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The failures of culverts can result in water and soil loss in the surrounding subsurface, leading to collapses. In densely constructed urban areas, buried culverts are inaccessible and invisible, making Ground Penetrating Radar (GPR) inspection a widely adopted nondestructive and efficient method. Previous research has employed artificial intelligence to automate GPR interpretation by identifying special radargram textures, but the irregular GPR responses of degraded subsurface soils present a challenge. Therefore, this study proposes a method for preliminary diagnosing large-scale culverts based on GPR wave attributes. The method comprises three parts: (1) evaluating soil compactness by estimating the tortuosity of GPR responses at the culvert top; (2) investigating soil moisture using the attenuation rate of GPR responses; and (3) overlaying the results of the incompact and moist soils to pinpoint potential degradations. Two long-distance culverts were inspected and diagnosed using the proposed method, and three abnormal sections were identified and confirmed by excavation evaluation. The case studies indicate that the proposed method can quickly provide reference for further in-depth investigation, thereby improving GPR survey efficiency and reducing workload by narrowing the investigation scope. Consequently, this study can facilitate large-scale GPR culvert surveys and further safeguard the water system. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is an effective tool for detecting internal defects in asphalt roads due to its non-destructive nature and high resolution. However, detecting defects in GPR images remains challenging, as existing models lack sufficient accuracy and are often complex and redundant. To address these issues, a lightweight real-time detection method based on ground-penetrating radar is proposed in this study. First, a field GPR image dataset of asphalt roads was collected and constructed. To address the limited defect sample data acquired by GPR, an efficient copy-and-paste augmentation method was employed. This method involved copying and pasting defect samples in GPR images while incorporating random scale jitter and position migration operations to generate a sufficient number of real defect samples. Second, the C2f-DSConv module and the SE attention mechanism were designed and introduced based on the YOLOv8 network to improve detection accuracy in the complex background environment of GPR images. Finally, a channel pruning strategy was used to prune the improved YOLOv8 network, reducing model complexity while maintaining detection accuracy. The final model achieves an average detection accuracy of 90.9% and a detection speed of 140.9 FPS. The results show that the proposed method combines both detection accuracy and real-time performance, further advancing the engineering application of internal defect detection in asphalt roads. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ground Penetrating Radar (GPR) image detection currently faces challenges such as low accuracy, false detections, and missed detections. To overcome these challenges, we propose a novel model referred to as GDS-YOLOv8n for detecting common underground targets in GPR images. The model incorporates the DRRB (Dilated Residual Reparam Block) feature extraction module to achieve enhanced multiscale feature extraction, with certain C2f modules in the YOLOv8n architecture being effectively replaced. In addition, the space-to-depth Conv downsampling module is used to replace the Conv modules corresponding to feature maps with a resolution of 320×320 pixels and less. This replacement assists in mitigating information loss during the downsampling of GPR images, particularly for images with limited resolution and small targets. Furthermore, the detection performance is enhanced using an auxiliary training module, ensuring performance improvement without increasing inference complexity. The introduction of the Inner-SIoU loss function refines bounding box predictions by imposing new constraints tailored to GPR image characteristics. Experimental results on real-world GPR datasets demonstrate the effectiveness of the GDS-YOLOv8n model. For six classes of common underground targets, including metal pipes, PVC pipes, and cables, the model achieves a precision of 97.1%, recall of 96.2%, and mean average precision at 50% IoU (mAP50) of 96.9%. These results indicate improvements of 4.0%, 6.1%, and 4.1%, respectively, compared to corresponding values of the YOLOv8n model, with notable improvements observed when detecting PVC pipes and cables. Compared with those of models such as YOLOv5n, YOLOv7-tiny, and SSD (Single Shot multibox Detector), our model’s mAP50 is improved by 7.20%, 5.70%, and 14.48%, respectively. Finally, the application of our model on a NVIDIA Jetson Orin NX embedded system results in an increase in the detection speed from 22 to 40.6 FPS after optimization via TensorRT and FP16 quantization, meeting the demands for the real-time detection of underground targets in mobile scenarios.

Ground Penetrating radars (GPR) are essential for detecting buried targets in civilian and military applications, especially given the increasing demand for detecting and imaging small targets within walls. The complex structures and materials of walls pose substantial challenges for precisely reconstructing small targets. To address this issue, this study proposes a multistage cascaded U-Net approach for the three-dimensional reconstruction of small targets within walls. First, we developed a high-resolution detection model and a dataset tailored to handle complex wall scenes. Thereafter, using the Monte Carlo sampling method, we sampled aggregate particle sizes to create a physical three-dimensional aggregate scattering model that satisfies grading requirements, thus enhancing the realism and accuracy of the simulated scenes. Our multistage network design effectively suppresses noise and inhomogeneous clutter in C-scan data, thereby improving signal quality. The preprocessed data are then fed into subsequent network stages to reconstruct the distribution of three-dimensional reconstruction values. In addition, we proposed an adaptive multiscale module and a cascaded network training strategy to better fit small target information in complex scenes. Through comparisons with simulated and measured data, we confirmed the effectiveness and generalizability of our method. Unlike existing techniques, our approach successfully reconstructs small targets within three-dimensional walls, thereby considerably enhancing the peak signal-to-noise ratio and providing critical technical support for accurately detecting small targets within walls.

The failures of culverts can result in water and soil loss in the surrounding subsurface, leading to collapses. In densely constructed urban areas, buried culverts are inaccessible and invisible, making Ground Penetrating Radar (GPR) inspection a widely adopted nondestructive and efficient method. Previous research has employed artificial intelligence to automate GPR interpretation by identifying special radargram textures, but the irregular GPR responses of degraded subsurface soils present a challenge. Therefore, this study proposes a method for preliminary diagnosing large-scale culverts based on GPR wave attributes. The method comprises three parts: (1) evaluating soil compactness by estimating the tortuosity of GPR responses at the culvert top; (2) investigating soil moisture using the attenuation rate of GPR responses; and (3) overlaying the results of the incompact and moist soils to pinpoint potential degradations. Two long-distance culverts were inspected and diagnosed using the proposed method, and three abnormal sections were identified and confirmed by excavation evaluation. The case studies indicate that the proposed method can quickly provide reference for further in-depth investigation, thereby improving GPR survey efficiency and reducing workload by narrowing the investigation scope. Consequently, this study can facilitate large-scale GPR culvert surveys and further safeguard the water system.

