Ground Penetrating Radar Detection Research Articles

Enhancement of penetration of electromagnetic waves by field focusing applied to GPR detection

Abstract Ground penetrating radar (GPR) is limited by challenges such as signal attenuation and inadequate target detection in lossy dielectrics, prompting research on data processing algorithms to enhance detection capability. In this study, we propose a method for enhancing
GPR detection capability based on electromagnetic field focusing (FF). The method involves calculating the phase difference attributable to the range difference between each antenna element and the focal point and compensating its feed phase accordingly. This approach effectively concentrates the energy of electromagnetic waves at the target area. Tapered slot antennas operating within the 2.5–8 GHz frequency band were employed for transmitting and receiving, thus facilitating algorithm verification and target detection. The synthetic aperture radar algorithm executed pulse compression and range migration on the received data, thereby intensifying the signal at the target object's position and achieving double focusing.
Notably, this study uniquely employs FF to enhance the penetrability of electromagnetic waves and augment the detection capability of GPR. Both simulation and experimental results demonstrated the effectiveness of the proposed method in improving the GPR imaging quality.

Performance Assessment of Existing Asphalt Pavement in China’s Highway Reconstruction and Expansion Project Based on Coupling Weighting Method and Cloud Model Theory

In China, a substantial portion of highway asphalt pavements are no longer capable of accommodating increasing traffic volumes and necessitate renovation and expansion. Prior to commencing such activities, it is crucial to evaluate the performance of the existing asphalt pavements. This study developed a novel normal cloud framework integrating a comprehensive weighted indicator system for existing asphalt pavement. Five key performance indicators including riding quality index (RQI), rutting area ratio (Rr), cracking area ratio (Cr), patching area ratio (Pr), and pavement structural strength index (PSSI) were selected to holistically represent the pavement condition in highway renovation and expansion projects. Subsequently, a method was proposed to determine the weights of these indicators by integrating the analytic hierarchy process (AHP) and entropy. A normal cloud model was constructed to address data characteristics and representation of indicator fuzziness/randomness through digital cloud modeling. The model was applied to 12 sections of the Jingjintang Expressway (Tianjin section). The results revealed only one section where the normal cloud model differed from the pavement maintenance quality assessment (PQI) model. The 3D ground-penetrating radar detection results of this different section indicated that the normal cloud model more closely aligned with the road structure condition. Compared to absolute pass/fail criteria of the traditional PQI model, the cloud model offered enhanced sensitivity to define graded condition assessments essential for reconstruction planning and decision analysis. Therefore, the normal cloud model is more suitable for assessing the performance of existing asphalt pavements in highway reconstruction and extension projects compared to the PQI model.

A DL method to detect multi-type hidden objects in tunnel linings using a comprehensive GPR dataset

This study proposes an enhanced detection method for tunnel lining hidden objects based on the comprehensive Ground Penetrating Radar (GPR) dataset and an improved deep learning (DL) algorithm. To mitigate the issue of data scarcity, a tailored dataset consisting of six types of hidden objects was compiled, which contained 7426 on-site collected GPR images and 71,014 well-labeled objects. This bespoke dataset offered substantial support for algorithm training and validation, making it one of the most extensive datasets in the domain for tunnel lining GPR detection. An enhanced Faster R-CNN algorithm was then developed to effectively detect different types of hidden objects. Modifications were made in four key areas: Feature extraction module, Feature Pyramid Network (FPN), customized anchor scheme and modified loss function. This algorithm was subsequently trained and validated using the comprehensive dataset. Evaluation metrics demonstrated the performance of this approach, with a mean average Precision (mAP) of 81.2%. Results also proved particularly effective in detecting rebars, pipelines, and steel arches while demonstrating a shortfall in detecting voids and cavities due to the data limitation. The results underscore the potential of this enhanced algorithm for the automatic detection of diverse hidden objects in tunnel linings, thus stimulating further research in this vital field.

