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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]

Ground penetrating radar (GPR) image inversion is of great significance for interpreting GPR data. In practical applications, the complexity and nonuniformity of underground structures bring noise and clutter interference, making GPR inversion problems more challenging. To address these issues, this study proposes a two-stage GPR image inversion network called MHInvNet based on multi-scale dilated convolution (MSDC) and hybrid attention gate (HAG). This method first denoises the B-scan through the first network MHInvNet1, then combines the denoised B-scan from MHInvNet1 with the undenoised B-scan as input to the second network MHInvNet2 for inversion to reconstruct the distribution of the permittivity of underground targets. To further enhance network performance, the MSDC and HAG modules are simultaneously introduced to both networks. Experimental results from simulated and actual measurement data show that MHInvNet can accurately invert the position, shape, size, and permittivity of underground targets. A comparison with existing methods demonstrates the superior inversion performance of MHInvNet. [ABSTRACT FROM AUTHOR]

As an effective geophysical tool, ground penetrating radar (GPR) is widely used for environmental and engineering detections. Numerous numerical simulation algorithms have been developed to improve the computational efficiency of GPR simulations, enabling the modeling of complex structures. The discontinuous Galerkin method is a high efficiency numerical simulation algorithm which can deal with complex geometry. This method uses numerical fluxes to ensure the continuity between elements, allowing Maxwell's equations to be solved within each element without the need to assemble a global matrix or solve large systems of linear equations. As a result, memory consumption can be significantly reduced, and parallel solvers can be applied at the element level, facilitating the construction of high-order schemes to enhance computational accuracy. In this paper, we apply the discontinuous Galerkin (DG) method based on unstructured meshes to 3D GPR simulation. To verify the accuracy of our algorithm, we simulate a full-space vacuum and a cuboid in a homogeneous medium and compare results, respectively, with the analytical solutions and those from the finite-difference method. The results demonstrate that, for the same error level, the proposed DG method has significant advantages over the FDTD method, with less than 20% of the memory consumption and calculation time. Additionally, we evaluate the effectiveness of our method by simulating targets in an undulating subsurface, and further demonstrate its capability for simulating complex models. [ABSTRACT FROM AUTHOR]

Drainage pipelines face significant threats to underground safety due to external defects. Ground Penetrating Radar (GPR) is a primary tool for detecting such defects from within the pipeline. However, existing methods are limited to single or multiple axial scan lines, which cannot provide the precise spatial coordinates of the defects. To address this limitation, this study introduces a novel GPR-based drainage pipeline inspection robot system integrated with multiple sensors. The system incorporates MEMS-IMU, encoder modules, and ultrasonic ranging modules to control the GPR antenna for axial and circumferential scanning. A novel Cross-Line Fusion of GPR (CLF-GPR) method is introduced to integrate axial and circumferential scan data for the precise localization of external pipeline defects. Laboratory simulations were performed to assess the effectiveness of the proposed technology and method, while its practical applicability was further validated through real-world drainage pipeline inspections. The results demonstrate that the proposed approach achieves axial positioning errors of less than 2.0 cm, spatial angular positioning errors below 2°, and depth coordinate errors within 2.3 cm. These findings indicate that the proposed approach is reliable and has the potential to support the transparency and digitalization of urban underground drainage networks. [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]