Ultra-wideband antennas that operate at low frequencies usually have a large geometry, which makes them very limited in practical engineering application environment where space is often constrained. In this paper, a novel low-frequency low-profile ultra-wideband magneto-electric (ME) dipole antenna is designed for the application in ground penetrating radar (GPR) to detect deeply buried targets. The antenna is designed at the operation frequency of 230 MHz, so that the objects that are buried as deep as 1.5 m can be detected. Compared to the traditional low-frequency _antennas, it has a profile as low as 740 mm $ \times $ × 140 mm $ \times $ × 150 mm ($0.49\lambda \times 0.09\lambda \times 0.10\lambda $ 0.49 λ × 0.09 λ × 0.10 λ). Based on the simulation analysis and validation measurements, the proposed ME dipole antenna has a wide impedance bandwidth for ${S_{11}} \lt - 10{\rm{dB}}$ S 11 < − 10 dB from 135 MHz to 382 MHz even when environmental influence is considered. The antenna is further validated with a GPR experiment, where a pipeline with a diameter of about 200 mm at 1200 mm beneath the road surface is successfully detected by an impulse GPR system where the proposed antennas are integrated. [ABSTRACT FROM AUTHOR]

With the increasing demand for safe, efficient and intelligent coal mining in China, ground-penetrating radar ( GPR), as a high-precision geophysical method, has broad application prospects in underground coal mine detection. Due to the interference of internal noise, external electromagnetic interference, roadway echoes, and inhomogeneous nature of underground media in the ground penetrating radar target echo signal, the false alarm rate is relatively high. Especially for small-sized targets, their echo signals are weak and easily overwhelmed by strong background clutter, significantly hampering detection capabilities of GPR at present. The suppression of this background clutter is crucial for enhancing underground coal seam and structure detection, as well as improving detection accuracy. The fundamental principles, advantages, and limitations of various clutter suppression techniques were comprehensively overviewed, including interference canceling, filtering, decomposition, and network-based methods. Furthermore, it delved into the potential future directions and capabilities of these approaches. With ongoing innovation in clutter suppression technology, further improvements in signal-to-noise ratio and sensitivity of GPR can be anticipated. [ABSTRACT FROM AUTHOR]

Full waveform inversion (FWI) is a high-resolution technique to estimate the parameters of dielectric permittivity (ϵ) and electrical conductivity (σ) and identify the structure of the subsurface for Ground Penetrating Radar (GPR) application. However, permittivity and conductivity parameters can be coupled in bi-parameter GPR inversion. This coupling effect leads to the crosstalk in the FWI result. To solve this problem, we propose a novel approach to use the multi-scale FWI with the hybrid regularization method, which combines Tikhonov and total-variation (TV) regularizers that simultaneously invert the ϵ and the σ parameters, which improve the inversion accuracy and reduce the crosstalk effect. The multi-scale strategy uses the Wiener filtering to process the GPR data in different frequency ranges. Then, the low frequencies signal updates the bottom part and subsequently increases the frequencies to invert for the shallow areas. The Tikhonov regularization stabilizes the reconstruction of the smoothly varying background part. In contrast, Total Variation (TV) regularization can recover the large contrasts associated with the LNAPL model. The new Tikhonov-TV (TT) regularization can mitigate the crosstalk caused by the parameter coupling effect. Numerical tests with typical GPR models demonstrate that the proposed multi-scale TT-FWI strategy can effectively eliminate the crosstalk and improve the reconstruction accuracy when the model parameters have a different structure. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) technology is used for nondestructive testing of roads. It relies on the processing and analysis of electromagnetic waves reflected due to the differences in dielectric properties encountered by electromagnetic pulses during propagation for road quality assessment and detects distresses. The moisture content of the asphalt mixture in asphalt pavement undergoes dynamic changes, affecting the pavement's relative humidity (RH). This alteration in RH has a direct impact on the dielectric properties of the asphalt mixture, thus influencing the accuracy of GPR road detection. Therefore, it is essential to quantify the relationship between RH and the dielectric properties of asphalt mixtures. This study utilized electromagnetic field theory to establish the correlation between air dielectric constant and RH. Dielectric constant tests were performed on commonly used single-phase component materials (limestone, 70# asphalt, SBS-modified asphalt) of asphalt mixtures and different types (asphalt type, asphalt-aggregate ratio, nominal maximum aggregate size) of asphalt mixtures under varying RH. The study quantified the impact of RH on the dielectric properties of asphalt mixtures and developed a CRIM optimization dielectric model that incorporates the RH effect. The research results showed that the dielectric constants of air, limestone, asphalt, and asphalt mixtures increase with the increase in RH. Specifically, the dielectric constants of air and asphalt mixtures exhibit a linear relationship with RH. Moreover, the increase in the dielectric constant of 70# asphalt with RH is greater than that of SBS-modified asphalt. Under identical conditions, reducing the impact of RH on the dielectric properties of asphalt mixtures can be achieved by increasing the asphalt-aggregate ratio, decreasing the nominal maximum particle size of the aggregates, or utilizing SBS-modified asphalt. The CRIM optimization model yielded an overall average relative error of 3.33% for calculating the dielectric constants of different types of asphalt mixtures under the influence of RH, representing at least a 1.67% improvement compared to classical models and the improved model proposed from existing literature. The research outcomes provide significant guidance for enriching and developing the fundamental theory as well as detection evaluation technology of GPR. [ABSTRACT FROM AUTHOR]