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 detection localization of underground structures based on deep learning and reverse time migration

Ground penetrating radar (GPR) is widely used in detection localization of underground structure anomalous bodies. However, it is impossible to achieve accurate imaging localization of underground structures targets only by the waveform of GPR detection profiles due to the interference of clutter signals in engineering application. In this paper, a RefineNet deep learning network based on hybrid loss function is proposed to remove clutter signals in GPR profiles, and a reverse time migration (RTM) imaging algorithm based on total variation(TV) regularization is developed to perform migration imaging on GPR profiles processed by RefineNet network to realize accurate imaging localization of underground structures targets. Numerical simulations demonstrate that the developed RefineNet deep learning network can significantly remove clutter signals and improve GPR profiles quality, and the proposed RTM imaging algorithm based on TV regularization can accurately obtain location and shape information of underground structures targets. The full-scale experiment and field detection show that the proposed algorithm can accurately obtain the location of underground pipelines, cavities and other anomaly bodies. It is concluded that the developed algorithm provides a strong technical support for the GPR detection localization of underground structures targets.

Research on the Technique of Determining the Internal Structure of Ancient Stone Pagodas Based on the Discriminant Features of Ground Penetrating Radar

Abstract Obtaining detailed information about the design and construction of ancient stone pagodas is challenging due to their significant age. Due to this, the internal structure is still unknown, and the analysis of their structural safety lacks a solid foundation. The purpose of this paper is to research ancient stone pagodas and propose adequate methods for identifying their internal structure and assessing their damage status using ground-penetrating radar nondestructive detection technology. The internal structure of the pagoda is inferred through a comprehensive process of layer-by-layer scanning using shielded antennas of varying frequencies, incorporating distance correction. The results demonstrate that by selecting suitable radar antenna frequencies, both test profiles become visible, and the position and characteristics of the internal substantial reflection area exhibit a high degree of consistency, mutually reinforcing each other. The White Pagoda in Hangzhou is believed to have both a mortise and tenon joint structure and a column structure. The ground penetrating radar detection method proves to be effective and has valuable applications.

Research on lightweight GPR road surface disease image recognition and data expansion algorithm based on YOLO and GAN

The aim of this paper is to improve the accuracy and efficiency of ground penetrating Radar (GPR) detection of internal road surface disease images. Based on the YOLOv4 target detection algorithm, this study introduces MobileNetV2 and CBAM attention mechanism, and combines the Focal loss confidence loss function to iterate the model, so as to design an efficient and lightweight GPR pavement disease image recognition algorithm, MC-YOLOv4. At the same time, in order to alleviate the problem of data scarcity in GPR, we redesign an unsupervised generative adversarial neural network based on self-attention mechanism, namely SAGAN-W. Experiments show that MC-YOLOv4 not only has faster reasoning ability, but also occupies only 23% of the memory of YOLOv5-S. After using the SAGAN data augmentation, the mAP@ .0.5 evaluation index is further improved by 2.63%, and the collapse and mode collapse that may occur in the training process of the traditional Convolutional Generative Adversarial Neural Network (DCGAN) are avoided. After introducing the Focal loss confidence loss function to participate in the training, It significantly improves the imbalance between the precision and recall of the detection model, and this scheme is verified and supported by real scenes. The experimental results show that the proposed method has significant advantages in automatic detection and data expansion of lightweight GPR pavement invisible diseases, which has a wide range of application value and research significance.

Ground penetrating radar used to detect drowning victims under ice.

Floating bodies are successfully detected under both ice and snow using a commercial ground penetrating radar system with ice depths reaching up to 26 cm in a controlled environment.The differences between using radar systems operating at/around 500 MHz and 1 GHz were not pronounced from the point of view of detection.Future studies should investigate the capabilities for detecting bodies in more realistic settings.

