Ground Penetrating Radar Imaging Research Articles

Underground Diagnosis Based on GPR and Learning in the Model Space.

Ground Penetrating Radar (GPR) has been widely used in pipeline detection and underground diagnosis. In practical applications, the characteristics of the GPR data of the detected area and the likely underground anomalous structures could be rarely acknowledged before fully analyzing the obtained GPR data, causing challenges to identify the underground structures or anomalies automatically. In this article, a GPR B-scan image diagnosis method based on learning in the model space is proposed. The idea of learning in the model space is to use models fitted on parts of data as more stable and parsimonious representations of the data. For the GPR image, 2-Direction Echo State Network (2D-ESN) is proposed to fit the image segments through the next item prediction. By building the connections between the points on the image in both the horizontal and vertical directions, the 2D-ESN regards the GPR image segment as a whole and could effectively capture the dynamic characteristics of the GPR image. And then, semi-supervised and supervised learning methods could be further implemented on the 2D-ESN models for underground diagnosis. Experiments on real-world datasets are conducted, and the results demonstrate the effectiveness of the proposed model.

Imaging of Structural Timber Based on In Situ Radar and Ultrasonic Wave Measurements: A Review of the State-of-the-Art.

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced.

Railroad Infrastructure Management: A Novel Tool for Automatic Interpretation of GPR Imaging to Minimize Human Intervention in Railroad Inspection

Regular monitoring and inspection of military railroad tracks are necessary to ensure the safe transportation of military freight. Manual railroad inspection has drawbacks and limitations that can impact accuracy and efficiency. This study introduces a novel tool designed to automate Ground Penetrating Radar (GPR) imaging interpretation for railroad ballast condition assessment, aiming to reduce human intervention in inspections. The tool uses advanced signal processing techniques, such as the Short-Time Fourier Transform (STFT) and Wavelet Transform (WT), to quantify ballast fouling levels accurately, enhancing maintenance and safety protocols for railroad tracks. Validation through comprehensive testing, including two case studies, demonstrates the tool’s superior efficacy over traditional manual inspection methods. This research represents a pivotal step towards more efficient and reliable infrastructure management, ensuring critical railroad systems’ safety and operational integrity.

Influence of the sand-clay ratio of the burial material of forensic targets on ground-penetrating radar (GPR) responses–comparison of dry and rainy season data

This study aims to assess the impact of the sand-clay ratio on the detection of simulated forensic targets resembling human burial evidence using ground-penetrating radar (GPR). To achieve this goal, we established a controlled site consisting of four experimental graves (SEPs), with each SEP representing a distinct burial scenario. The project’s objectives encompass evaluating how varying the clay soil content influences the detection of swine targets using 2D, quasi-3D, and 4D GPR imaging techniques. We observed signal attenuation in the GPR data during the rainy season and after reflection from larger targets, primarily due to the conductive behavior of the soft tissues within the targets. We achieved more precise results in an SEP that was 85% sand and 15% clay material, regardless of seasonality. We obtained better results in the dry season due to the greater penetrability of electromagnetic waves and more explicit reflections. In comparison, we achieved more precise results for SEP03, which was 70% sand and 30% clay material. In conclusion, controlled sites are invaluable tools for geophysical investigations, as they provide a controlled environment where we can meticulously adjust various parameters, leading to a more precise and insightful analysis of the results. By systematically manipulating factors such as the soil composition, target depth, and environmental conditions, researchers can effectively calibrate their instruments and methodologies, enhancing the accuracy and reliability of their findings. Therefore, controlled sites not only facilitate comprehensive data collection but also serve as essential platforms for refining and validating geophysical techniques, ultimately advancing the field of geophysical forensics.

