Search

In this paper, a fluted E‐shaped antipodal Vivaldi antenna (AVA) loaded with rectangular‐shaped strips is proposed for ground‐penetrating radar (GPR) application. The corrugation method is implemented by introducing multiple slots in the uppermost and lowermost arms of the antenna, followed by adding additional 10 strips to broaden and stabilize the impedance bandwidth. The electrical dimension of the antenna is 1.07λ × 1.8λ × 0.004λ. The proposed modified AVA (MAVA) exhibits a high fractional bandwidth of 144.8% over an ultrawideband of 1.6–10 GHz. The peak gain achieved by the antenna is 8.97 dBi. The proposed antenna offers a stable unidirectional radiation pattern along E‐ and H‐plane throughout the spectrum with maximum radiation efficiency of 96%. A prototype of the proposed antenna has been fabricated and tested where the measured results bear close resemblance with the simulated results. The pertinence of proposed antenna for GPR subsurface scanning was examined through a field test where scanned result signifies its aptness for such submissions. [ABSTRACT FROM AUTHOR]

Storm deposits in marine facies are distributed widely across all geological periods, greatly extending the historical record of storms and providing valuable insights into the ongoing debate regarding the link between tropical cyclone activity and global climate change. This paper reviews the processes for identifying, interpreting, and analyzing the evidence of paleostorms across various depositional environments. We developed a range of regional storm deposit models and contributed to paleostorm reconstruction efforts, including studies on distinguishing storm deposits from tsunami deposits. Research has indicated that the sedimentary characteristics of storm deposits, such as lithology, sedimentary structures, and palaeontological markers, are effective in identifying storm events. This paper presents examples of storm deposits from the Late Cambrian in the Alborz region of Iran, the Early Paleocene in southwestern Iran, and the Middle Holocene in Durban, South Africa and examines the influence of storm activity on the formation of banded rock features and the impact of storm deposits on hydrocarbon reservoirs. Furthermore, this study explores advanced methods for studying storm deposits, including microfossil tracing, geochemical analyses, laboratory analog experiments, ground-penetrating radar (GPR), and the identification of mineral luminescence properties. The findings suggest that storm deposits not only shed light on the relationship between storm activity and global climate change but also offer new ideas for hydrocarbon exploration and development. Studying storm deposits in marine environments is critical for mitigating the damage and economic losses from extreme tropical cyclones and advancing the field of storm sediment research within geology. [ABSTRACT FROM AUTHOR]

Aiming to address the issue of significant artefact interference in conventional reverse time migration (RTM) imaging under zero-time imaging conditions of ground-penetrating radar (GPR), this study proposes an improvement of RTM using cross-correlation superposition projection (CSP) to enhance imaging quality. The method is based on the original RTM imaging framework, where all single-trace signals are individually processed using RTM. The individual imaging results are then multiplied pairwise and added together to create a CSP. After normalizing the CSP and properly setting the adaptive threshold, the CSP can focus RTM imaging on the GPR signal reflection interface area, effectively removing artefact interference. Synthetic data test confirms that the CSP-improved RTM, when compared to conventional RTM and Laplace filtering methods, not only preserves all effective information in imaging but also eliminates artefact interference. This improvement significantly enhances the quality and resolution of the imaging results. Furthermore, the practicality and effectiveness of the CSP-improved RTM method in engineering applications have been validated using a set of measured GPR data. [ABSTRACT FROM AUTHOR]

As a contactless and non-invasive tool, ground penetrating radar (GPR) plays an important role in buried object detection. However, the performance of GPR images is deteriorated severely due to the clutter in radar echo signals, including echoes reflected from ground-air surface and other undesired signals. Low-rank sparse decomposition (LRSD) has been proved to be an effective tool to separate clutter and targets as low-rank and sparse components respectively. However, the properties of targets in images cannot be fully represented by sparsity. To combine LRSD with characters of target images, a robust principal component analysis combined with top-hat transform (RPCA-THT) is proposed. RPCA-THT optimizes the step of shrinking the sparse component in robust principal component analysis (RPCA). It performs a top-hat transform on the sparse component with a specially designed kernel matrix. Then the sparse component shrinks according to the normalized top-hat transform of the sparse matrix. In this way, targets prefer to be left in the sparse component than clutter. We design a well-performed kernel for distinguishing targets in the sparse matrix. The experimental results on simulation and real data show that the proposed method has better performance than several state-of-the-art clutter suppression algorithms. [ABSTRACT FROM AUTHOR]