Breakthrough and Practice of Ground Penetrating Radar Detection Technology in Baiyangdian Lake

In order to investigate the stratum structure of the lake bottom in the Baiyangdian Lake area, implement the test of the water-based ground penetrating radar detection system, explore the working mode of the water-based radar detection, compare the water exploration capabilities of different hardware devices, analyze the electromagnetic wave propagation characteristics in the Baiyangdian Lake area, and interpret the geophysical response of the native strata at the bottom of the lake. The stratum structure data in the 15m depth range of the bottom of the lake was obtained to provide a basis for the establishment of a three-dimensional geological model of the Dian District. This technical achievement can strongly promote the construction of a lake and wetland water-land integrated technical method system.

Envelope Derivative Operator-based Instantaneous Attributes for Locating Road Cavities

Ground penetrating radar, as an important means of near-surface detection, is being used more and more extensively in road detection. The instantaneous centroid frequency of ground penetrating radar has good directivity for road cavities, and the research of this group further improves the instantaneous centroid frequency for ground-penetrating radar road detection. In this paper, we will model and simulate ground penetrating radar detection to verify the effectiveness of the improved instantaneous pointing frequency.

Application of Variational Mode decomposition in GPR Detection of Oil Contaminated Soil

In this article, Ground penetrating radar (GPR) is used to investigate the contaminated soil by oil pollutants in oilfield operation area. Time frequency analysis method is applied to GPR data to reveal the changes of GPR signal frequency components with time variation, which is very important for data interpretation. We use variational mode decomposition (VMD) for time frequency analysis. It can decompose radar signal into several quasi-orthogonal intrinsic mode functions (IMFs) with limited bandwidth, and has strong robustness and local decomposition ability. We apply the VMD on field data acquired in the area with waste liquid contaminated soil in oilfield. The decomposed IMFs from GPR data shows that the signals from the contaminated soil is characterized by the strong energy and low frequency. Then the instantaneous attribute analysis of the IMF is carried out to delineate the scope and thickness of contaminated soil. The experimental results show that the limited bandwidth IMF with specific sparsity obtained by VMD method is more conducive to the processing and interpretation of GPR data.

Near-field Focusing in Ground Penetrating Radar Based on Phase Compensation Algorithm

Underground energy intensity and electromagnetic wave penetration depth are important parameters to characterize the detection performance of Ground Penetrating Radar (GPR). Near-field focusing (NFF) is an effective means to improve the electromagnetic energy fed into the ground by the antennas. In this paper, the phase compensation algorithm considering underground media is derived. In order to verify the effectiveness of the algorithm, the NFF antenna array is proposed. By changing the phase of the NFF array, the focus distance can be adjusted in the range of 400 mm – 800 mm. The maximum field enhancement coefficient in the focal region can achieve 19.2 dB. This shows that NFF array based on phase compensation algorithm has potential application prospects in increasing GPR detection performance.

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.

Study on fatigue crack propagation failure in semi-rigid base

Nowadays, it is of non-negligible importance to extend the service life of in-service pavements. As the base form, shrinkage fractures and plate faults are the main reasons for the failure of semi-rigid base and it is of practical significance to study the cracking mechanism of the fracture damage from the microscopic point of view. In order to obtain the fatigue propagation law of the cracks inside the semi-rigid base, a three-dimensional preset depth crack fatigue propagation model was established based on the finite element quasi-static method and ground-penetrating radar detection data, and monitors the propagation of the crack tip section through the probe, obtaining the number of load actions and the crack propagation depth during the destabilization. Subsequently, this paper investigates the stress field distribution and fatigue propagation failure law of crack tip section inside the semi-rigid base based on the ensemble analysis method of stress intensity factor, combined with the macroscopic modulus change of the base structure. The propagation form of single-crack cracks within the base layer is mainly dominated by type I (opening type) cracks, but there are also the remaining two crack propagation trends at the tip. The analysis shows that the smaller the modulus of the semi-rigid base, the less likely the crack is to undergo an initial tip change. In the case of four preset depths, the preset depth of 50 mm is the most vulnerable to crack tip change. Relatively speaking, and the 200 mm preset depth cracks expand the maximum depth when the structure fails in all preset depths. It can be seen from the experiments that the strain field at the crack tip of 50 mm presetting depth is stronger. The strength of stress and strain fields near the crack tip at 200 mm and 150 mm preset depths is mainly related to the modulus of the base layer. The larger the modulus, the stronger the stress and strain fields. The distribution of the field strength at the crack tip of 100 mm and 50 mm presetting depth is less affected by the base layer modulus. In this study, the three-dimensional simulation of the fatigue action of the internal cracks in the semi-rigid base is innovatively proposed, which helps to clarify the distribution of the stress field and the fatigue failure of the internal crack tip of the semi-rigid base, and provides a new way of thinking for the assessment of the service performance of the long-life pavements and the failure mechanism of the microscopic damages.