Indirect joint petrophysical inversion of shallow-seismic and multi-offset ground-penetrating radar field data

SUMMARY In near-surface surveys, shallow-seismic and ground-penetrating radar (GPR) full-waveform inversions (FWIs) have received increasing attention because of their ability to reconstruct high-resolution subsurface models. However, they have different sensitivities to the same targets and thus may yield conflicting geophysical parameter models. To solve this issue, we have developed an indirect joint petrophysical inversion (JPI) integrating shallow-seismic and multi-offset GPR data. These data are used to reconstruct porosity and saturation whereby we use only strong sensitivities between petrophysical and geophysical parameters. To promote its field application, we proposed an input strategy to avoid measuring rock matrix parameters and make indirect JPI more robust. We apply indirect JPI to the field data acquired in Rheinstetten, Germany and find that it reveals the mechanical, electrical and petrophysical properties more reliably than individual inversions. The reconstructed models are assessed by direct-push technology, borehole sample measurements and migrated GPR image. Indirect JPI can fit seismic and GPR observed data simultaneously and provide consistent multiparameter models, which are hard to achieve by FWIs and individual petrophysical inversions. We also find that the method is robust when there are uncertainties in petrophysical a priori information. Overall, the field example proves the great potential of using indirect JPI to solve real-world problems.

Dynamic wave tunnel lining GPR images multi-disease detection method based on deep learning

Ground-penetrating radar (GPR) is widely used in tunnel lining non-destructive testing. However, diverse diseases need more data, leading to low accuracy in multi-disease detection. This paper proposes a dynamic wave tunnel lining GPR images multi-disease detection method based on deep learning to improve the detection accuracy of multi-diseases. Firstly, the tunnel lining generative adversarial networks (TLGAN) model generates multi-dimensional GPR images. Secondly, we designed the Wave-backbone model according to the different disease shape attribute characteristics. Meanwhile, multi-dimensional space fusion combined with the global dynamic wave detection networks Wave-DetNets detects the captured multi-scale global waveform features. Finally, the disease depth was further estimated by detecting results. The experimental results show that the average model accuracy in this paper for identifying cavities, loost, and steel rebars is more than 91%, the omission rate reduces to 0.04, and the detection speed reaches 37FPS with a 320 × 320 GPR image sizing. The model has excellent stability and is better than the existing detection model.

CycleGAN-Based Data Augmentation for Subgrade Disease Detection in GPR Images with YOLOv5

Vehicle-mounted ground-penetrating radar (GPR) technology is an effective means of detecting railway subgrade diseases. However, existing methods of GPR data interpretation largely rely on manual identification, which is not only inefficient but also highly subjective. This paper proposes a semi-supervised deep learning method to identify railway subgrade diseases. This method addresses the sample imbalance problem in the defect dataset by utilizing a data augmentation method based on a generative adversarial network model. An initial network model for disease identification is obtained by training the YOLOv5 network with a small number of existing samples. The intelligently extended samples are then labeled to achieve a balance in the disease samples. The network is trained to improve the recognition accuracy of the intelligent model using a more complete dataset. The experimental results show that the accuracy of the proposed method can reach up to 94.53%, which is 23.85% higher than that of the supervised learning model without an extended dataset. This has strong industrial application value for railway subgrade disease detection as the potential learning ability of the model can be explored to a greater extent, thereby improving the recognition accuracy of subgrade diseases.

Study on the impact of physical characteristics of soil-rock composite medium on its relative permittivity based on laboratory experiments

The permittivity of soil-rock composite media is crucial for interpreting data originating from geophysical surveys such as ground-penetrating radar (GPR) imaging. At present, relevant theoretical research on the correlation analysis between the electrical and physical parameters of soil-rock composite media has not yet resulted in a complete reference system. This makes it difficult to characterize soil-rock composite media using the electromagnetic characteristic parameters of rock and soil media. In this study, the indoor electromagnetic testing method was used to extract electrical parameters such as the relative permittivity of soil-rock composite, and the influence of water content, density, soil-rock ratio, and other factors on the electrical parameters of soil-rock composite medium was analyzed. Furthermore, an artificial neural network prediction model was established, and the accuracy of the model was evaluated. The results showed that the relative permittivity tends to decrease with an increase in saturation when the moisture content of the mixture is high. The rock has a lower permittivity value than the surrounding soil particles. When the rock content increases, the average permittivity and conductive permittivity of the composite medium decreases. The main factors affecting the electrical properties of soil-rock composite media (in order of their influence, from most to least) are water content, compaction degree, and soil-rock ratio. These results provide a reliable reference for field geophysical exploration.