The 1980 Ms 6.9 Irpinia earthquake was responsible for the activation or reactivation of numerous gravitative deformations mainly hosted by clayey lithotypes, affecting wide areas of Benevento Province and the Sele and Ofanto R. Valleys. The case of Calitri offers valuable insights into a methodological approach to studying mass movements affecting human settlements. Post-earthquake investigations in Calitri involved extensive geognostic boreholes and in situ surveys, providing substantial data for lithological characterization and landslide modeling. Additionally, over the past two decades, satellite-based techniques have supported the mapping and characterization of ground deformations in this area, improving our understanding of spatiotemporal evolution. Despite these efforts, a detailed subsurface comprehensionof the tectono-stratigraphy and geometriesof gravity-induced deformation remains incomplete. This study aims to enhance our knowledge of gravity-driven deformations affecting urban areas by using deep-penetrating GroundPenetrating Radar (GPR) surveys to identify landslide-related structures, rupture surfaces, and lithological characterization of the involved lithotypes. The integration of GPR surveys with classical morphotectonic analysis led to the delineation of the main subsurface discontinuities (stratigraphy, tectonics, and gravity-related), correlating them with available geognostic data. This approach provided non-invasive, detailed insights into subsurface features and stands out as one of the rare case studies in Italy that employed the GPR method for landslide investigations. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model have been used. The task is to independently predict characteristic parameters of a buried object of diverse radii allocated at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable data structures (raw B-scans, extracted features, consecutive A-scans) with respect to computational cost and accuracy of surrogates. The usage of raw B-scan data and the applications for processing steps on B-scan profiles in the context of object characterization incur high computational cost so it can be a challenging issue. The proposed surrogate model referred to as the deep regression network (DRN) is utilized for time frequency spectrogram (TFS) of consecutive A-scans. DRN is developed with the main aim being computationally efficient (about 13 times acceleration) compared to conventional network models using B-scan images (2D data). DRN with TFS is favorably benchmarked to the state-of-the-art regression techniques. The experimental results obtained for the proposed model and second-best model, CNN-1D show mean absolute and relative error rates of 3.6 mm, 11.8 mm and 4.7%, 11.6% respectively. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources.

Time-Lapse Ground Penetrating Radar (GPR) was employed to study used engine oil (UEO) contamination of sandy environment in laboratory setting. GPR is a near-surface geophysical method that uses electromagnetic field to provide image of the dielectric properties of earth materials to detect structures and changes in material properties within the subsurface. This research aimed to detect, monitor and map the migration of UEO contaminant in sand. The results of this study revealed that the migration of the UEO contamination in homogeneously laid sand is non-uniform. UEO plumes were identified as high amplitude signals with enhanced reflectivity. There was a progressive decrease in GPR signal amplitudes (reflection reduction) within the contaminated area of the tank with time. This decrease of GPR signal amplitudes was interpreted as caused by the evaporation of some portion of the UEO in the vadose zone as a result of temperature increase in time and also due to the occurrence of UEO biodegradation. The time-lapse GPR proved to be an effective technique for detecting, monitoring, and mapping UEO migration within sand tank in laboratory setting.

In this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model have been used. The task is to independently predict characteristic parameters of a buried object of diverse radii allocated at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable data structures (raw B-scans, extracted features, consecutive A-scans) with respect to computational cost and accuracy of surrogates. The usage of raw B-scan data and the applications for processing steps on B-scan profiles in the context of object characterization incur high computational cost so it can be a challenging issue. The proposed surrogate model referred to as the deep regression network (DRN) is utilized for time frequency spectrogram (TFS) of consecutive A-scans. DRN is developed with the main aim being computationally efficient (about 13 times acceleration) compared to conventional network models using B-scan images (2D data). DRN with TFS is favorably benchmarked to the state-of-the-art regression techniques. The experimental results obtained for the proposed model and second-best model, CNN-1D show mean absolute and relative error rates of 3.6 mm, 11.8 mm and 4.7%, 11.6% respectively. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources. [ABSTRACT FROM AUTHOR]

Ground penetrating radar is a high-resolution, efficient, non-destructive geophysical detection method. It is widely used in various application scenarios such as tunnel geological prediction and road maintenance. Ground penetrating radar data contains a variety of valid signals as well as noise. The diffracted waves of ground penetrating radar contain high-resolution small target imaging information. A critical challenge in GPR applications is how to extract diffracted waves from the wave fields. We provide a strategy to achieve this goal by applying the masking filters. Considering the complexity of the ground penetrating radar wave field and the weak energy of the diffracted waves, the median filter is first employed to suppress the linear reflections and then the f-k filter and τ-p filter are implemented to further increase the proportion of diffractions in the wave fields. Three numerical experiments are employed to test the diffraction-separation method. [ABSTRACT FROM AUTHOR]

The occurrence of subsurface karst caves can cause the development of superficial depressions which, in turn, may pose a construction hazard. Identifying such a substratum requires integrated non-invasive measurement methods. The main objective of the study was to demonstrate the effectiveness of the non-invasive ERT, TLS, and GPR survey techniques in identifying the karst floor and determining the direction of discontinuities around the cave. The paper analyzes the limitations of the methods used in the study of heterogeneous media. These limitations are related to the methodology and measurement conditions, data processing, and interpretation in the context of the resolution and depth range. The study was conducted using the example of the Jaskinia pod Świecami cave, formed in the Sarmatianal calcarenites in Poland. The research confirmed its complex karst-anthropogenic genesis. The cave was formed as a result of the infiltration of rainwater and the dissolution of limestone by groundwater, while the paleokarst forms that are characteristic of it and of the surrounding caves and occur in their vicinity, i.e., narrow ridges called "karst candles", were formed as a result of water circulation during the local permafrost degradation in the middle Pleistocene. However, these forms were modified in the Upper Pleistocene and Holocene, as indicated by ERT images. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) has evolved over the years as a profound sensor-based investigation technique operating in a wide range of frequencies ranging from 250 to 1000 MHz and utilizing interaction of electromagnetic waves with subsurface to obtain a pseudo-image of the strata. Often due to constructional negligence and poor standards of construction, the pavement so constructed lacks structural strength and have poor compaction of construction material resulting into air pockets. During rainy season, water seeps into the pavement resulting into settling of sand and gravel underneath the pavement. Hence, when a heavy vehicle crosses over the road patch, it results in subsidence of that patch leading to casualties and property damage. In the present study, a system is developed for classification and identification of hazardous zones on a GPR image that can result in subsidence during rainy season. The system works on extraction of features from GPR response using discrete Fourier Transform. The GPR data collected at two sites in IIT Roorkee campus using 1 GHz antenna are fed to the support vector machine for training the classification system. It has been observed that presence of clay and trapping of water beneath the pavement resulted in subsidence of the patch. From the study, it can be deduced that the GPR can be used as a non-destructive tool for hazardous zone identification and pavement fault detection. [ABSTRACT FROM AUTHOR]