Obtaining information on tectonic tendencies is a prerequisite for intelligent and accurate mining in mines. In the special mine environment, the co‐polarized ground‐penetrating radar can only identify the spatial location of faults, and it is difficult to analyse the inclination information of fault structures. This paper proposes a mine full‐polarimetric ground‐penetrating radar fault tendency detection method based on this. First, based on the stacking characteristics of the coal depositional, this paper analyses the propagation law of the pulse electromagnetic wave in the coal seam and puts forward the assumption of the overlapping echo reflection of the fault structure. The reasonableness of the fault reflection assumption is verified through a numerical simulation study. Second, based on the cutting relationship of the fault to the coal seam, we divided the reflection structure of the fault structure into plane scattering and dihedral angle scattering. We realized the mingled echoes' decomposition using the improved Yamaguchi decomposition technique. To analyse the applicability of the modified Yamaguchi and Freeman decomposition methods in the identification of fault inclination, we use the upright fault simulation data for the discussion, and we find that the improved Yamaguchi decomposition method is more advantageous in the identification of fault inclination in the mine. The decomposition results based on the simulation data of fault models with different dip angles found that when the dip angle of the fault is less than 90°, the scattering of the fault structure is dominated by planar scattering and dihedral angle scattering; when the dip angle of the fault is greater than or equal to 90°, the scattering of the fault structure is dominated by planar scattering, and the scattering power of the dihedral angle model is zero. By analysing the effect of fault strike on the decomposition results, it is found that the fault strike angle has little effect on the identification of fault tendency. Finally, the application potential of this paper's method is tested by constructing complex numerical models and probing experiments. Therefore, the method proposed in this paper can solve the fault tendency identification under a thick coal seam. [ABSTRACT FROM AUTHOR]

Bentonite is often considered as buffer material for deep geological radioactive waste repositories. To support decision making and safety assessment of radioactive waste repositories, international agencies and research institutions proposed the implementation of monitoring programmes. While the overall concepts of such monitoring programmes have been largely developed, the selection of key observations parameters, such as temperature, pressure and water content, and the technical implementation are still under development. The direct measurement of such parameters requires the placement of sensors inside a repository, which can significantly affect its safety functions and only provides information at the typically sparse sensor locations. Geophysical tomography can help gaining valuable insights into the state of the repository non-invasively by providing images of the distribution of geophysical parameters from measurements that are purely taken from the outside. However, the extracted geophysical parameters are often difficult to interpret and the geophysical tomography problem is non-unique, meaning that there exist multiple models that explain the data equally well. Here, we demonstrate that this non-uniqueness can be significantly reduced by simultaneously employing multiple geophysical methods in a joint tomography scheme. We simultaneously invert seismic and ground penetrating radar (GPR) traveltimes and amplitudes by imposing structural similarity constraints on the tomographic velocity and attenuation images. The resulting, estimated geophysical parameter maps show a strongly improved correlation when compared to results obtained from individual inversions, which in turn facilitates the establishment of constitutive relationships between the geophysical parameters (seismic and GPR velocity and attenuation) with the water content, as key parameter for the evaluation of the state of a radioactive waste repository. Using data from the full-scale emplacement (FE) experiment, we employ a supervised machine-learning model that enables the translation of the tomographic velocity and attenuation images obtained in bentonite to an image of the distribution of the water content inside the repository, where the machine learning model is trained using direct point measurements of the water content at sparse locations inside the tomographic plane. Due to the lack of direct water content sensors in the FE experiment, we use neutron log data (which are directly linked to water content) to train the machine learning model. Ultimately, this enables us to extrapolate the sparse neutron log data to a spatially cohesive distribution inside the repository corresponding to a visualization of the spatial distribution of water content. [ABSTRACT FROM AUTHOR]

De Geer moraines (DGMs) may act as valuable ice margin indicators; however, to date, their variable mode of formation has presented challenges for this utility. Morphometric investigations provide useful insights into formation processes, which can be developed using sedimentological and geophysical methods. Here we present sedimentological and ground penetrating radar (GPR) data of DGMs located in southwest Finland. Individual lithofacies were identified and interpreted using sediment architectural elements. These were correlated with neighbouring GPR radargrams and extrapolated across the wider study area. Generally, internal architecture presents a multi‐phase structure with lower units representing subglacial traction till and ice margin infill deposits, truncated by a larger prominent push unit, which is then successively deformed via the overriding of active ice. Significantly, there are notable differences between proximal and distal structures, with proximal sides characterized by silts, clays, and diamicton with laminae, stratification and thrust planes, and distal sides characterized by poorly consolidated diamicton and proglacial water current reworkings. Internal architecture of both prominent and intermediate ridges is very similar, reflecting similar formation processes, however, slight differences also reflect inter‐seasonal variations. Based on our findings, we present an integrated conceptual model for the genesis of DGMs whereby inter‐seasonal ridge forming processes occur within a sub‐aqueous ice‐marginal environment. Our model highlights that DGMs can be subcategorized as: (i) sediment deposition at an unstable margin during summer calving, and/or (ii) sediment pushing at a stabilized margin during a winter re‐advance. We do not find evidence of crevasse filling as a mechanism for DGM formation. We propose a landform assemblage classification whereby ‘De Geer terrain’ is used to describe series of parallel ridges arranged in a typical washboard‐like configuration. This classification identifies all DGMs derived within a sub‐aqueous ice‐marginal environment, whilst also capturing the equifinal characteristics between individual landforms. [ABSTRACT FROM AUTHOR]