Roads are critical components of infrastructure, and assessing their quality is essential to ensure the safe transport of people and goods, which in turn supports economic prosperity. Various factors, such as subsurface conditions, moisture content, and temperature, influence road performance and can degrade their efficiency as transportation networks. While surface road defects can often be identified through visual inspection, information about subsurface extensions, their impact on structural integrity, and potential risks remain concealed. This study aimed to perform a comparative analysis of dielectric permittivity (ε) using time-lapse Ground Penetrating Radar (GPR) measurements on pre- and post-renovated road sections. This study also sought to evaluate the effectiveness of this approach for road assessment and to employ forward modeling for a deeper understanding of road defects and their associated hazards. Results revealed that the pre-renovated road section exhibited significant fluctuations in dielectric values, ranging from 3.13 to 15.9. In contrast, the post-renovated section showed consistent values within a narrow range of 5 to 6.6. Different crack types were classified, and the mean ε for each visually identified crack type was calculated. Despite the higher frequency of transverse cracks compared to other defects, longitudinal cracks exhibited the highest mean dielectric value (~10.3), while alligator cracks had the lowest (~8.33). Numerical simulations facilitated accurate interpretation of the defects identified in the road section, providing insights into their nature and associated risks. The methodology used for crack classification and numerical simulation can be applied to other road sections globally, offering a standardized approach to road assessment and maintenance planning. [ABSTRACT FROM AUTHOR]

The current paper is dedicated to the documentation of the characteristics of vertical connectors (gomfoi) in ancient columns and to the observed pathology. The gathering of the relative information aims to provide the data necessary for decisions related to an experimental program, investigating the behaviour of columns provided with connectors between successive drums. The investigation has been carried out in monuments where the use of connectors is already known, as well as in monuments for which there is insufficient information. Monuments for which it is assumed that there are no connectors have also been studied to confirm the hypothesis. Literature review, in situ observation, and the application of non-destructive investigation methods (geo-radar) have led to the documentation of the use and location of connectors in the monuments under study. [ABSTRACT FROM AUTHOR]

Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in UAV-borne geophysical RS. The objective of this study is to comprehensively review the state-of-the-art UAV-based geophysical methods, encompassing magnetometry, gravimetry, gamma-ray spectrometry/radiometry, electromagnetic (EM) surveys, ground penetrating radar (GPR), traditional UAV RS methods (i.e., photogrammetry and LiDARgrammetry), and integrated approaches. Each method is scrutinized concerning essential aspects such as sensors, platforms, challenges, applications, etc. Drawing upon an extensive systematic review of over 435 scholarly works, our analysis reveals the versatility of these systems, which ranges from geophysical development to applications over various geoscientific domains. Among the UAV platforms, rotary-wing multirotors were the most used (64%), followed by fixed-wing UAVs (27%). Unmanned helicopters and airships comprise the remaining 9%. In terms of sensors and methods, imaging-based methods and magnetometry were the most prevalent, which accounted for 35% and 27% of the research, respectively. Other methods had a more balanced representation (6–11%). From an application perspective, the primary use of UAVs in geoscience included soil mapping (19.6%), landslide/subsidence mapping (17.2%), and near-surface object detection (13.5%). The reviewed studies consistently highlight the advantages of UAV RS in geophysical surveys. UAV geophysical RS effectively balances the benefits of ground-based and traditional RS methods regarding cost, resolution, accuracy, and other factors. Integrating multiple sensors on a single platform and fusion of multi-source data enhance efficiency in geoscientific analysis. However, implementing geophysical methods on UAVs poses challenges, prompting ongoing research and development efforts worldwide to find optimal solutions from both hardware and software perspectives. [ABSTRACT FROM AUTHOR]

Effective placement and compaction of the concrete mixture within the spans of prestressed bridges are essential for the proper anchoring and prestressing of tendons. The high density of reinforcement and location of the cable ducts present significant challenges, increasing the risk of void formation and structural irregularities, which can lead to failures during the prestressing process. Ground Penetrating Radar (GPR) emerges as a pivotal non-destructive testing method for diagnosing such complex prestressed structures. Utilizing high-frequency electromagnetic waves, GPR accurately detects and maps anomalies within hardened concrete, enabling precise identification of defect locations and their dimensions. The detailed imaging provided by GPR facilitates the development of targeted repair strategies and allows for the exclusion of concrete voids through selective invasive inspections in designated boreholes. This study presents the use of GPR for the investigation of anomalies and damage in prestressing tendons of a newly built concrete bridge. It underscores the critical role of GPR in enhancing the diagnostic and maintenance programs for prestressed bridge structures, thereby improving their overall integrity and longevity. [ABSTRACT FROM AUTHOR]