Trees sequester carbon dioxide from the atmosphere through photosynthesis, storing it in branches, stems, and roots, where the belowground carbon fraction, approximately 1/4 of the total amount, exhibits significant interspecies root biomass variability. Estimating the amount of carbon stored in tree roots of different species is key to understanding an important aspect of climate change and exploring how natural forests, urban tree planting policies, and reforestation projects might help to address it. In this context, one of the most prominent Non-Destructive Testing methods capable of estimating the diameter and length of roots at different depths is ground penetrating radar (GPR). It has been widely used for geological, archaeological, and geotechnical studies due to its accuracy in locating buried material in different contexts, although standards for the correct management of datasets related to belowground root systems are still been developed. This paper reports a GPR signal processing flow to estimate the root diameter of three species of tropical forest trees, and to demonstrate the method's viability, a dataset was collected in a study area with a 900 MHz shielded antenna. A multistage data processing flow is then presented, including raw data, file format conversion, zero-time adjustment, background removal, signal gain, Stolt FK migration, and time-to-depth conversion with hyperbolic adjustment velocity. The resulting data were converted from true amplitude data to a trace envelope. High amplitudes on the envelope section, with lateral continuity in parallel sections, were interpreted as roots. However, the interpretation of outcomes encounters notable complexities, primarily attributable to the intricate nature of subsurface root architectures, the soil matrix characterized by significant clay content, and the co-occurrence of buried materials proximate to the arboreal subjects. Consequently, amplitudes discerned within ground penetrating radar (GPR) 2D sections necessitate cautious interpretation, as they are not immediately indicative of subsurface roots. To overcome this difficulty, this study used direct measurements of the roots in the field, to confirm the GPR data. Despite these complexities, the study demonstrates GPR's efficacy, particularly in the uppermost soil layer-a pivotal carbon reservoir with a 96% correlation (R²) between GPR-derived coarse-root diameter estimates and field measurements. [ABSTRACT FROM AUTHOR]

This article presents the results of estimating the scale of the present-day glaciation of the Mongun-Taiga Mountain range (Eastern Altai) based on the decoding multi-time satellite images, GPR data and modelling using GlabTop2 and the Volume-Area Scaling (VAS) method. By 2021, 38 glaciers have been identified according to the hydrological principle and 36 ones – by the morphological principle. The total area is estimated as 17.18 ± 1.13 km2. Since 2010, area of the glaciers has decreased by 15%. The thickness of the glacial complex on the main peak of the Mongun-Taiga Mountain range was measured in the ablation season of 2021. More than 6 km of profiles were obtained by the GPR survey with accuracy of about 1%. Based on these data, the GlabTop2 model was calibrated. Then the spatial distribution of the ice thickness was obtained over the entire massif. The total volume of ice in the f lat-summit glacier № 17 is estimated at 0.202 ± 0.008 km3 of ice. According to the GlabTop2 model with the morphological approach the ice volume of the whole massif was estimated at 0.733 ± 0.052 km3, and with the hydrological approach: 0.888 ± 0.061 km3. Determination of the boundaries of glaciers by the VAS method gave larger values: 0.690 ± 0.038 km3 with a morphological approach and 0.757 ± 0.036 km3 with a hydrological method. Consequently, with the same area of glaciers, volume determined by two different approaches can be rather different. This has a decisive influence on the morphological structure of ice reserves: the role of large forms of glaciation sharply prevails with the morphological approach. Most of the ice is contained in glaciers of the flat summit (27–40%). With the hydrological approach, which is used most often, the role of small forms of glaciation is overestimated. At the same time, the contribution of flat-summit glaciers is estimated at only 2%.

As a country with a rich cultural and historical heritage, there are many grottoes in China. Unfortunately, many grottoes suffer from fracture seepage due to their age, causing weathering damage that requires prompt attention, detection, and repair to preserve them for future generations. In order to preserve the historical and cultural heritage of Beishan, Chongqing, China, this study illustrates how we assess the fracture seepage system of sandstone grottoes within the Penglaizhen Formation using multiple geophysical survey techniques. They are core data, conventional logging, borehole televiewer, and ground penetrating radar (GPR). Combining these methods, borehole televiewer and core data can improve the accuracy of conventional logging for stratigraphic delineation, as well as aid GPR in finding deep stratigraphic and seepage fractures. This study provides an effective routine to find sandstone grotto fractures and achieves positive results.

Unsupervised anomaly detection analysis using rebar response rejection is presented for microwave ground penetrating radar (GPR)-based pavement inspections. Various approaches have been used to detect cracks, water, or corrosion in buried objects in the GPR model using synthetic aperture radar (SAR) image. However, this does not always accurately identify an anomaly state because the SAR image largely depends on the selected propagation model (e.g., relative permittivity of background concrete media) or suffers from unnecessary responses such as those from rebars. To address this issue, this article first introduces an effective clutter rejection scheme focusing on the rebar response, using transfer-function-based signal extraction to identify anomalous responses from the boundary between the asphalt and its floorboard. Moreover, we introduce several unsupervised anomaly detection algorithms for time–frequency response data if a large part of the investigation area has normal reflection responses. We performed experimental tests on data from real roads in need of repair to validate that our approach can detect internal anomalies in asphalt or its floorboard.