Introduction: Dobšiná Ice Cave (Slovakia) has attracted the attention of many researchers since its discovery more than 150 years ago. Although the cave is located outside the high-mountain area, it hosts one of the largest volumes of underground perennial ice. The topographic mapping of this unique UNESCO Natural Heritage site has led to several historical surveys. In the last decades of rapid climate change, this natural formation has been subject to rapid changes that are dynamically affecting the shape of the ice body. Increased precipitation, the rise in year-round surface temperatures, and the gravity cause significant shape changes in the ice filling. Methods: This paper describes modern technological tools to comprehensively survey and evaluate interannual changes in both the floor and wall of the underground ice body. Technologies such as digital photogrammetry, in combination with precise digital tacheometry and terrestrial laser scanning, make it possible to detect ice accumulation and loss, including the effect of sublimation due to airflow, as well as sliding movements of the ice body to the lower part of the cave. To get a comprehensive model of the ice volume, geophysical methods (microgravimetry and ground penetrating radar) have been added to determine the thickness of the floor ice in the upper parts of the cave in the last 2 years. Results: Between 2018 and 2023, the ice volume in certain sections of the cave decreased by up to 667 m³, with notable reductions in ice thickness ranging from 0.3 to 0.9 m in areas like the Small Hall and Collapsed Dome. The study also detected dynamic changes, such as the widening of the ice tunnel by 20 cm in some sections, and a vertical ice wall in Ruffinyi's Corridor showed localized volume losses up to 9 m3 (between 2018 and 2023). Additional geophysical methods - microgravimetry and ground penetrating radar - revealed an average ice thickness ranging from 10 to 25 m. Discussion: The paper not only highlights the current technological possibilities but also points out the limitations of these technologies and then sets out solutions with a proposal of technological procedures for obtaining accurate geodetic and geophysical data. [ABSTRACT FROM AUTHOR]

Semi-rigid bases are widely used in road construction due to their excellent properties, high rigidity, and frost resistance, and they have been in service for many years. However, as the service life increases, the maintenance demands also grow, with traditional maintenance methods still being the primary approach. Based on a typical case using ground-penetrating radar (GPR) technology, this study explores the issue of cracks in semi-rigid bases and their impact on overlay layers. The findings indicate that the overlay layer at semi-rigid base cracks struggles to withstand significant tensile and shear stresses, leading to reflective cracking and reducing pavement durability. To address this problem, this paper investigates the application potential of crushing technology in maintaining semi-rigid bases. Crushing technology has been widely employed in the maintenance of cement concrete panels, effectively eliminating reflective cracks and extending the service life of overlays. Given that semi-rigid bases share similar high-strength characteristics with cement concrete panels, crushing technology shows considerable applicability in semi-rigid base maintenance. This study employs a finite element analysis method to establish a semi-rigid base model under the impact load of multi-hammer equipment. It examines its dynamic mechanical response and evaluates the feasibility and effectiveness of crushing technology for semi-rigid base maintenance. Additionally, this study investigates the influence of the crushed layer's modulus and thickness on key mechanical design indicators of the overlay and proposes recommendations for optimal design parameters. The research results provide valuable references for the design of the thickness and modulus in maintaining and repairing semi-rigid bases, contributing to improving pavement performance and durability. [ABSTRACT FROM AUTHOR]

Commonly found landmines, such as the TM-62M, MON-100, and PDM-1, in the recent Russia–Ukraine war confirm the continued use of metals in munitions. Traditional demining techniques, primarily relying on handheld metal detectors and Ground Penetrating Radar (GPR) systems, remain state of the art for subsurface detection. However, manual demining with handheld metal detectors can be slow and pose significant risks to operators. Drone-based metal detection techniques offer promising solutions for rapid and effective landmine detection, but their reliability and accuracy remain a concern, as even a single missed detection can be life-threatening. This study evaluates the potential of an airborne metal detection system as an alternative to traditional handheld detectors. A comparative analysis of three distinct metal detectors for landmine detection is presented: the EM61Lite, a sensitive airborne metal detection system (tested in a pseudo-drone-based scenario); the CTX 3030, a traditional handheld all-metal detector; and the ML 3S, a traditional handheld ferrous-only detector. The comparison focuses on the number of metallic targets each detector identifies in a controlled test field containing inert landmines and UXOs. Our findings highlight the strengths and limitations of airborne metal detection systems like the EM61Lite and emphasize the need for advanced processing techniques to facilitate their practical deployment. We demonstrate how our experimental normalization technique effectively identifies additional anomalies in airborne metal detector data, providing insights for improved detection methodologies. [ABSTRACT FROM AUTHOR]

ABSTRACT The archaeological zone of Mitla has an invaluable place within the Mexican cultural heritage. This pre‐Hispanic city concentrated the political and religious power of the central valleys of Oaxaca, south of Mexico, mainly during the Postclassic period. However, despite its great cultural importance and considering the risk to its monuments due to the high seismicity of the region, very few studies have focused on the systematic exploration of the subsoil of this area. The present research aimed to characterize the subsoil of one of the architectural compounds of the archaeological zone, the Group of the Columns, aiming to identify possible underground elements such as architectural structures, tunnels or tombs. For that purpose, three non‐invasive geophysical methods were applied, ground penetrating radar (GPR), electrical resistivity tomography (ERT) and ambient noise tomography (ANT). In the palace, all three methods showed the presence of a substructure below the Hall of the Columns. In the Plaza of the Columns, the GPR detected anthropogenic walls and platforms at different depths that suggest a different configuration of the square and a more prolonged occupation of this group. The results open the discussion about the temporality of the first construction of this architectural group. [ABSTRACT FROM AUTHOR]