Ground-Penetrating Radar (GPR) has demonstrated significant advantages in the non-destructive detection of road structural defects due to its speed, safety, and efficiency. This paper proposes a three-dimensional (3D) reconstruction method for GPR images, integrating the back-projection (BP) imaging algorithm to accurately determine the size, location, and other parameters of road structural defects. Initially, GPR detection images were preprocessed, including direct wave removal and wavelet denoising, followed by the application of the BP algorithm to effectively restore the defect's location and size. Subsequently, a 3D data set was constructed through interpolation, and the effective reflection data were extracted by using a clustering algorithm. This algorithm distinguished the effective reflection data from the background data by determining the distance threshold between the data points. The 3D imaging of the defect was then performed in MATLAB. The proposed method was validated using both gprMax simulations and laboratory test models. The experimental results indicate that the correlation between the reconstructed and actual defects was approximately 0.67, demonstrating the method's efficacy in accurately achieving the 3D reconstruction of road structural defects. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

We present an extensive dataset of ice thickness measurements from Jostedalsbreen ice cap, mainland Europe's largest glacier. The dataset consists of more than 351 000 point values of ice thickness distributed along ∼ 1100 km profile segments that cover most of the ice cap. Ice thickness was measured during field campaigns in 2018, 2021, 2022 and 2023 using various ground-penetrating radar (GPR) systems with frequencies ranging between 2.5 and 500 MHz. A large majority of the ice thickness observations were collected in spring using either snowmobiles (90 %) or a helicopter-based radar system (8 %), while summer measurements were carried out on foot (2 %). To ensure accessibility and ease of use, metadata were attributed following the GlaThiDa dataset (GlaThiDa Consortium, 2020) and follow the FAIR (findable, accessible, interoperable and reusable) guiding principles. Our findings show that glacier ice of more than 400 m thickness is found in the upper regions of large outlet glaciers, with a maximum ice thickness of ∼ 630 m in the accumulation area of Tunsbergdalsbreen. Thin ice of less than 50 m covers narrow regions joining the central part of Jostedalsbreen with its northern and southern parts, making the ice cap vulnerable to break-up with future climate warming. Using the point values of ice thickness as input to an ice thickness model, we computed 10 m grids of ice thickness and bed topography that cover the entire ice cap. From these distributed datasets, we find that Jostedalsbreen (458 km2 in 2019) has a present (∼ 2020) mean ice thickness of 154 ± 22 m and an ice volume of 70.6 ± 10.2 km3. Locations of depressions in the map of bed topography are used to delineate potential future lakes, consequently providing a glimpse of the landscape if the entire Jostedalsbreen melts away. Together, the comprehensive ice thickness point values and ice-cap-wide grids serve as a baseline for future climate change impact studies at Jostedalsbreen. All data are available for download at 10.58059/yhwr-rx55 (Gillespie et al., 2024). [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]

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]

In order to address the impact of various parameters of non-metallic pipelines on the response of ground penetrating radar, the factors that affect the response of ground penetrating radar are identified. A forward model was established by combining the finite time domain difference method and the vector component finite difference method. Using the electromagnetic wave simulation software GPRMax3. 0, a large number of practical situations were simulated, and different antenna frequencies were used to detect the shape, material, burial depth, and other characteristics of different pipelines. The influence of different parameters of non-metallic pipelines on radar response was simulated using this model, where the diameter of the pipeline has a decisive impact on the echo intensity. The simulation results are consistent with the actual situation, proving the effectiveness of this model. [ABSTRACT FROM AUTHOR]