Ground penetrating radar (GPR) has emerged as a pivotal tool for subsurface explorations, particularly in detecting subsurface defects that might endanger structural integrity. While GPR B-scan data visually depict underground conditions, it represents the time delay and amplitude of the returned electromagnetic (EM) waves, making them complex to interpret due to both their image-like appearance and their inherent waveform changes. To address this complexity, this article introduces the novel temporal–spatial synthesis network, designed to harness both temporal and spatial features for enhanced subsurface defect detection in GPR B-scan data. The echo state network, underpinned by reservoir computing, is utilized to fit the GPR data and capture its “temporal features,” emphasizing the temporal variations present in the EM waves. Concurrently, the convolutional neural network focuses on discerning “spatial features” from the B-scan images, spotlighting spatial patterns that possibly indicate subsurface defects. After extracting these temporal and spatial features, they are synthesized to form a comprehensive representation of the GPR data. The enhanced synthesized feature facilitates precise classification, resulting in heightened differentiation between normal and defect-contained subsurface areas. Experiments on real-world GPR datasets are conducted, with the results underscoring the efficacy of the proposed approach.

Denoising plays a fundamental role in ground penetrating radar (GPR) data processing and determines the effect of anomaly extraction, inversion imaging, and other subsequent processing. In recent years, the sparse dictionary representation method k-singular value decomposition (K-SVD) based on K-means, which can adaptively change the basis function according to the data, has become a hotspot in the field of image denoising and data reconstruction. Nevertheless, the SVD is a time-consuming calculation, especially unacceptable in multidimensional problems; we introduce a dictionary learning method based on the sequential generalized K-means (SGK), where the dictionary atoms are updated by the arithmetic average of several training signals instead of a great deal of SVD calculation in K-SVD. We establish a 3-D road simulation model and conduct finite-difference time-domain forward numerical simulation to acquire 3-D GPR data. Through three sets of experiments on 3-D numerical examples and 3-D field data, the results show that both dictionary learning algorithms can successfully remove random noise from GPR data even at a lower input signal-to-noise ratio. The clutter interference in the random medium forward data can be effectively eliminated simultaneously, and both denoising methods exhibit promising applications in 3-D field data. However, the SGK method solves the serious problem of computational efficiency to a certain extent. The computational acceleration ratio of SGK remains consistently above 7.5× that of the K-SVD algorithm in multigroup experiments, with only a marginal decline in denoising performance.

Ground Penetrating Radar (GPR) is an effective non-destructive detection method, that is frequently utilized in the detection of urban underground defects because of its quick speed, convenient and flexible operation, and high resolution. However, there are some limitations to defect detection using GPR, such as less data, poor data quality, and complexity of data interpretation. In this study, an underground defect detection system based on GPR was established. First, a Simple Linear Iterative Clustering (SLIC)-PHash, a Data Augmentation (DA) optimization algorithm, was created to obtain high-quality datasets. Second, the Convolutional Block Attention Module (CBAM)-YOLOv8, a detection model, was produced for the recognition of defects. This model uses GhostConv and CBAM to create a lighter design that better focuses on target detection and increases efficiency. Finally, a one-click detection system was formed by fusing SLIC-Phsh and CBAM-YOLOv8, which were used for one-click GPR dataset optimization and defect detection. The developed system has the best detection mAP and F1 scores of 90.8% and 88.3%, respectively, compared to several well-known Deep Learning (DL)-based techniques. The results demonstrated that the system proposed in this paper can greatly improve detection efficiency and reduce detection time by achieving a good balance between detection speed and accuracy.

Ground penetrating radar (GPR) systems on board unmanned aerial vehicles (UAVs) have been successfully used for subsurface imaging applications. Their capability to detect buried targets avoiding the contact with the soil turn these systems into a great solution to detect buried threats, such as landmines and improvised explosive devices. Significant advances have been also conducted to enhance the detection capabilities of these systems, complementing the synthetic aperture radar (SAR) processing methods with several clutter mitigation techniques. However, the improvement in the scanning throughput (i.e., increasing the inspected area in a given time) remains a significant challenge. In this regard, this article compares several scanning strategies for UAV-mounted multichannel GPR-SAR systems using antenna arrays. In particular, two different scanning strategies have been compared: a uniform scheme and a nonuniform strategy called 3X. In addition, different across-track spacing values to generate dense and sparse sampling distributions were considered for each scanning scheme. After conducting a theoretical analysis of these strategies, they have been experimentally validated with measurements gathered with a portable scanner and during flights in realistic scenarios. Results show that the dense configurations of both scanning strategies yield good quality images of buried targets while improving the scanning throughput (compared to a single-channel architecture). In particular, the dense uniform scheme (with a 20-cm across-track spacing) achieves a greater reduction in the inspection time, compared to the dense 3X strategy, at the expense of a slightly smaller signal to clutter ratio.