Internal road voids can lead to decreased load-bearing capacity, which may result in sudden road collapse, posing threats to traffic safety. Three-dimensional ground-penetrating radar (3D GPR) detects internal road structures by transmitting high-frequency electromagnetic waves into the ground and receiving reflected waves. However, due to noise interference during detection, accurately identifying void areas based on GPR-collected images remains a significant challenge. Therefore, in order to more accurately detect and identify the void areas inside the road, this study proposes an intelligent recognition method for internal road voids based on 3D GPR. First, extensive data on internal road voids was collected using 3D GPR, and the GPR echo characteristics of void areas were analyzed. To address the issue of poor image quality in GPR images, a GPR image enhancement model integrating multi-frequency information was proposed by combining the Unet model, Multi-Head Cross Attention mechanism, and diffusion model. Finally, the intelligent recognition model and enhanced GPR images were used to achieve intelligent and accurate recognition of internal road voids, followed by engineering validation. The research results demonstrate that the proposed road internal void image enhancement model achieves significant improvements in both visual effects and quantitative evaluation metrics, while providing more effective void features for intelligent recognition models. This study offers technical support for precise decision making in road maintenance and ensuring safe road operations. [ABSTRACT FROM AUTHOR]

Featured Application: Imaging in stratified media and imaging of buried cavities. This contribution deals with the focusing and time–depth conversion of GPR data in a two-layer scenario. In a layered scenario, the different propagation velocities of the electromagnetic waves in different layers can induce distortion in the reconstruction. In particular, it is not possible, with common data processing, to achieve the focusing of both the targets embedded in the upper and deeper layers at the same time. We will present a strategy for mitigating this problem. The strength points of this strategy are its simplicity and the fact that it only requires a commercial code for the GPR data processing plus some steps easily implementable through a homemade code. This work extends a contribution of the same authors at the 20th International GPR conference held in Chanchung, China, in June 2024, and the conclusions are updated with three-dimensional numerical results, including depth slices determined directly in the spatial domain. Both numerical and experimental data will be shown. [ABSTRACT FROM AUTHOR]

This study focused on analyzing the impact of ground-penetrating radar (GPR) scan spacing on accurately assessing the reinforcement of concrete bridge girders, providing practical insights. A decommissioned bridge box beam was evaluated to unveil rebars and tendons' depth and spacing. The box beam was decommissioned from the West Virginia Division of Highways inventory. An innovative algorithm was developed to fully automate the analysis of survey grid data across all sides of the beam. Implementing this algorithm into a computer code has paved the way for comprehensive automation of GPR data analyses. Comparing GPR data analyses from various profile line offsets, this study assists in producing optimal protocols for inspecting box beams. Transverse profile line offsets between 4 in. and 24 in. yielded nearly identical results, setting a new standard for precision. Utilizing more than one longitudinal profile line was highly beneficial in accurately assessing prestressed concrete box beams. This research helps redefine bridge evaluation by precisely finding rebar spacing, concrete cover, and other internal characteristics. This study's findings offer invaluable advancements and equip state departments of transportation with the knowledge to accurately assess in-service concrete bridge box beams, empowering them to make informed decisions. [ABSTRACT FROM AUTHOR]

The integration of remote sensing technologies, such as Mobile Mapping Systems (MMS), Ground Penetrating Radar (GPR), airborne LiDAR, high-speed terrestrial laser scanner and airborne multispectral cameras, is transforming the execution and management of large-scale infrastructure projects. These tools enable efficient and accurate data collection without requiring operators to be physically present in the observed area, enhancing the ability to map, analyze, and model both surface and subsurface features. This paper explores the synergy of these technologies, focusing on their application in critical sectors such as climate change mitigation, smart cities, and digital twins. It examines the benefits of technology integration, the challenges of data interpretation, and the opportunities for improving project efficiency and sustainability. Through a detailed analysis of each remote sensing technology, this paper highlights their potential to redefine large-scale project execution, driving innovation in infrastructure development and to bring business benefits to organisation which implements it. [ABSTRACT FROM AUTHOR]

Cracks in asphalt pavement are typically identified by analyzing electromagnetic or mechanical wave propagation. Validation is done by examining core samples obtained through drilling. This study proposes a threshold based on the structure crack damage rate (SCDR) collected by ground-penetrating radar (GPR) to quantify the severity of structure cracks. The Fuzzy c-means (FCM) algorithm categorises SCDR into different severity levels. Results indicate clear separation among most data points, but some overlap exists, particularly between non-structural and low-structural damage, and low-structural and medium-structural damage. High-structural damage areas are consistently distinct. The most successful outcomes are achieved when dividing structural crack damage levels into three groups. Thresholds for crack damage rates are ultimately determined to be 0.1909% to differentiate between non-structural and low-structural damage, and 0.6632% to differentiate between low-structural and high-structural damage. These findings serve as a foundation for assessing asphalt pavement crack damage and guiding maintenance decisions. [ABSTRACT FROM AUTHOR]