Coal–rock interface recognition is one of the key challenges in advancing unmanned rapid tunneling systems. To address this, a method based on variations in ground-penetrating radar (GPR) reflection wave amplitude is proposed for identifying coal-rock interfaces in soft coal seams. A mathematical model derived from Maxwell's equations calculates the coal seam area based on the reflected wave amplitude. Experiments using a 400 MHz radar antenna show that the method achieves identification errors of 3.58 cm, 3.74 cm, and 4.23 cm for coal-to-rock ratios of 1:2, 1:1, and 2:1, respectively. In practical applications within a coal mine excavation face, the identification error is 6.64 cm. This method provides crucial data for drum height adjustment and cutting arm speed regulation in unmanned tunneling systems. [ABSTRACT FROM AUTHOR]

Shrinking of intermontane basins and expansion of their flanking ranges by reverse faulting and backthrusting in two counter-dipping systems is a typical mechanism of crustal shortening and mountain building in Central Asia. This mechanism is realized along the Kurai Fault Zone (southeastern Gorny Altai). Motions on two reverse fault systems maintained thrusting of the Kurai Range and the Kubadru Uplift on the Kurai Basin sediments and caused the growth of a foreberg before the mountain front. Forberg separates narrow Aktash Basin from the Kurai Basin. The paleoearthquakes were generated by reverse faults that delineate the foreberg. Analysis of the QuickBird satellite images, drone imagery, trenching, archaeoseismological research, radiocarbon dating, dendrochronology, and previous results show that eleven large (МW = 6.5–7.6) paleoearthquakes left traces as surface ruptures along the Kurai Fault Zone: twice before 7.5 ka BP, three events between 7.5 and 5.9 ka BP (7.0, 6.3, and 5.9–5.8 ka BP), one from 5.8 to 4.6 ka BP, four more at 4.6, 3.2, 1.5, and 1.3–1.2 ka BP, and the ultimate earthquake no older than 1450–1650 AD. The time difference between large earthquakes was from 200 to 1700 years. Surface faulting occurred mainly along the northern border of the foreberg where fault scarps are progressively younger northward and the Cenozoic sediments of the Aktash Basin thus become involved into uplift. GPR data to a depth of 12 m confirm the complex structure and slip geometry of the observed surface ruptures. The fault scarps are located < 1 km from the planned route of the gas pipeline from Russia to China, and the potential seismic hazard has to be taken into account in its design and construction. [ABSTRACT FROM AUTHOR]

Radar polarimetry is a technique that can be used to enhance target detection, identification and classification; however, the quality of these measurements can be significantly influenced by the characteristics of the radar antenna. For an accurate and reliable system, the calibration of the antenna is vitally important to mitigate these effects. This study presents a methodology to calibrate Ultra-Wideband (UWB) dual-polarised antennas in the near-field using a thin elongated metallic cylinder as the calibration object. The calibration process involves measuring the scattering matrix of the metallic cylinder as it is rotated, in this case producing 100 distinct scattering matrices from which the calibration parameters are derived, facilitating a robust and stable solution. The calibration procedure was tested and validated using a Vector Network Analyser (VNA) and two quad-ridged antennas, which presented different performance levels. The calibration methodology demonstrated notable improvements, aligning the performance of both functioning and under-performing antennas to equivalent specifications. Mid-band validation measurements indicated minimal co-polar channel imbalance (<0.3 dB), low phase error (<0.8°) and improved cross-polar isolation (≈48 dB). [ABSTRACT FROM AUTHOR]

This study presents a procedure in which 3D electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to determine singularities in the foundations of protected historic towers, where space is limited due to their characteristics and location in highly populated areas. This study was carried out on the Tower of the Cathedral "Santa Iglesia Catedral de Santa María" in Murcia, Spain. The novel distribution of a continuous nonlinear profile along the outer and inner perimeters of the Tower allowed us to obtain a 3D ERT model of the subsoil, even under its load-bearing walls. This nonlinear configuration of the electrodes allowed us to reach adequate investigation depths in buildings with limited interior and exterior space for data collection without disturbing the historic structure. The ERT results were compared with GPR measurements and with information from archaeological excavations conducted in 1999 and 2009. The geometry and distribution of the cavities in the entire foundation slab of the Tower were determined, verifying the proposed procedure. This methodology allows the acquisition of a detailed understanding of the singularities of the foundations of protected historic towers in urban areas with limited space, reducing time and costs and avoiding the use of destructive techniques, with the aim of implementing a more efficient and effective strategy for the protection of other tower foundations. [ABSTRACT FROM AUTHOR]