Automatic defect detection in operational high-speed railway tunnels guided by train-mounted ground penetrating radar data

The tunnel diseases of high-speed railways present significant risks under the substantial dynamic loads from trains. Train-mounted ground-penetrating radar detection data for tunnels relies heavily on manual judgment, resulting in low efficiency and inaccurate identification. Meanwhile, automatic detection models need more samples. We propose an automated recognition framework, guided by generated data, to address these issues for identifying defects in operational high-speed railway tunnels. In this work, we propose a multi-category GPR generative adversarial network (MGPR-GAN) to generate many defect data across different categories to guide YOLOv5 training instead of relying predominantly on real data. It then uses generated data (65.43%) alongside a smaller proportion of real defect data (34.57%) to guide a YOLOv5 model. The model outputs the defect category, confidence level, and defect width and height estimates in conjunction with prior information. We validated our method using samples from 71 tunnels. The results reveal that our proposed method outperforms the average accuracy of the three advanced identification methods (FasterRCNN, SSD, and YOLOv5) by over 20.62%.

Primarily investigation with multiple methods on permafrost state around a rapid change lake in the interior of the Tibet Plateau

Changes of the lakes on high-altitude regions of the Tibet Plateau influence the state of the surrounding permafrost. Due to the climate warming and wetting trend, extreme events including lake outburst has occurred more frequent. In 2011, an outburst event occurred on the Zonag Lake and this event changed the water distribution in the basin, leading a rapid expansion of the Tailwater lake, named as the Salt Lake. However, the construction of the drainage channel in the Salt Lake ended the expansion process and the shrinkage of the lake started since 2020. To investigate the permafrost state around the Salt Lake, multiple methods, including drilling boreholes, the unmanned aerial vehicle survey and the ground penetrating radar detection have been applied. By integrating these multi-source data, the thermal regime, topography and the spatial distribution of the permafrost around the Salt Lake were analyzed. The result showed that the permafrost state around the Salt Lake was related to the distance from the lake water. The permafrost table appears at 90 m away from the Salt Lake and interrupted by a nearby thermokarst lake at 220 m. The ground temperature in the natural field is 0.2 °C lower than the temperature in the lake at a depth of −5 m.

Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar

The inspection and monitoring of structural conditions are crucial for the maintenance of semi-rigid base pavement. To achieve the inverse calculation of material parameters and obtain the mechanical response of asphalt pavement, a method of modulus correction by reducing the error between tested and simulated strains was first developed. The relationship between the temperature at various depths within the pavement structure and atmospheric temperature was effectively demonstrated using a dual sinusoidal regression model. Subsequently, pavement monitoring data illustrated that as loading weight and temperature increased and loading speed decreased, the three-way strain of the asphalt layer increased. Thus, the relationship model between loading conditions and three-way strain was established with a good fitting degree (R2 > 0.95). The corrected modulus was obtained by approximating the error between simulated and measured strains. Then, the finite element analysis was performed to calculate key mechanical index values under various working conditions and predict the fatigue life of asphalt and base layers. Finally, ground-penetrating radar (GPR) detection was performed, and the internal pavement condition index was defined for quantitative assessment of structure conditions. The results show that there is a good correlation between the internal pavement condition index (IPCI) and remaining life of pavement structure. Therefore, our works solve the problems of the parameter reliability of pavement structures and quantitative assessment for structural conditions, which could support the performance prediction and maintenance analysis on asphalt pavement with a semi-rigid base.