In order to improve the temperature detection ability of municipal asphalt pavement in high-temperature weather, a method to extract the temperature distribution characteristics of municipal asphalt pavement in high-temperature weather under the constraint of compaction quality was proposed. Using a digital image system, laser profiler, and ground penetrating radar system, collect construction temperature data of urban asphalt pavement under the constraints of compaction quality under high-temperature weather conditions, and use time series analysis to reconstruct the data. Combined with the nondestructive detection method of ground penetrating radar, the echo analysis model of temperature information is established, and the characteristics of temperature distribution are reconstructed using the spectral analysis method. The time–frequency transformation method is used to transform the time series of construction temperature under the constraint of compaction quality. The discrete signal window method was used to analyse the temperature characteristics of construction. According to the characteristic state monitoring, the temperature distribution characteristics are extracted. The test results show that the method is reliable in extracting the temperature distribution characteristics of municipal asphalt pavement construction under high-temperature weather and improves the real-time monitoring capability of asphalt pavement temperature. [ABSTRACT FROM AUTHOR]

During test operation of a geological latent heat storage system as a potential option in the context of heat supply for heating and cooling demand, part of a shallow Quaternary glacial aquifer was frozen at the TestUM test site. In order to evaluate the current thermal state in the subsurface, the dimension of the frozen volume has to be known. As the target is too deep for high-resolution imaging from the surface, the use of borehole ground-penetrating radar (GPR) is being investigated. For imaging and monitoring of a vertical freeze–thaw boundary, crosshole zero-offset and reflection borehole GPR measurements are applied. The freezing can be imaged in the zero-offset profiles (ZOPs), but the determination of ice body size is ambiguous because of the lack of velocity information in the frozen sediment. Reflection borehole GPR measurements are able to accurately image the position of the freezing boundary through repeated measurements of ±0.1 m, relying on the velocity information from ZOPs. We have found that the complementary use of ZOPs and reflection measurements provides a fast and simple method to image freezing in geological latent heat storage systems. The presence of superimposed reflections from other observation wells and the low signal-to-noise ratio are problematic. The use in multiple observation wells allows an estimation of ice body size. A velocity model derived from multiple ZOPs enabled us to extrapolate geological information from direct-push-based logging and sediment cores to a refined subsurface model. [ABSTRACT FROM AUTHOR]

Definition: Non-destructive testing (NDT) is essential for evaluating the integrity and safety of structures without causing damage. The integration of artificial intelligence (AI) into traditional NDT methods can revolutionize the field by automating data analysis, enhancing defect detection accuracy, enabling predictive maintenance, and facilitating data-driven decision-making. This paper provides a comprehensive overview of AI-enhanced NDT, detailing AI models and their applications in techniques like ultrasonic testing and ground-penetrating radar. Case studies demonstrate that AI can improve defect detection accuracy and reduce inspection times. Challenges related to data quality, ethical considerations, and regulatory standards were discussed as well. By summarizing established knowledge and highlighting advancements, this paper serves as a valuable reference for engineers and researchers, contributing to the development of safer and more efficient infrastructure management practices. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) has been widely used to assess asphalt and pavement, especially in quality testing for newly constructed roads. However, its usage has been limited in regard to aged roads. Thus, this study focuses on the applicability of GPR to extract diverse information regarding structure, thickness, and various conditions, including the moisture content of an aged road section that has undergone repeated renewals. First, two methods were employed to calculate the thickness and dielectric values; the reflection amplitude and ground truth methods. The analysis was done by RADAN 7 software. Based on the findings, the average error of thickness on the same day between continuous GPR and the core data were 2.87% and 8.72%, respectively. Second, dielectric analysis of three structural units was performed under different moisture conditions. As a result, the average dielectric values of macadam (3, 3.3, and 4), surface asphalt layer (4, 7.06, and 8.31), and cement-treated base (4.83, 10.88, and 11.88) were determined under dry, medium-wet, and wet conditions, respectively. The volumetric water difference (f) within the pavement was also estimated. As for the asphalt, macadam, and cement-treated base, the difference in the volume fraction of water (f) was 0.06, 0.01, and 0.1, respectively, under dry and wet conditions, and 0.04, 0.004, and 0.09, respectively, under dry and medium-wet conditions. Overall, the findings demonstrate that reasonably accurate assessments of the pavement thickness, structure, dielectric values, and amplitude of aged roads can be achieved by using a GPR survey under various conditions. [ABSTRACT FROM AUTHOR]

The article discusses the history of the Contrabands and Freedmen Cemetery in Alexandria, Virginia, which was established in 1864 by the US Army to bury African American Contrabands who sought freedom during the Civil War. Overcrowding, disease, and lack of space led to the creation of this segregated cemetery. The cemetery was forgotten in the 1920s, but efforts in the late 20th century led to its rediscovery and eventual memorialization, highlighting the importance of remembering marginalized histories and decolonization efforts. The cemetery serves as a significant site of remembrance and resistance against erasure, showcasing the ongoing process of reclaiming and honoring the lives of those buried there. [Extracted from the article]

In this study we show the successful deployment of Ground Penetrating Radar (GPR) together with Electrical Resistivity Surveys (ERS) in guiding the archeological excavations at Ashdod-Yam (southern coast of Israel). This approach enabled the precise identification of excavation targets relating to an Early Byzantine ecclesiastical complex located in a residential neighborhood of the modern city of Ashdod. Applied over the course of five years, the combined use of GPR and ERS, interspersed with phases of archeological excavation, not only facilitated an efficient exploration but also ensured the preservation of valuable historical structures. The geophysical data, corroborated by drone images of the post-excavation site, revealed a striking correlation between excavation and non-intrusive survey data. This study not only charts a successful excavation journey but serves as a methodological blueprint for future archeological explorations. The techniques and strategies detailed here have broader implications for the preservation and public presentation of historical sites. [ABSTRACT FROM AUTHOR]