Enhancing anomaly detection in ground-penetrating radar images through reconstruction loss and high-variability

The aim of this study is to enhance the anomaly detection capabilities of ground-penetrating radar (GPR) images by adopting a novel loss function composed of cross-entropy and reconstruction loss. The reconstruction loss measures the error between the inputs and reconstructed inputs via a network that reconstructs inputs using the features extracted by the classification networks. Additionally, GPR images with high variability are generated by combining raw GPR images with background noise collected in the survey fields and white noise (Gaussian noise). The experimental results show that activating reconstruction loss results in an increase in training time (approximately 1.79 times) but significantly improves anomaly detection performance (accuracy peaking at 92.5 %) with the utmost optimal weighted factor being 0.1 using various state-of-the-art classification networks. Furthermore, simulating highly variable GPR images using background and white noise significantly improves the detection accuracy. Background noise introduces noisy details into the GPR images, whereas white noise functions as a high-pass filter depending on the coefficient of variation. This study suggests that the proposed loss function and image manipulation technique can effectively enhance anomaly detection performance.

Intelligent detection of hidden distresses in asphalt pavement based on GPR and deep learning algorithm

Hidden distresses of asphalt pavement are hidden dangers in the service of expressways. Efficient and accurate detection of hidden distresses is of great importance for maintaining and managing the expressways suffering from them. In this study, the intelligent detection technology of asphalt pavement hidden distresses was studied using the object detection algorithm and ground penetrating radar (GPR) image data. A GPR image data set was established to train and test the object detection model. And the distresses samples in the data set include settlement, hidden cracks and loosening. According to the distribution of hidden distresses in GPR image data, a two-way object detection model for targets of different scales was proposed, and each part of the two-way model was optimized and improved based on the basic model. At the same time, a copy-and-paste data enhancement scheme was designed to expand and balance the number of distress data samples. The final model greatly improved the detection accuracy of asphalt pavement hidden distresses. The results of the model test showed that the two-way object detection model proposed in this paper can effectively improve the detection accuracy of small-sized distress objects. Compared to the basic model, the final model had an increase of 17.9% in the average precision (AP) of small objects and a 9.9% increase in the comprehensive index AP (0.5: 0.95). The results of the ablation experiment show that the optimization methods for each part of the model improved the comprehensive detection effect of the model. When compared with other object detection models, the final model was superior to other models in all AP indicators, and had obvious advantages in the detection accuracy of the three types of hidden distresses. The confusion matrix of the test results of the final model showed that the recall rate of the final model for the three types of hidden distresses was above 96%. Thus, applying the final model established in this study to actual road detection can effectively improve the efficiency of distress detection in GPR images.

Comparative deep learning studies for indirect tunnel monitoring with and without Fourier pre-processing

In the last decades, the majority of the existing infrastructure heritage is approaching the end of its nominal design life mainly due to aging, deterioration, and degradation phenomena, threatening the safety levels of these strategic routes of communications. For civil engineers and researchers devoted to assessing and monitoring the structural health (SHM) of existing structures, the demand for innovative indirect non-destructive testing (NDT) methods aided with artificial intelligence (AI) is progressively spreading. In the present study, the authors analyzed the exertion of various deep learning models in order to increase the productivity of classifying ground penetrating radar (GPR) images for SHM purposes, especially focusing on road tunnel linings evaluations. Specifically, the authors presented a comparative study employing two convolutional models, i.e. the ResNet-50 and the EfficientNet-B0, and a recent transformer model, i.e. the Vision Transformer (ViT). Precisely, the authors evaluated the effects of training the models with or without pre-processed data through the bi-dimensional Fourier transform. Despite the theoretical advantages envisaged by adopting this kind of pre-processing technique on GPR images, the best classification performances have been still manifested by the classifiers trained without the Fourier pre-processing.

Geophysical and chemical characteristics of peatland in coastal wetland, southern Thailand