Application of Ground-Penetrating Radar with the Logging Data Constraint in the Detection of Fractured Rock Mass in Dazu Rock Carvings, Chongqing, China

Geologic interpretation results from conventional ground-penetrating radar (GPR) detection methods tend to have a certain degree of uncertainty. In order to improve the reliability of ground-penetrating radars in the detection of rock mass fissures in grottoes, this study proposes a ground-penetrating radar detection method with the logging data constraint, which is applied to detect the fractured rock mass in the Baodingshan Scenic Area of Dazu Rock Carvings, Chongqing, China. First, conventional logging and borehole televiewer data were compared and verified, yielding detailed lithological and wellbore fissure information. Next, electromagnetic wave velocity was calibrated using GPR profile and the depth of the stratigraphic interface determined by borehole data. Utilizing this calibrated velocity, we are able to accurately calculate the depth values of anomalies in GPR interpretation profiles. Subsequently, we compared the preliminary GPR interpretation profile with the borehole televiewer images. After eliminating false anomalies caused by interference, we obtained more reliable location information for detection targets such as fissures, fracture zones, and weak interlayers. The results of fissure detection in the Dazu Rock Carvings indicate that the detection results of ground-penetrating radar are verified and supplemented under the constraints of stratigraphic and well-wall fissure information obtained by logging. This effectively mitigates the influence of multiplicity and false anomalies of GPR detection on interpretation results. GPR with the logging data constraint enhances the accuracy of the fissure detection results, providing novel technical means for the protection and restoration of grotto relics.

Least Squares Reverse Time Migration of Ground Penetrating Radar Data Based on Modified Total Variation

As a fundamental part of ground penetrating radar (GPR) data processing, reverse time migration (RTM) can correctly position reflection waves and focusing diffraction waves on the proper spatial position. Least-squares reverse-time migration (LSRTM) is widely used in the seismic field for its ability to suppress artifacts and generate high-resolution images in comparison to conventional RTM. However, in the particular case of GPR detection methods, LSRTM is extremely susceptible to aliasing artifacts caused by under-sampling. In pursuit of enhanced precision in underground structure characterization, this paper presents the development of a new LSRTM based on modified total variation (MTV) regularization to improve imaging resolution. Initially, the objective function of LSRTM is derived by combining the Born approximation in 2-D transversal magnetic mode. Next, the adjoint equations and their gradients are solved using the Lagrange multiplier method. The objective function is then constrained by MTV regularization to ensure the precision and convergence of the LSRTM, which delivers a refined edge with reconstruction details. In the numerical experiments, in comparison to the conventional LSRTM method, the LSRTM-MTV algorithm demonstrated a 30.4% increase in computational speed and a 21.1% reduction in mean squared error (MSE). The outperformance of the proposed method is verified in detail through the image resolution and amplitude preservation in the test of synthetic data and laboratory data. Future research efforts will center on applying the proposed method to models featuring dispersive or anisotropic media that closely mimic real-world conditions and extending the application to various imaging techniques involving objective function minimization.

Ground penetrating radar detection of buried depth of pavement internal crack in asphalt surface: A study based on multiphase heterogeneous model

Sample identification method and virtual generation method were put forward to construct the multiphase heterogeneous asphalt surface models of the pavement for electromagnetic differential simulation, which reproduces the actual phase distribution better compared with the homogeneous models and the random heterogeneous models applied in the current studies. For the electromagnetic wave with different frequency which transmitted by the ground penetrating radar (GPR), the generation mechanism of the heterogeneity interference wave and the reflection wave from the top or bottom of layer was discussed, and their influences on the identification of internal crack with different relative permittivity values were analyzed. The heterogenous dielectric distribution in the asphalt surface layer was studied by combining simulation and laboratory test, and the double-way travel time method for detecting the buried depth was optimized on this basis, and an additional term reflecting the heterogeneity interference was introduced into the buried depth detection results.