Hyperbolae in radargrams are caused by a variety of small subsurface objects. The analysis of their curvature enables the determination of propagation velocity in the subsurface, which is important for exact time-to-depth conversion and migration and also yields information on the water content of the soil. Using deep learning methods and fitting (DLF) algorithms, it is possible to automatically detect and analyze large numbers of hyperbola in 3D Ground-Penetrating Radar (GPR) datasets. As a result, a 3D velocity model can be established. Combining the hyperbola locations and the 3D velocity model with reflection depth sections and timeslices leads to improved archaeological interpretation due to (1) correct time-to-depth conversion through migration with the 3D velocity model, (2) creation of depthslices following the topography, (3) evaluation of the spatial distribution of hyperbolae, and (4) derivation of a 3D water content model of the site. In an exemplary study, we applied DLF to a 3D GPR dataset from the multi-phased (2nd to 12th century CE) archaeological site of Goting on the island of Föhr, Northern Germany. Using RetinaNet, we detected 38,490 hyperbolae in an area of 1.76 ha and created a 3D velocity model. The velocities ranged from approximately 0.12 m/ns at the surface to 0.07 m/ns at approx. 3 m depth in the vertical direction; in the lateral direction, the maximum velocity variation was ±0.048 m/ns. The 2D-migrated radargrams and subsequently created depthslices revealed the remains of a longhouse, which was not known beforehand and had not been visible in the unmigrated timeslices. We found hyperbola apex points aligned along linear strong reflections. They can be interpreted as stones contained in ditch fills. The hyperbola points help to differentiate between ditches and processing artifacts that have a similar appearance as the ditches in time-/depthslices. From the derived 3D water content model, we could identify the thickness of the archaeologically relevant layer across the whole site. The layer contains a lot of humus and has a high water retention capability, leading to a higher water content compared to the underlying glacial moraine sand, which is well-drained. [ABSTRACT FROM AUTHOR]

Crosshole ground penetrating radar (GPR) is an efficient method for ensuring the quality of retaining structures without the need for excavation. However, interpreting crosshole GPR data is time-consuming and prone to inaccuracies. To address this challenge, we proposed a novel three-dimensional (3D) reconstruction method based on a generative adversarial network (GAN) to recover 3D permittivity distributions from crosshole GPR images. The established framework, named CGPR2VOX, integrates a fully connected layer, a residual network, and a specialized 3D decoder in the generator to effectively translate crosshole GPR data into 3D permittivity voxels. The discriminator was designed to enhance the generator's performance by ensuring the physical plausibility and accuracy of the reconstructed models. This adversarial training mechanism enables the network to learn non-linear relationships between crosshole GPR data and subsurface permittivity distributions. CGPR2VOX was trained using a dataset generated through finite-difference time-domain (FDTD) simulations, achieving precision, recall and F1-score of 91.43%, 96.97% and 94.12%, respectively. Model experiments validate that the relative errors of the estimated positions of the defects were 1.67%, 1.65%, and 1.30% in the X-, Y-, and Z-direction, respectively. Meanwhile, the method exhibits noteworthy generalization capabilities under complex conditions, including condition variations, heterogeneous materials and electromagnetic noise, highlighting its reliability and effectiveness for practical quality assurance of retaining structures. [ABSTRACT FROM AUTHOR]

