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"Ground penetrating radar"
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RCE-GAN: A Rebar Clutter Elimination Network to Improve Tunnel Lining Void Detection from GPR Images
Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.
Journal Article
Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks
Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.
Journal Article
Reconstruction of High-Resolution 3D GPR Data from 2D Profiles: A Multiple-Point Statistical Approach
Ground-penetrating radar (GPR) is a popular geophysical tool for mapping the underground. High-resolution 3D GPR data carry a large amount of information and can greatly help to interpret complex subsurface geometries. However, such data require a dense collection along closely spaced parallel survey lines, which is time consuming and costly. In many cases, for the sake of efficiency, a choice is made during 3D acquisitions to use a larger spacing between the profile lines, resulting in a dense measurement spacing along the lines but a much coarser one in the across-line direction. Simple interpolation methods are then commonly used to increase the sampling before interpretation, which can work well when the subsurface structures are already well sampled in the across-line direction but can distort such structures when this is not the case. In this work, we address the latter problem using a novel multiple-point geostatistical (MPS) simulation methodology. For a considered 3D GPR dataset with reduced sampling in the across-line direction, we attempt to reconstruct a more densely spaced, high-resolution dataset using a series of 2D conditional stochastic simulations in both the along-line and across-line directions. For these simulations, the existing profile data serve as training images from which complex spatial patterns are quantified and reproduced. To reduce discontinuities in the generated 3D spatial structures caused by independent 2D simulations, the target profile being simulated is chosen randomly, and simulations in the along-line and across-line directions are performed alternately. We show the successful application of our approach to 100 MHz synthetic and 200 MHz field GPR data under multiple decimation scenarios where survey lines are regularly deleted from a dense 3D reference dataset, and the corresponding reconstructions are compared with the original data.
Journal Article
Directional and High-Gain Ultra-Wideband Bow-Tie Antenna for Ground-Penetrating Radar Applications
Bow-tie antennas are utilized extensively in ground-penetrating radar (GPR) systems. In order to achieve sufficient penetration depth and resolution, the bow-tie antennas for GPR applications require low operating frequency, high gain, and excellent broadband. A novel ultra-wideband (UWB) bow-tie antenna with gain enhancement for GPR applications is proposed in this paper. First, a UWB bow-tie antenna with resistive loading is designed. The metal reflector and metamaterial loading make the bow-tie antenna directional, and loading the same metamaterial on the front side of the antenna further improves directional gain. After testing, the lowest frequency of the fabricated antenna is 317 MHz, the relative bandwidth is 98.6%, the peak gain in the frequency range is 9.3 dBi, and the size is only 0.38 λ at the lowest frequency. The proposed compact antenna takes both gain and bandwidth into consideration. Finally, in order to further verify the effectiveness of the proposed antenna in the GPR system, a stepped frequency continuous wave ground-penetrating radar (SFCW-GPR) system was built. The experimental results show that the designed antenna is suitable for the GPR system of deep penetration and high-resolution detection, which is beneficial to the imaging of underground structures.
Journal Article
Water Ice Resources on the Shallow Subsurface of Mars: Indications to Rover-Mounted Radar Observation
The planet Mars is the most probable among the terrestrial planets in our solar system to support human settlement or colonization in the future. The detection of water ice or liquid water on the shallow subsurface of Mars is a crucial scientific objective for both the Chinese Tianwen-1 and United States Mars 2020 missions, which were launched in 2020. Both missions were equipped with Rover-mounted ground-penetrating radar (GPR) instruments, specifically the RoPeR on the Zhurong rover and the RIMFAX radar on the Perseverance rover. The in situ radar provides unprecedented opportunities to study the distribution of shallow subsurface water ice on Mars with its unique penetrating capability. The presence of water ice on the shallow surface layers of Mars is one of the most significant indicators of habitability on the extraterrestrial planet. A considerable amount of evidence pointing to the existence of water ice on Mars has been gathered by previous researchers through remote sensing photography, radar, measurements by gamma ray spectroscopy and neutron spectrometers, soil analysis, etc. This paper aims to review the various approaches utilized in detecting shallow subsurface water ice on Mars to date and to sort out the past and current evidence for its presence. This paper also provides a comprehensive overview of the possible clues of shallow subsurface water ice in the landing area of the Perseverance rover, serving as a reference for the RIMFAX radar to detect water ice on Mars in the future. Finally, this paper proposes the future emphasis and direction of rover-mounted radar for water ice exploration on the Martian shallow subsurface.
Journal Article
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.
Journal Article
A Pipeline Leakage Detection Technique Using Ground Penetrating Radar Polarization Method
Rapid detection of leaks in water distribution systems is conducive to saving resources and protecting the environment. Ground-penetrating radar (GPR) is an efficient and economical nondestructive testing technique that can realize the rapid detection of pipeline leaks. However, because the diffraction characteristics of the leakage area are not obvious, it is difficult for the traditional GPR to accurately extract the scattering information of the leakage area in the complex buried environment. This paper proposes a fast pipeline leakage detection method based on eigenvalue decomposition with full polarimetric ground-penetrating radar (FP-GPR). Aiming at the limitations of the traditional
H-α
decomposition regarding parameter instability and computational efficiency, we propose more stable and computationally efficient substitution parameters
h
and
q
. Based on the roughness state of the interface in the leakage area and the saturation distribution of the soil, we analyze the influencing factors of the polarized rotating field. Numerical simulations validate the workflow of the method, and modeling experiments verify the method's applicability in engineering. The results show that pipe leakage leads to a decrease in
α
and an increase in
H
. The
H-q
and
h-q
planar classifiers are more favorable for pipeline leakage identification. The method can be adapted to rapidly detect pipe leakage in complex scenarios, which is significant for managing and maintaining urban water distribution systems.
Journal Article
Experimental Co-Polarimetric GPR Survey on Artificial Vertical Concrete Cracks by the Improved Time-Varying Centroid Frequency Scheme
The experimental setup is devised to simulate the presence of vertical cracks with varying widths within concrete structures. Co-polarimetric ground-penetrating radar (GPR) surveys are carried out to acquire the “VV” and “HH” polarization data. The time-varying centroid frequency attribute is employed to describe the vertical variation in the center frequency of the radar wave, unveiling a gradual vertical decay in the centroid frequency at the locations of vertical cracks. An improved time-varying centroid frequency attribute based on the adaptive sparse S-transform (ASST) is proposed and tested by a finite-difference time-domain model and co-polarimetric GPR data, which can offer better resolution compared to that of the conventional S-transform. By analyzing the waveform and centroid frequency properties of the two polarizations, we conclude that the “VV” polarization is relatively sensitive to centimeter scale cracks, while the “HH” polarization is more sensitive to millimeter scale cracks.
Journal Article