Maintaining the integrity of urban road pavements is vital for public safety, transportation efficiency, and economic stability. However, aging infrastructure and limited budgets make it challenging to detect subsurface defects that can lead to pavement collapses. Traditional inspection methods are often inadequate for identifying such underground anomalies. Ground Penetrating Radar (GPR), especially dual-polarized array systems, offers a non-destructive, high-resolution solution for subsurface inspection. Despite its potential, effectively detecting and analyzing areas at risk of collapse in urban pavements remains a challenge. This study employed a dual-polarized array GPR system to inspect road pavements in London. The research involved comprehensive field testing, including data acquisition, signal processing, calibration, background noise removal, and 3D migration for enhanced imaging. Additionally, Short-Fourier Transform Spectrum (SFTS) analysis was applied to detect moisture-related anomalies. The results show that dual-polarized GPR systems effectively detect subsurface issues like voids, cracks, and moisture-induced weaknesses. The ability to capture data in multiple polarizations improves resolution and depth, enabling the identification of collapse-prone areas, particularly in regions with moisture infiltration. This study demonstrates the practical value of dual-polarized GPR technology in urban pavement inspection, offering a reliable tool for early detection of subsurface defects and contributing to the longevity and safety of road infrastructure. [ABSTRACT FROM AUTHOR]

Handheld Ground Penetrating Radar (GPR) is utilized for detecting rebar, but detecting damage is difficult due to its low reflectance. This study introduces an algorithm to quantitatively estimate damage thickness from GPR-received waveforms. Simple methods to separate peaks from time waveforms at the top and bottom of the crack prove challenging due to destructive interference and side lobes. In previous studies, it has been confirmed that minor variations in damage thickness affect the frequency property. We propose an algorithm to estimate damage thickness using pattern matching with a theoretical amplitude spectrum that accounts for multiple reflections. Initially, the damage thickness is roughly determined by combining low-frequency spectrum centroids with spectrum amplitude. After roughly estimating the damage thickness, subsequent spectral pattern matching is performed within predefined gating and bandwidth ranges. This approach enables quantitative estimation of damage thickness from 2 mm to 180 mm with a millimeter order accuracy, demonstrating its practical application potential. [ABSTRACT FROM AUTHOR]

Summit flats are low-relief, gently sloping landforms common in periglacial mountain environments. Apart from at their edges where summit flats are truncated by glacial headwalls and at their crests where isolated tors are occasionally present, bedrock is typically mantled on a summit flat by a continuous layer of regolith. This study applied ground-penetrating radar (GPR) to survey the thickness of regolith on a summit flat in the Uinta Mountains (Utah, USA). More than 500 m of GPR data were collected along transects extending from the edge of the summit flat to the crest, as well as adjacent to a deep soil pit. Results indicate that the regolith thickness is quite variable, with a mean of 91 ± 38 cm when calculated with an appropriate radar velocity. Because the ground surface of the summit flat is notably smooth, the variability in thickness is a consequence of irregularities in the bedrock surface at depth, which is significantly rougher. Recognition that regolith thickness can vary considerably beneath an alpine summit flat has implications for soil formation, carbon storage, and the transmission and storage of shallow groundwater, as well as evolutionary models for periglacial mountain landscapes. [ABSTRACT FROM AUTHOR]

In recent years, urban road collapse incidents have been occurring with increasing frequency, particularly in populous cities. To mitigate road collapses, geophysical prospecting plays a crucial role in urban road inspections. Ground Penetrating Radar (GPR), a non-destructive technology, is extensively employed for detecting urban road damage, with manual interpretation of GPR images typically used to identify buried objects. Nonetheless, manual interpretation methods are not only inefficient but also subjective, as they rely on the interpreter's experience, thereby affecting the interpreting reliability. This study investigates the distribution and causes of road collapses and develops a deep learning-based intelligent recognition model using GPR detection images of urban roads in cities of the South China as original samples. The finding reveal that road collapses are concentrated in the months of July and August, mainly caused by pipe leakage and rainfall. Common anomalies in urban road GPR detection comprise seven types of target objects, including cavity, pipeline, etc., with standard GPR images acquired through outdoor field experiments. Utilizing GPR forward simulation and image augmentation methods to expand the sample size, as well as generating anchor box dimensions through clustering analysis, have all been proven to effectively improve the model's performance. The urban road GPR image intelligent recognition model, based on the YOLOv4 algorithm, achieves a detection accuracy of up to 85%, proving effective in GPR detection of urban roads in cities of North China. This research offers valuable insights for the future application of deep learning-based image recognition algorithms in urban road GPR detection. [ABSTRACT FROM AUTHOR]

