify the accucracy and efficiency of the symplectic Euler method with debye model. It can be concluded that the first order symplectic Euler method with debye model and the difference time domain method have almost the same precision, however using the proposed method can result in substantial reduction floating point of per second (FLOPS). Finally, the effect of pavement structure content on the performance of GPR trace profile in detecting the road quality by the proposed method with debye model is investigated.

Attribute Recognition of Buried Pipes Based on Multi-Trace Phase Features Using K-means Clustering for GPR Data Interpretation

Accurate location and depth determination of underground pipes, especially the attribute recognition, are of great importance yet remake a challenging issue in municipal environments. Single-trace phase difference analysis remains a bottleneck due to its inherent and strong randomness in object identification. This paper developed a multi-trace phase difference analysis framework for ground-penetrating radar (GPR) data based on K-means cluster analysis technique and the theory of region of interest (ROI), which could serve as a new criterion for successful pipe attribute recognition. After improving signal-to-noise ratio of GPR data by using the preprocessing techniques, the connected components algorithms (CCA) based on image segmentation and morphological operation is performed to delineate the ROI. The K-means cluster analysis technique is further employed to efficiently extract the multi-trace phase statistical features for comprehensively evaluating the attributes of ROI. We verify this proposed framework by simulated GPR signals, laboratory data and field datasets. Results demonstrate that the proposed method can not only facilitate the attribute recognition of pipes, but also reduce the interpretation ambiguity of the pipe material even in the field site environment. Specifically, if the phase difference of pipe turns out to be even multiples of π, the target can be automatically identified as metallic-category pipes, whereas odd multiples of π, point to non-metallic-category pipes with a lower permittivity than that of the background. This criterion presents promising applicability in subsurface pipeline identification and attributes recognition, especially in constructing a more appropriate initial model of GPR full waveform inversion for survey in pipes.