The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. During research, it was found that the detection energy of the same target significantly changes with the antenna direction. Based on this phenomenon, this paper proposes a geological radar advanced detection method using spatial scanning. This method overcomes constraints imposed by the underground coal mine environment on detection equipment, enhancing both detection range and accuracy compared to traditional approaches. Experiments using this method revealed pea-shaped response characteristics of planar geological structures in radar images, and the mechanisms behind their formation were analyzed. Additionally, this paper studied the changes in response characteristics under changes in target inclination, providing a basis for understanding the spatial distribution of geological structures. Finally, application experiments in underground coal mine environments explored the practical potential of this method. Results indicate that, compared to drilling data, this method achieves identification accuracies of 91.88%, 90.42%, and 78.72% for the depth and spatial extent of geological structures, providing effective technical support for coal mining operations. [ABSTRACT FROM AUTHOR]

This paper investigates the use of ground-penetrating radar (GPR) technology for detecting shallow buried objects, utilizing an air-coupled stepped frequency continuous wave (SFCW) radar system that operates within a 2 GHz bandwidth starting at 500 MHz. Different GPR data acquisition methods for air-coupled systems are compared, specifically down-looking, side-looking, and circular acquisition strategies, employing the back projection algorithm to provide focusing of the acquired GPR data. Experimental results showed that the GPR can penetrate up to 0.6 m below the surface in a down-looking mode. The developed radar and the back projection focusing algorithm were used to acquire data in the side-looking and circular mode, providing focused images with a resolution of 0.1 m and detecting subsurface objects up to 0.3 m below the surface. The proposed approach transforms B-scans of the GPR-based data into 2D images. The provided approach has significant potential for advancing shallow object detection capabilities by transforming hyperbola-based features into point-like features. [ABSTRACT FROM AUTHOR]

To address low detection accuracy and speed due to the multisolvability of the ground‐penetrating radar signal, we proposed a novel centralized feature pyramid‐YOLOv6l–based model to enhance detection precision and speed in road damage and pipeline detection. The centralized feature pyramid was used to obtain rich intra‐layer features and improve the network performance. Our proposed model achieves higher accuracy compared with the existing detection models. We also built two new evaluating indexes, relative average precision and relative mean average precision, to fully evaluate the detection accuracy. To verify the applicability of our model, we conducted a road field detection experiment on a ground‐penetrating radar dataset we collected and found that the proposed model had good performance in increasing detection precision, achieving the highest mean average precision compared with YOLOv7, YOLOv5 and YOLOx models, with relative mean average precision and frame rate per second at 16.38% and 30.5%, respectively. The detection information for the road damage and pipeline were used to conduct three‐dimensional imaging. Our model is suitable for object detection in ground‐penetrating radar images, thereby providing technical support for road damage and underground pipeline detection. [ABSTRACT FROM AUTHOR]

Electronics and other anthropogenic sources of noise in urban environments interfere with the early time signals of traditional transient electromagnetic (TEM) surveys due to the mutual inductance effect of transmitter and receiver coils. This poses problems for the detection of shallow geohazards such as karst dissolution features that lead to the subsidence and subsequent damage to infrastructure. The opposing-coils transient electromagnetic method (OCTEM) provides an alternative to traditional TEM surveys that is less sensitive to anthropogenic noise, and which is applied in this study to characterize shallow geohazards in a residential area responsible for subsidence and ground collapse. An investigation in Xiacun Town, China, was supplemented by ground-penetrating radar (GPR), drilling, and groundwater level monitoring to verify the OCTEM results and develop a conceptual model relating site hydrogeological factors to the ground collapse. OCTEM accurately identified shallow Quaternary gravel aquifers across the study area. However, OCTEM failed to identify additional subsidence structures near the collapsed pit demonstrated by the GPR results or the presence of a large, soil-filled cave below the pit determined from drilling. The site was concluded to be at further risk of subsidence and ground collapse associated with groundwater erosion driven by extreme precipitation events and excessive groundwater abstraction. [ABSTRACT FROM AUTHOR]