Peatlands, as an important carbon sink, store a large amount of atmospheric carbon that is vulnerable to becoming a carbon emission source due to the disturbance of the peat layer by natural and anthropogenic processes. The Kuan Kreng peat swamp forest is the second largest peatland in Thailand and is in coastal wetland serving for carbon stock in the subsurface. To assess carbon stock in peatland, the thickness and distribution of the peat layer are necessary. This study determines physical and chemical characteristics of the peatlands in the coastal wetland, southern Thailand by conventional core study and geophysical surveys, including ground penetrating radar (GPR) and electrical resistivity imaging (ERI). There is a significant relationship between chemical and physical properties from the peat layer, which is useful to approximately predict the physical properties and peat layers in the geophysical profiles. The resistivity profiles exhibit high resistivity response, peat between 21.9 and 145.0 ohm-m, interpreted as the peat layers in the shallow subsurface. The high resolution GPR profiles provide an average GPR velocity in peat of 0.040 m/ns and a relative dielectric constant of 54.9 for the peat layers. The peat layer is characterized by a high amplitude of reflection in the GPR profiles. The thickness of the peat layers was estimated from the geophysical surveys and the drilling cores having an average thickness of 18 cm. Average values of bulk density (0.19 g/cm3) and TOC (31.18 wt. %) from the drilling core samples are used to calculate the carbon density in the peat layers giving a result of 59.24 Kg C/m3. Hence, the carbon stock at the Kuan Kreng peat swamp forest is estimated at least 7.53 megaton carbon, which is much higher than aboveground biomass carbon in this area. The innumerably high amount of carbon stock could raise awareness of peatland disturbances.

Investigating soil layers with ground penetrating radar in the modern Yellow River Delta of China

AbstractSoil layers affect the vertical movement of moisture and salt, eventually resulting in land cover and land use pattern changes. This study explored the ability of ground penetrating radar (GPR) to detect soil layers in the modern Yellow River Delta of China and assessed its accuracy. It was found that soil moisture and salt had a strong dampening effect on the electromagnetic wave signal which resulted in blurred GPR images of the soil profile below 1 m. The cultivated soil layers of different crop types, such as rice, wheat, corn and cotton, were accurately identified in GPR images. To estimate an individual soil layer thickness, the propagation velocity of the electromagnetic wave was calculated using soil mass moisture content, and the propagation time was confirmed by comparing the GPR image with the amplitude–time plot of the soil profile. The estimated thickness was 1.02 times the thickness determined in the field and the average estimation error was 0.04 m, which was 24.09% of the soil layer thickness determined in the field. The second derivative value of envelope amplitude energy with time (SDEA) was used to describe the amplitude change in the soil layers. The SDEA has negative logarithmic and power function relationships with soil mass moisture content and electrical conductivity, respectively. The present results provide a reference database for future quantitative soil investigation in the sedimentary plain area using GPR.

GCNet: Ground Collapse Prediction Based on the Ground-Penetrating Radar and Deep Learning Technique

Manual methods to detect and identify potential ground collapse hazards have a high volume of workload and rely on manual skills and experience strongly, with substantial inconsistencies. This paper proposes an automatic recognition method of potential underground hazards and designs an object detection-based algorithm to predict the hidden danger of ground collapse, called GCNet. The GCNet is a ground collapse hazard detection network, which is developed to detect underground hazards, including cavities, poor soil quality, pipelines, and soil background. The GCNet uses the deep residual network ResNet101 and feature pyramid network (FPN) to extract features and a task coordination network (TCN) to identify the category and location of hidden underground hazards. Further, a feature enhancement method based on the regional binary pattern is proposed to improve the accuracy of the proposed model by expanding the ground-penetrating radar (GPR) data by adding multi-level features to GPR images. The experiments are conducted on a large amount of real GPR data and experiment results show that the proposed automatic recognition method can surpass the existing deep learning-based methods in hidden underground hazard recognition and identification.

A Refined Combined Grid Model for Characterizing Concealed Microcracks with Various Geometric Shapes Based on Radar Signal Processing

The concealed microcracks in shield tunnel lining present the characteristics of being of small size, unknown shape, and are difficult to detect. Based on the finite-difference time domain (FDTD) approach, this study proposed a new construction method of a refined grid accommodating and combining the variable shapes of microcracks, and capable of designing cross type, mesh type, and wave type microcrack models. The proposed new method also configured steel bars in the models to simulate actual engineering conditions, and characteristic response images of the models under different working conditions were obtained using ground penetrating radar (GPR) technology, which were then compared and analyzed to identify the imaging characteristics and differences of microcracks with variable geometric shapes. The waveform, amplitude, and time span of the characteristic single channel signal were furthermore studied. The results showed that the new method could successfully simulate the GPR characteristic response images of 0.5[Formula: see text]mm microcracks of diverse geometric shapes. When the microcracks were wavy, their real shape could only be determined after signal pre-processing; the density and quantity of steel bars directly affected the appearance of microcrack characteristic signals; the greater the density and quantity of steel bars, the greater the interference on the waveform, amplitude, and time-frequency range of electromagnetic wave signals; a special correlation existed between the maximum mean root square value of the amplitude and the single channel signal of the cracks. Moreover, the finding that the extension in time and distance in the GPR time distance profile intersected with the cracks was deemed potentially to provide fresh insights into identifying the characteristic points of the cracks in the GPR images. The new method proposed in this study successfully obtained the GPR numerical simulation images and characteristic signals of microcracks with variable geometric shapes. Through the processing and analysis of the characteristic response signals of microcracks, the conclusions obtained were considered to provide an interpretation basis for the detection of microcracks in practical engineering.