New government policy in China has facilitated research on the hazards in open-pit mine slopes. Remote sensing, geological, geophysical, and computer simulation techniques have been applied to obtain better information in slope investigation. In this study, we investigated the slope of the Qianlingshan open-pit mine area in Beijing, China. Electrical resistivity tomography and ground-penetrating radar were used, and the results were compared with borehole data to ascertain the slope structure, potential slip surface, and deformation zone. A geological model of the slope was established, and the slope stability was analyzed by numerical simulation. The results showed that the slope had an unstable surface layer over bedrock, and probable creep sliding-tension deformation. In addition, the slope would become less stable in wet conditions. The geological methods used in this study will provide a useful reference for further slope stability analysis and prevention. We took the Qianlingshan open-pit slope in Beijing, China, as the research object, by comprehensive application of historical remote sensing images analysis, field investigation, electrical resistivity tomography (ERT) and ground penetrating radar (GPR), and comparative analysis with borehole data, to obtain the slope parameter such as resistivity and velocity of electromagnetic wave, and to ascertain the slope structure, potential slip surface and deformation zone. On this basis, the geological generalisation model of slope was established, and the failure mechanism and stability of slope were analyzed by numerical simulation. [ABSTRACT FROM AUTHOR]

This paper presents a set of Ground Penetrating Radar (GPR) data obtained from in situ measurements conducted in four ornamental stone quarries located in Italy (Botticino quarry) and Romania (Ruschita, Carpinis, and Pietroasa quarries). The GPR is a Non-Destructive Testing (NDT) technique that enables the detection and localization of fractures without damage to the surface, among other capabilities. In this study, two instruments of ground-coupled GPR were used to detect and locate the fractures, discontinuities, or weakened zones. The GPR data contains radargrams for discontinuities and fracture detection, besides the geographic location of the measures. For each measurement site, a set of radargrams has been acquired in two orthogonal directions, allowing for a 3D reconstruction of the investigated site. Dataset: DOI:10.17632/w26n6nftxs.3 (https://data.mendeley.com/datasets/w26n6nftxs/3) Dataset License: CC BY-NC-ND [ABSTRACT FROM AUTHOR]

Time-Lapse Ground Penetrating Radar (GPR) was employed to study used engine oil (UEO) contamination of sandy environment in laboratory setting. GPR is a near-surface geophysical method that uses electromagnetic field to provide image of the dielectric properties of earth materials to detect structures and changes in material properties within the subsurface. This research aimed to detect, monitor and map the migration of UEO contaminant in sand. The results of this study revealed that the migration of the UEO contamination in homogeneously laid sand is non-uniform. UEO plumes were identified as high amplitude signals with enhanced reflectivity. There was a progressive decrease in GPR signal amplitudes (reflection reduction) within the contaminated area of the tank with time. This decrease of GPR signal amplitudes was interpreted as caused by the evaporation of some portion of the UEO in the vadose zone as a result of temperature increase in time and also due to the occurrence of UEO biodegradation. The time-lapse GPR proved to be an effective technique for detecting, monitoring, and mapping UEO migration within sand tank in laboratory setting. [ABSTRACT FROM AUTHOR]

After the discovery of an archive document regarding an underground crypt beneath the floors of the Church of St. Margaret (Sv. Marjeta) in Dol pri Ljubljani, Slovenia, further research was carried out to confirm its presence. An area filled with construction waste was discovered during a recent small-scale renovation of the church floor. This finding suggested the potential underground chamber may have been partly filled in during one of the previous restorations. A non-invasive GPR study was carried out along eight profiles inside the church to prove the existence of an underground crypt. Results show the presence of an air-filled chamber, confirmed later by a hole drilled in the floor. Additional findings in the church archive and pictures taken by a camera, lowered through a drilled hole, revealed three previously unknown caskets in the crypt. According to the archives, two of them belong to Baron Wolf Daniel Erberg and his wife who died in 1783 and 1774, respectively.

The 1980 Ms 6.9 Irpinia earthquake was responsible for the activation or reactivation of numerous gravitative deformations mainly hosted by clayey lithotypes, affecting wide areas of Benevento Province and the Sele and Ofanto R. Valleys. The case of Calitri offers valuable insights into a methodological approach to studying mass movements affecting human settlements. Post-earthquake investigations in Calitri involved extensive geognostic boreholes and in situ surveys, providing substantial data for lithological characterization and landslide modeling. Additionally, over the past two decades, satellite-based techniques have supported the mapping and characterization of ground deformations in this area, improving our understanding of spatiotemporal evolution. Despite these efforts, a detailed subsurface comprehensionof the tectono-stratigraphy and geometriesof gravity-induced deformation remains incomplete. This study aims to enhance our knowledge of gravity-driven deformations affecting urban areas by using deep-penetrating GroundPenetrating Radar (GPR) surveys to identify landslide-related structures, rupture surfaces, and lithological characterization of the involved lithotypes. The integration of GPR surveys with classical morphotectonic analysis led to the delineation of the main subsurface discontinuities (stratigraphy, tectonics, and gravity-related), correlating them with available geognostic data. This approach provided non-invasive, detailed insights into subsurface features and stands out as one of the rare case studies in Italy that employed the GPR method for landslide investigations.

Trees sequester carbon dioxide from the atmosphere through photosynthesis, storing it in branches, stems, and roots, where the belowground carbon fraction, approximately ¼ of the total amount, exhibits significant interspecies root biomass variability. Estimating the amount of carbon stored in tree roots of different species is key to understanding an important aspect of climate change and exploring how natural forests, urban tree planting policies, and reforestation projects might help to address it. In this context, one of the most prominent Non-Destructive Testing methods capable of estimating the diameter and length of roots at different depths is ground penetrating radar (GPR). It has been widely used for geological, archaeological, and geotechnical studies due to its accuracy in locating buried material in different contexts, although standards for the correct management of datasets related to belowground root systems are still been developed. This paper reports a GPR signal processing flow to estimate the root diameter of three species of tropical forest trees, and to demonstrate the method’s viability, a dataset was collected in a study area with a 900 MHz shielded antenna. A multi-stage data processing flow is then presented, including raw data, file format conversion, zero-time adjustment, background removal, signal gain, Stolt FK migration, and time-to-depth conversion with hyperbolic adjustment velocity. The resulting data were converted from true amplitude data to a trace envelope. High amplitudes on the envelope section, with lateral continuity in parallel sections, were interpreted as roots. However, the interpretation of outcomes encounters notable complexities, primarily attributable to the intricate nature of subsurface root architectures, the soil matrix characterized by significant clay content, and the co-occurrence of buried materials proximate to the arboreal subjects. Consequently, amplitudes discerned within ground penetrating radar (GPR) 2D sections necessitate cautious interpretation, as they are not immediately indicative of subsurface roots. To overcome this difficulty, this study used direct measurements of the roots in the field, to confirm the GPR data. Despite these complexities, the study demonstrates GPR’s efficacy, particularly in the uppermost soil layer-a pivotal carbon reservoir with a 96% correlation (R2) between GPR-derived coarse-root diameter estimates and field measurements.