Madīnat Ilbīra was the capital of one of the territorial organization units of the Umayyad state. The city was founded in the second half of the ninth century, and then abandoned in the eleventh century as a result of the collapse of the Caliphate of Córdoba and the transfer of the centre of the region to Granada. Its remains were located only in the nineteenth century. Modern excavations and non-invasive geophysical prospections carried out in this century allow the reconstruction of the spatial system of the city in the era of its greatest prosperity, the location of the citadel and the main mosque, and the reconstruction of the city's buildings. [ABSTRACT FROM AUTHOR]

Water seepage in tunnel lining cracks considerably influences the structural safety of a tunnel. In this study, chitosan was introduced in the enzyme-induced carbonate precipitation (EICP) to repair the cracks in in-service tunnel linings. The influence of chitosan incorporation on the Ca2+ precipitation ratio during EICP was analyzed by aqueous solution experiments, and the optimal content of added chitosan was obtained. Moreover, in terms of specimen scale and field tests, the determination of permeability characteristics, and mass loss, scanning electron microscopy and ground penetrating radar technology were used to analyze the changes in permeability coefficient and mass loss of cracked concrete repaired by chitosan-combined EICP under normal temperature (25°C±2°C) and freeze–thaw (FT) cycling conditions. The effect, feasibility, and action mechanism of chitosan-combined EICP for tunnel crack repair in extreme environments were explored. The results showed that the incorporation of an appropriate amount of chitosan in traditional EICP could accelerate Ca2+ precipitation, provide nucleation sites for the precipitation of CaCO3 , promote the existence of the deposited CaCO3 crystals in the form of calcite with higher strength, and reduce mass loss in extreme circumstances. The combination of hydrogel and CaCO3 makes the impermeable layer more compact and reduces the permeability coefficient of repair concrete. The permeability coefficient descends exponentially with the decrease of mass loss under FT conditions. Chitosan-combined EICP represents an environmentally friendly and feasible method for crack repair in in-service tunnel linings. [ABSTRACT FROM AUTHOR]

For ground-penetrating radar (GPR) detection, multiphase heterogeneous pavement models were built and on-site detections were conducted. The statistic distribution rules of relative permittivity in pavement materials including asphalt mixture and cement-stabilized macadam were determined, and the heterogeneous error correction method of the layer thickness and distress buried depth were studied. The heterogeneous interferences in the A-scan curve before and after interlayer reflection wave filtering were quantified. The relationship between the crack width and homogeneous reflection wave was proposed, and the heterogeneous interference on the identification of the internal crack in the surface layer was discussed. The results show that the relative permittivity values of the surface and base layers obey Gaussian distribution obviously. Heterogeneous error formulas of the layer thickness and distress buried depth were proposed, and the concept of assurance rate was introduced in the error correction. The heterogeneous interference ratio (HIR) index was proposed to represent the heterogeneous interference in A-scan curves, and its Gaussian distribution characteristics were put forward. The heterogeneous interference increases with the wave frequency increase, and the standard deviation of HIR in the surface layer is higher than that in the base layer because of its finer gradation. The average value subtraction method filters the interlayer boundary reflection wave, which has no or minimal influence on the mean value and standard deviation of HIR, respectively. The crack interference ratio (CIR) index was proposed to quantify the heterogeneous interference on the crack reflection wave. Higher wave frequency, wider crack width, and higher water content of the crack medium are conducive to decrease the heterogeneous interference on the crack identification. The reflection waves of the millimeter-scale internal cracks have close magnitude compared with the heterogeneous interference wave, so filtering the heterogeneous interference wave is the premise to identify the millimeter-scale internal cracks in the surface layer. [ABSTRACT FROM AUTHOR]