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.

Safety Monitoring of Transportation Infrastructure Foundation: Intelligent Recognition of Subgrade Distresses Based on B-Scan GPR Images

The safety monitoring of transportation infrastructure foundation is crucial for the sustainable service of transportation systems. In recent years, the Ground Penetrating Radar (GPR) has become a powerful tool to identify and locate the subgrade distresses according to the different responses of wave characteristics, preliminarily realizing an intelligent nondestructive detection. To solve the problems like small sample size and unbalanced dataset, this study used a deep data augmentation method, e.g. WGAN-GP network, to augment the original limited B-Scan GPR data of subgrade, and then carried out supervised learning for classification task. The detailed computation steps include the image processing, data augmentation and intelligent analysis. First, the dataset was initially enlarged through the traditional methods after noise filtering, gamma transform and other processing methods. Then, the WGAN-GP network was adopted to generate new high-quality B-Scan images. Finally, the intelligent classification of subgrade distresses was realized by ResNet50 model with a satisfactory accuracy of 90.85%.

Automatic recognition of defects behind railway tunnel linings in GPR images using transfer learning

Ground-penetrating radar (GPR) is widely used for non-destructive detection of hidden defects behind railway tunnel linings, including non-compactness, air- and water-filled voids, fractures, and insufficient lining thickness. However, manual interpreting a substantial volume of GPR B-scan images is time-consuming, and the results heavily depend on the professional knowledge of the practitioner and the available prior information. This paper presents an automatic recognition algorithm for detecting hidden defects, as well as estimating lining thickness in GPR B-scan images of railway tunnels using a You Only Look Once for Panoptic driving perception network (YOLOP) model. The process mainly contains three steps. Firstly, a real GPR dataset of underground objects is employed for transfer learning to enhance the robustness of the prediction model. Secondly, a dataset containing 2,245 real GPR B-scan images of railway tunnel linings is created. Thirdly, the established dataset is used to retrain the YOLOP model and assess the prediction accuracy for identifying hidden defects and lining thickness in GPR images. Field experiment results demonstrate that the trained hidden defects recognition algorithm achieves an average accuracy of 80.6%, with a detection time of only 0.02 s for a GPR image sizing of 1024 × 512. Therefore, it is concluded that the proposed algorithm is efficient and reliable for automatic recognition of hidden defects behind railway tunnel linings.

Detection of roadbed layers in mountainous area using down-up-crosshole penetrometer and ground penetrating radar

The characteristics and depths of the old roadbed layers must be assessed for the serviceability and rehabilitation of aging road pavements. This study’s objective is to detect roadbed layers in mountainous areas using the obtained stiffness profile and ground penetrating radar (GPR) results. Down-up-crosshole penetrometer (DUCP), GPR, and light falling weight deflectometer (LFWD) tests were conducted to determine the overlay, surface, base, subbase, antifrost, and subgrade layers. The results show that the dynamic cone penetration index DCPI, shear modulus calculated by the shear wave velocity, and GPR image capture each layer. The elastic modulus estimated by the shear modulus is corrected by considering the strain influence factor and matches the LFWD modulus, despite the ranges of values differing owing to the disparate strain level. Therefore, the DUCP can be applied to roadbeds in mountainous areas to assess the stiffness at each layer as well as to detect the layer interface.

Automatic Detection and Classification of Underground Objects in Ground Penetrating Radar Images Using Machine Learning

Automatic Detection and Classification of Underground Objects in Ground Penetrating Radar Images Using Machine Learning