Saturn's moon Enceladus is thought to possess habitable conditions due to the presence of a global ocean, potential water‐rock interactions at the ocean's base, and its ocean composition derived from sampling active plumes at the south pole. The thin (< ${< } $35 km) and cold ice shell of this small moon in the outer solar system is expected to allow the direct detection of the ice‐ocean interface using low‐frequency radar sounder instruments. Here we investigate the two‐way radar attenuation in Enceladus' ice shell, focusing on the effect of a porous icy layer generated by Enceladus' jet activity. Our results show that in regions with a thin (or absent) porous surface layer, total ice shell penetration is possible. In regions covered by thick and strongly insulating porous surface layers, however, as little as 2% of the ice shell can be penetrated. Nevertheless, a thick porous surface layer leads to high subsurface temperatures promoting the formation of brines at shallow depth that can be detected by future radar measurements. Plain Language Summary: Saturn's moon Enceladus is a prime target for planetary exploration and for the search of habitable conditions beyond Earth. Beneath its icy surface, this small moon is thought to harbor a global ocean, presumably sampled by active water jets which have been observed at Enceladus' south pole. Moreover, shallow brines may exist within the ice shell. The detection of subsurface water reservoirs (either the ocean or shallow brines) that can be achieved by radar is fundamental in characterizing Enceladus' subsurface environment and its habitability potential. In this study we calculate the attenuation of radar signals through the ice shell in the presence of snow deposits that are believed to exist on the surface of Enceladus due to its water jets activity. While we do identify some scenarios in which the radar is able to reach the ice‐ocean interface, we note that this interface may not be reached by a radar sounding instrument in regions covered by thick snow deposits that act as a blanket and keep the subsurface warm. However, in these regions, due to the high subsurface temperatures, shallow water bodies are likely to exist and could be detected by future radar observations. Key Points: We calculate the two‐way radar attenuation on Enceladus considering a porous thermally insulating surface layerFor regions covered by a thick insulating porous surface layer the detection of the ice‐ocean interface is unlikelyFor the same regions the high subsurface temperatures increase the likelihood that shallow brines are present and can be detected by radar [ABSTRACT FROM AUTHOR]

Copyright of Urban Geology is the property of Urban Geology 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.)

In order to study the feasibility of upward mining in a certain coal mine, the mechanical parameters of the coal rock mass were obtained based on on-site investigation and rock mechanics experiments. A numerical model that conforms to the on-site mining layout was established using numerical simulation methods. The stress, displacement, and plastic zone distribution characteristics of the upper coal rock mass at different positions along the X, Y and Z directions after the initial mining of layered coal were analyzed. The critical thickness for tensile failure of the roof during upward mining was calculated using the thick plate theory. A ground penetrating radar was used on site to conduct three-dimensional detection of the fragmentation characteristics of the overlying roof rock. The results show that after the mining of 5# coal, the stress of the overlying roof surrounding rock in the goaf exhibits a dynamic change process of rising peak falling. As the vertical height from the working face increases, the stress in the center of the goaf and around the working face gradually decreases; the displacement distribution extends outwards from the center of the working face, forming a sinking basin with an upward opening in the direction of inclination and direction of the working face. The maximum displacement value is at the bottom of the basin; there is a tensile stress zone above the working face, and the plastic zone has the largest range at the center of the working face direction and inclination. The plastic zone has not evolved to coal 4# and coal 3#; the maximum critical thickness for tensile failure of the roof is 55.4 m, which is much smaller than the actual thickness of 99.82 m on site, and upward mining can be adopted. [ABSTRACT FROM AUTHOR]

Copyright of Transportation Science & Technolgy is the property of Transportation Science & 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.)

In this study, a combined workflow of computational methodologies is introduced to explore the transformative landscape of the ancient city of Savatra (Central Anatolia Region, Türkiye), which faces long‐term risks stemming from natural and anthropogenic threats. Emphasis was placed on regional and local scale landscape analysis, employing aerial and ground‐based remote‐sensing techniques to unravel past settlement patterns and understand the impact of environmental factors, topography and natural resources on both the location of Savatra and spatial organization of its features. On a regional scale, the influence of hydrological conditions, slope and aspect on the landscape was determined through the employment of Geographical Information System (GIS)‐based analysis of digital elevation models (DEMs). At a more local scale, the utilization of the Unmanned Aerial Systems‐derived DEM and geophysical survey helped identify potential archaeological features and also assessed the risk posed to these features. Furthermore, the incorporation of 3D GIS analysis, integrating 3D point cloud representations of the ground‐penetrating radar volume and DEM, provided essential insight into the state of preservation of the buried features. The collaborative application and joint interpretation of these methodologies yielded a wide range of clues and explanations, unravelling the complex palimpsest of past activities. This research not only serves as foundation for future studies specifically for Savatra, but also provides a preliminary remote sensing–based exploration blueprint to other yet to be studied archaeological sites. [ABSTRACT FROM AUTHOR]

The nonlinear mechanical properties of ballasted tracks along railways result in complex dynamics of the vehicle–track systems. Employing localized characterization of ballast and a simplified model might underestimate the vehicle–track system's dynamical responses and safety. This paper presents a new dynamical stiffness model of railway ballast by incorporating the ballast's time-varying, nonlinear viscoelastic, and elastoplastic properties. The new nonlinear stiffness model is a versatile tool that comprehensively characterizes the ballast characteristics of displacement-dependent stiffness, frequency-dependent stiffness, hysteresis, and time/space-varying features. These features, widely reported in previous experimental research, can now be effectively understood. Conventionally, to characterize time/space-varying characteristics of ballast along the track, ground-penetrating radar (GPR) has been used as the most efficient approach to survey railway networks quickly and to infer track dynamical properties. Aiming to improve the present technique for characterizing time/space-varying properties of ballast stiffness by using a GPR signal, the adaptive optimal kernel time–frequency representation (AOKTFR) method is used to process a typical GPR signal from a railway ballast scanning. It is found that the results of AOKTFR exhibit a clear time-varying pattern and precise frequency modulation. In contrast, the conventional time–frequency methods failed to give a clear time-varying pattern. The results illustrate that AOKTFR is a practical approach for processing the time-varying nonlinear signal of GPR and correlating it with the time-varying nonlinear stiffness of ballast. [ABSTRACT FROM AUTHOR]