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.

The non-destructive testing of wood fibre properties is crucial for informing forest management decisions and achieving optimal resource utilization. Moisture content (MC) is an important indicator of wood freshness and may reveal the presence of wood degradation. However, efficient methods are still needed to better monitor this property along the forest–wood value chain. The objective of the study was to develop prediction models to evaluate log MC based on the propagation of ground penetrating radar (GPR) signals. A total of 165 trees representing four species (black spruce (Picea mariana (Mill.) B.S.P.), white spruce (Picea glauca (Moench) Voss), red spruce (Picea rubens Sarg.), and balsam fir (Abies balsamea (L.) Mill.)) were harvested in two regions of the province of Quebec. GPR signals were acquired in the green (fresh) state and at three subsequent drying stages. Partial least squares regression (PLSR) and locally weighted PLSR (LWPLSR) were employed to establish relationships between GPR signals (antenna frequency: 1.6 GHz) and log properties. The models were fitted on three calibration sets containing four drying stages and different species mixes. The LWPLSR models performed better than the PLSR models for predicting log MC, with a lower root mean square error (RMSEp range: 10.8%–20.2% vs. 13.0%–20.5%) and a higher R2p (0.63–0.87 vs. 0.62–0.82). Spruce-only models performed considerably better than fir-only models while multi-species models were in-between. Despite the complex anisotropy of wood and the physics of wave propagation, the GPR technology can be successfully used to estimate log moisture content, but the GPR-based MC models should be calibrated for each specific type of wood material. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Agrochemistry & Soil Science / Agrokémia és Talajtan is the property of Akademiai Kiado and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ground penetrating radar (GPR) is widely used in subsurface utility mapping. It is a nondestructive tool that has gained popularity in supporting underground drilling projects such as horizontal directional drilling (HDD). Even with the benefits including equipment portability, low cost, and high versatility in locating underground objects, GPR has a drawback of the time spent and expertise needed in data interpretation. Recent researchers have shown success in utilizing machine learning (ML) algorithms in GPR images for the automatic detection of underground objects. However, due to the lack of availability of labeled GPR datasets, most of these algorithms used synthetic data. This study presents the application of the state-of-the-art You Only Look Once (YOLO) v5 algorithm to detect underground objects using GPR images. A GPR dataset was prepared by collecting GPR images in a laboratory setup. For this purpose, a commercially available 2GHz high-frequency GPR antenna was used, and a dataset was collected with images of metal and PVC pipes, air and water voids, and boulders. The YOLOv5 algorithm was trained with a dataset that successfully detected and classified underground objects to their respective classes. [ABSTRACT FROM AUTHOR]

For the problem of strong clutter when using ground penetrating radar (GPR) for landmine detection, a low rank and sparse decomposition (LRSD) based method is proposed. The proposed method introduces weighted nuclear norm (WNN) into robust principle component analysis (RPCA) and combines randomized singular value decomposition (RSVD) with the alternating direction method of multipliers (ADMM) to solve the optimization function, which suppresses the clutter by treating the target image as sparse component and the clutter image as low rank component and enhance the accuracy and efficiency of the proposed algorithm. The experimental results show that the proposed method can significantly improve the signal-to-clutter ratio (SCR) of the imaging results and the computation efficiency is over five times than that of the RPCA, which demonstrates the efficiency in clutter mitigation. [ABSTRACT FROM AUTHOR]

This paper describes scientific research conducted to highlight the potential of an integrated GPR-UAV system in engineering-geological applications. The analysis focused on the stability of a natural scree slope in the Germanasca Valley, in the western Italian Alps. As a consequence of its steep shape and the related geological hazard, the study used different remote sensed methodologies such as UAV photogrammetry and geophysics survey by a GPR-drone integrated system. Furthermore, conventional in-situ surveys led to the collection of geological and geomorphological data. The use of the UAV-mounted GPR allowed us to investigate the bedrock depth under the detrital slope deposit, using a non-invasive technique able to conduct surveys on inaccessible areas prone to hazardous conditions for operators. The collected evidence and the results of the analysis highlighted the stability of the slope with Factors of Safety, verified in static conditions (i.e., natural static condition and static condition with snow cover), slightly above the stability limit value of 1. On the contrary, the dynamic loading conditions (i.e., seismic action applied) showed a Factor of Safety below the stability limit value. The UAV-mounted GPR represented an essential contribution to the surveys allowing the definition of the interface debris deposit-bedrock, which are useful to design the slope model and to evaluate the scree slope stability in different conditions. [ABSTRACT FROM AUTHOR]

In this paper, we adapt AI techniques, especially YOLOv5 algorithm, for the detection and classification of underground buried objects in B-scan ground penetrating radar (GPR) images. We used gprMax toolbox to prepare the dataset and we considered seven models: voids, tunnels, groundwater, rebar and subsurface concrete. Data augmentation was applied to enlarge the Dataset. The recognition results were promising and the algorithm was tested on real GPR images taken from different references. This study shows that YOLOv5 can detect and classify underground targets, which makes it a promising methodology for practical investment to analyze this kind of specific GPR data even with few training samples.