Full waveform inversion (FWI) of ground-penetrating radar (GPR) data is one of the key methods for reconstructing high-resolution near-surface images and understanding changes in the near-surface environment. However, FWI faces great challenges due to the ill-posed nature of the optimization problem and its high sensitivity to the quality of the initial model. In this paper, we propose the implicit full waveform inversion (IFWI) algorithm for GPR that addresses these limitations by using an implicit and continuous function built by deep neural networks to represent multiple subsurface parameters. In this way, IFWI can be thought of as training a physically constrained neural network representation, allowing it to exploit the frequency principle in deep learning optimization, that is first recovering the low-frequency components of the subsurface parameters and large-scale structures, and then their high-frequency and fine-grained information, which makes IFWI an automated multiscale inversion approach without manual frequency band selection. Numerical experiments are conducted to verify the effectiveness and performance of IFWI relative to FWI. The results demonstrate that IFWI can accurately reconstruct subsurface images and anomalies even with a suboptimal initial model, while FWI and multiscale FWI struggle to provide useful information due to being trapped in local minima. Consequently, IFWI exhibits less dependence on the initial model compared to FWI and multiscale FWI. To better understand the behaviour of IFWI and its sensitivity to neural network architecture, we comprehensively discuss the influences of these hyperparameters on IFWI, providing initial selection guidelines and practical guidance for adjustments. [ABSTRACT FROM AUTHOR]

ABSTRACT Electrical resistivity tomography (ERT) is nowadays widely used in archaeological prospection. This study deals with an approach to make ERT more time‐efficient and flexible. It is based on calculating arbitrary 4‐point configurations by superposition of multiple pole–pole measurements. Investigating its applicability for archaeological purposes is the objective of this work. To do so, a synthetic study and a case study are conducted to gain insights into effects of nonideal field conditions, noise susceptibility and other challenges during processing and interpretation. Remains of an early modern manor in Noer served as an exemplary object of investigation. Their high resistivity contrast in relation to the surrounding soil makes them ideal for a functionality test. Beforehand, ground penetrating radar measurements were carried out to constrain the forward model used in the synthetic study. It turns out that the pole–pole conversion is well applicable for archaeological prospection under some conditions. The synthetic study shows that the approach is relatively prone to systematic errors. Therefore, it is recommended to locate the external electrodes at a distance of at least 0.7 times (preferably 1.7 times) the maximum internal electrode spacing from the area of investigation. Other error sources like nonideal electrode coupling must be excluded to keep relative noise levels below 1%. The pole–pole conversion can be considered reliable for absolute noise levels below 0.3 mV. Therefore, an A/D converter resolution of, for example, 16‐bit should be sufficient for a dynamic range of ±10 V. If all conditions are met, the pole–pole conversion has a great potential to make ERT more time‐efficient (up to 50%, depending on configuration sets) and flexible, as it allows to calculate nearly every arbitrary 4‐point configuration in the given setup. Combined with optimization approaches like the ‘Compare R’ method, data sets can also be adapted for specific (archaeological) questions or conditions. [ABSTRACT FROM AUTHOR]

In this article, the inverse problems for the wave equation in a medium in which multiple types of cavities and inclusion exist in a mixture are considered. From the point of view of the indicator function of the enclosure method, there are two types of heterogeneous parts: “minus group” and “plus group.” For example, cavities with the Dirichlet boundary condition belong to the minus group, while inclusions with smaller propagation velocity belong to the plus group. The heterogeneous part of the minus group gives a negative sign to the indicator function, and the heterogeneous part of the plus group gives a positive sign. In general, the presence of many types of heterogeneous parts causes cancelation of the sign of the indicator function. Such cases are referred to as “mixed cases.” Here, we consider the case that the shortest length obtained from the indicator function is attained only by heterogeneous parts of the same group. This case is called the “mixed but separated case,” and it is shown that the method of elliptic estimates developed by Ikehata works well. We also show that the case of a two‐layered background medium with a flat layer can be considered in the same way as the case of a homogeneous background medium. [ABSTRACT FROM AUTHOR]