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 a non-destructive sensing technology that utilizes high electromagnetic frequencies. However, mutual interference waves caused by multiple scattering between targets can significantly complicate the interpretation of GPR B-scan images, especially when shallow targets obscure deeper ones. Existing methods primarily focus on extracting target signals from background clutter, frequently overlooking the impact of mutual interference. This paper proposes a convolutional neural network, termed MIS-SE-Net (Mutual Interference Suppression and Signal Enhancement Network), designed to suppress mutual interference waves while preserving shallow target signals and enhancing deeper ones. MIS-SE-Net incorporates attention gates into its encoder–decoder architecture, thereby improving its capabilities in interference suppression and enhancement of weak signals. The network is optimized using a combination of Mean Absolute Error (MAE) loss and perceptual loss. To evaluate MIS-SE-Net, the multi-scale weighted back projection (MWBP) imaging algorithm is used. Simulation results show that after processing with MIS-SE-Net, the integrated side-lobe ratio (ISLR) metric of MWBP imaging decreases by an average of 2.37%, while the signal-to-clutter ratio (SCR) increases by an average of 1.65%. For measured data, results show an average decrease of 7.51% in ISLR and an increase of 2.47% in SCR. These findings validate the effectiveness of the proposed method in suppressing interference, enhancing weak signals, and improving imaging quality. [ABSTRACT FROM AUTHOR]

This study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck. [ABSTRACT FROM AUTHOR]

Characterizing soil moisture around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated using back projection as a radar‐based algorithm and the Born approximation as an inverse scattering method, based on spatio‐temporal (collected data at various positions over the soil surface and at different time points.) measurements at various frequencies. The study investigates the performance of K‐nearest neighbour (KNN) and convolutional neural networks (CNN) algorithms for moisture classification based on these imaging techniques. We also explore the potential of KNN and CNN for moisture estimation around the plant roots and in the presence of pebbles. In general, CNN outperforms KNN in moisture content detection from microwave data, especially after applying imaging algorithms. A combination of CNN as the ML approach and the back projection algorithm to provide training data, yielded 20%$20\%$ accuracy more than other models for moisture content estimation. Also, the practical results demonstrate that our method can detect soil moisture with an estimation error of less than 10%. [ABSTRACT FROM AUTHOR]

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively. [ABSTRACT FROM AUTHOR]

Detecting sub-surface objects poses significant challenges, partly due to attenuation of the ground medium and cluttered environments. The acquisition polarisation and antenna orientation can also yield significant variation of detection performance. These challenges can be mitigated by developing more versatile systems and algorithms to enhance detection and identification. In this study, a novel application of a 3D SAR inverse algorithm and polarisation synthesis was applied to ultra-wideband polarimetric data of buried objects. The principle of polarisation synthesis facilitates an adaptable technique which can be used to match the target's polarisation characteristics, and the application of this revealed hidden structures, enhanced detection, and increased received power when compared to single polarisation results. This study emphasises the significance of polarimetry in ground-penetrating radar (GPR), particularly for target discrimination in high-lift-off applications. The findings offer valuable insights that could drive future research and enhance the performance of these sensing systems. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) is one of the most promising techniques in current ground-penetrating radar (GPR) inversion methods. The least-squares method is usually used, minimizing the mismatch between the observed signal and the simulated signal. However, the cycle-skipping problem has become an urgent focus of this method because of the nonlinearity of the inversion problem. To mitigate the issue of local minima, the optimal transport problem has been introduced into full-waveform inversion in this study. The Wasserstein distance derived from the optimal transport problem is defined as the mismatch function in the FWI objective function, replacing the L 2 norm. In this study, the Wasserstein distance is computed by using entropy regularization and the Sinkhorn algorithm to reduce computational complexity and improve efficiency. Additionally, this study presents the objective function for dual-parameter full-waveform inversion of ground-penetrating radar, with the Wasserstein distance as the mismatch function. By normalizing with the Softplus function, the electromagnetic wave signals are adjusted to meet the non-negativity and mass conservation assumptions of the Wasserstein distance, and the convexity of the method has been proven. A multi-scale frequency-domain Wasserstein distance full-waveform inversion method based on the Softplus normalization approach is proposed, enabling the simultaneous inversion of relative permittivity and conductivity from ground-penetrating radar data. Numerical simulation cases demonstrate that this method has low initial model dependency and low noise sensitivity, allowing for high-precision inversion of relative permittivity and conductivity. The inversion results show that it, in particular, significantly improves the accuracy of conductivity inversion. [ABSTRACT FROM AUTHOR]

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing. [ABSTRACT FROM AUTHOR]