AbstractThe Mirpur Mw 5.8 earthquake on September 24, 2019, produced extensive liquefaction-induced surface deformation (LISD) in the surrounding villages. Due to the complexity of seismic hazards and the occurrence of their effects on a large spatial scale, the resulting surface, and subsurface deformation are often poorly resolved. To cover spatially extended LISD, the PSInSAR technique provided subsidence and uplift rate values ranging from −110 to +145 mm/yr consistent with the spatial distribution of the mapped liquefaction features. The most prominent surface change occurred in Abdupur and Sang villages. GPR measurements were conducted to map the near-surface cracks produced by transported liquified sand into the shallow subsurface layers and other liquefaction features (elevated groundwater table, conductive clay pockets, fractures, sand dikes, and water-enriched zones). Thus, the GPR survey assisted in the reconstruction of these structural and hydrogeological features on the near surface. In addition, the highly vulnerable zones were identified and mapped using space- and ground-based remote sensing measurements supported by the field observations. The results highlight the effectiveness of the proposed novel approach for detailed assessment of the coseismic liquefaction-induced deformation on- and near-ground surfaces by identifying areas prone to failure during earthquakes and thereby can help with hazard mitigation.

Good feature engineering is a prerequisite for accurate classification, especially in challenging scenarios such as detecting the breathing of living persons trapped under building rubble using bioradar. Unlike monitoring patients' breathing through the air, the measuring conditions of a rescue bioradar are very complex. The ultimate goal of search and rescue is to determine the presence of a living person, which requires extracting representative features that can distinguish measurements with the presence of a person and without. To address this challenge, we conducted a bioradar test scenario under laboratory conditions and decomposed the radar signal into different range intervals to derive multiple virtual scenes from the real one. We then extracted physical and statistical quantitative features that represent a measurement, aiming to find those features that are robust to the complexity of rescue-radar measuring conditions, including different rubble sites, breathing rates, signal strengths, and short-duration disturbances. To this end, we utilized two methods, Analysis of Variance (ANOVA), and Minimum Redundancy Maximum Relevance (MRMR), to analyze the significance of the extracted features. We then trained the classification model using a linear kernel support vector machine (SVM). As the main result of this work, we identified an optimal feature set of four features based on the feature ranking and the improvement in the classification accuracy of the SVM model. These four features are related to four different physical quantities and independent from different rubble sites. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a mainstream tool to detect defects behind tunnel linings, but the difficulty in interpreting GPR signals limits its application. This paper proposes an intelligent way to differentiate the defects behind tunnel linings by means of a model test and feature parameter analysis, to achieve an efficient and accurate analysis of GPR signals. The model test reveals the differences between defect and non-defect GPR data in amplitude, signal entropy, standard deviation, and other feature parameters. The amplitude of the defect data was on average six times as large as that of non-defect data, and the signal entropy was about 1.2 times larger. The two feature parameters lay the basis for defect detection by GPR. On this basis, a GPR-based automatic identification method was proposed, and the program was compiled on MATLAB. The program achieved a defect recognition rate as high as 96% through a field test at a rapid speed. The program is highly accurate and feasible for detecting defects behind tunnel linings with the aid of GPR. [ABSTRACT FROM AUTHOR]

The Ground Penetrating Radar (GPR) method is high-resolution surveys with an electromagnetic pulse reflection method for shallow earth layers. This method is similar to reflection seismography, which operates on the basis of wave propagation and reflection. In a simple case, a GPR device consists of a signal generator. The signal generated by this generator is sent into the earth by the transmitter antenna. Waves travel through the earth at a high speed and when these waves hit an object or reflection surface, due to the change of electromagnetic impedance in these places, part of the waves will be reflected from the surface. The receiving antenna located in the device shows these reflections as a high peak and records the movement time and amplitudes of the reflections. The electromagnetic wave inside the earth moves downwards in the form of a three-dimensional cone, and at the same time, factors affect the speed and loss of the amplitude of these waves. In fact, the electromagnetic characteristics of the materials, which depend on the constituent materials and the amount of water in them, will have a great effect on the speed and loss of the range of the radar waves penetrating the ground. Some materials, such as polar ice, are transparent to ground-penetrating radar waves, and these waves can pass through it without much loss. Some other materials such as clays saturated with water and also sea water is cloudy to these waves and reflect or absorb these waves. Therefore, radar waves penetrating the ground are attenuated in short distances and cannot propagate in such environments. Considering the high-resolution power of the ground penetrating radar method, as well as the high speed and ease of acquisition, it can be said that this method is useful in investigating and detecting shallow subsurface targets similar to subsurface holes, canals and other targets that have a different dielectric constant from the surrounding environment. It is an acceptable method. In addition to the effect of the frequency of the waves sent from the GPR antenna, other factors such as soil moisture, the amount of fine-grained clay materials, or the fineness of the sediments in general, reduce the depth of investigation or penetration of GPR waves. This problem is mainly caused by the higher electrical conductivity due to the presence of moisture or fine-grained sediment particles compared to coarse-grained sediments. In this study, a ground penetrating radar device with 80 MHz antenna was used and seven profiles with a total length of 662 meters were taken to investigate the intersection of Kargar Street in Tehran. The radargrams have been processed and the anomalies related to the meetings have been identified on them. These anomalies are discussed in the next step on the positioning satellite images and on the obtained results. According to the estimated results, there is a good agreement between the anomalies detected in the parallel profiles, which can be proof of the acceptable identification of possible anomalies in the study area. [ABSTRACT FROM AUTHOR]

After the discovery of an archive document regarding an underground crypt beneath the floors of the Church of St. Margaret (Sv. Marjeta) in Dol pri Ljubljani, Slovenia, further research was carried out to confirm its presence. An area filled with construction waste was discovered during a recent small-scale renovation of the church floor. This finding suggested the potential underground chamber may have been partly filled in during one of the previous restorations. A non-invasive GPR study was carried out along eight profiles inside the church to prove the existence of an underground crypt. Results show the presence of an air-filled chamber, confirmed later by a hole drilled in the floor. Additional findings in the church archive and pictures taken by a camera, lowered through a drilled hole, revealed three previously unknown caskets in the crypt. According to the archives, two of them belong to Baron Wolf Daniel Erberg and his wife who died in 1783 and 1774, respectively. [ABSTRACT FROM AUTHOR]