Search

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]

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]

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]

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]

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]

Many machine learning (ML)-based detection methods for interpreting ground-penetrating radar (GPR) data of concrete tunnels require extensive labeled damage-state data for model training, limiting their practical use in concealed damage detection of in-service tunnels. This study presents a probabilistic, data-driven method for GPR-based damage detection, which exempts the requirement in the training process of supervised ML models. The approach involves extracting a radar feature vector (RFV), building a Bayesian baseline model with healthy data, and quantifying damage severity with the Bayes factor. The RFV is a complex vector obtained by radargram data fusion. Bayesian regression is applied to build a model for the relationship between real and imaginary parts of the RFV. The Bayes factor is employed for defect identification and severity assessment, by quantifying the difference between the RFV built with new observations and the baseline RFV predicted by the baseline model with new input. The probability of damage is calculated to reflect the influence of uncertainties on the detection result. The effectiveness of the proposed method is validated through simulated data with random noise and physical model tests. This method facilitates GPR-based hidden damage detection of in-service tunnels when lacking labeled damage-state data in the model training process. [ABSTRACT FROM AUTHOR]

We present a technique for the detection of vertebrate skeletons buried at shallow depths through the use of a ground-penetrating radar (GPR). The technique is based on the acquisition of high-resolution data by medium-to-high frequency GPR antennas and the analysis of the radar profiles by a new forward modelling method that is applied on a set of representative traces. This approach allows us to obtain synthetic traces that can be used to build detailed reflectivity diagrams that plot spikes with a distinct amplitude and polarity for each reflector in the ground. The method was tested in a controlled experiment performed at the top of Cerro Los Quesos, one of the most fossiliferous localities in the Ica Desert of Peru. We acquired GPR data at the location of a partially buried fossil skeleton of a large whale and analyzed the reflections associated with the bones using the new technique, determining the possible signature of vertebrae, ribs, the cranium (including the rostrum), and mandibles. Our results show that the technique is effective in the mapping of buried structures, particularly in the detection of tiny features, even below the classical (Ricker and Rayleigh) estimates of the vertical resolution of the antenna in civil engineering and forensic applications. [ABSTRACT FROM AUTHOR]

The Greek marble statue of the Auriga of Mozia Island, in Sicily, is the most important artwork displayed at the Whitaker Foundation Archaeological Museum. It underwent geophysical investigations twice, in 2012 and 2021, to assess the marble's degradation. The 2012 investigation prepared the statue for transfer to the Paul Getty Museum in New York and repositioning on an anti-seismic pedestal. The 2021 investigation evaluated potential new damage before another transfer. Both investigations utilized 3D ultrasonic tomography (UST) to detect degraded marble areas and ground-penetrating radar (GPR) to identify internal discontinuities, such as fractures or lesions, and locate metal pins that were previously inserted to reassemble the statue and its pedestal. Results from the UST indicate an average marble velocity of approximately 4700 m/s, suggesting good mechanical strength, with some areas showing lower velocities (~3000 m/s) within the material's variability range. The GPR profiles demonstrated internal signal homogeneity, excluding internal fracture surfaces or lesions, and confirmed the presence of metallic pins. This study highlights the effectiveness of integrating UST and GPR for non-invasive diagnostics of marble sculptures, providing detailed insights into the marble's condition and identifying hidden defects or damage. [ABSTRACT FROM AUTHOR]

The location of covert graves is an important but challenging part of missing persons investigations. Although traditional search techniques, such as foot searches and cadaver dogs, have proved successful, the incorporation of higher technology methods, such as geophysical techniques, can be used to increase the chances of locating covert graves. This article will present a field method for the use of two geophysical techniques, including ground penetrating radar and electrical resistivity tomography, which can successfully locate covert graves in an Australian environment. If the soil and climate conditions permit, this technical protocol can be applied to other clandestine grave search sites as well. Ultimately, by increasing chances of locating the covert grave, and by extension the missing person, a successful judicial outcome can be achieved and highly sought after answers can be provided to the family. [ABSTRACT FROM AUTHOR]

Locating clandestine graves presents significant challenges to law enforcement agencies, necessitating the testing of grave detection techniques. This experimental study, conducted under Australian field conditions, assesses the effectiveness of time-lapse ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) in detecting pig burials as simulated forensic cases. The research addresses two key questions: (1) observability of graves using GPR and ERT, and (2) changes in geophysical responses with reference to changing climatic conditions. The principal novelty of this research is its Australian focus—this is the first time-lapse GPR and ERT study used to locate clandestine graves in Australia. The results reveal that both GPR and ERT can detect graves; however, ERT demonstrates greater suitability in homogeneous soil and anomalously wet climate conditions, with the detectability affected by grave depth. This project also found that resistivity values are likely influenced by soil moisture and decomposition fluids; however, these parameters were not directly measured in this study. Contrastingly, although GPR successfully achieved 2 m penetration in each survey, the site's undeveloped soil likely resulted in inconsistent detectability. The findings underscore the significance of site-specific factors when employing GPR and/or ERT for grave detection, including soil homogeneity, climate conditions, water percolation, and body decomposition state. These findings offer practical insights into each technique's utility as a search tool for missing persons, aiding law enforcement agencies with homicide cases involving covert graves. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is widely employed as a non-destructive tool for subsurface detection of transport infrastructures. Typically, data collected by high-frequency antennas offer high resolution but limited penetration depth, whereas data from low-frequency antennas provide deeper penetration but lower resolution. To simultaneously achieve high resolution and deep penetration via a composite radargram, a Non-Subsampled Contourlet Transform (NSCT) algorithm-based multifrequency GPR data-fusion method is proposed by integrating NSCT with appropriate fusion rules, respectively, for high-frequency and low-frequency coefficients of decomposed radargrams and by incorporating quantitative assessment metrics. Despite the advantages of NSCT in image processing, its applications to GPR data fusion for concealed damage identification of transport infrastructures are rarely reported. Numerical simulation, tunnel model test, and on-site road test are conducted for performance validation. The comparison between the evaluation metrics before and after fusion demonstrates the effectiveness of the proposed fusion method. Both shallow and deep hollow targets hidden in the simulated concrete structure, real tunnel model, and road are identified through one radargram obtained by fusing different radargrams. The significance of this study is producing a high-quality composite radargram to enable multi-depth concealed damage detection and exempting human interference in the interpretation of multiple radargrams. [ABSTRACT FROM AUTHOR]

Shrub encroachment onto grassland is a global problem, and thus the relationship between shrub encroachment and grazing disturbance needs to be clarified. In this study, we set up two treatments in China's desert steppe, including no grazing (CK) and heavy grazing (HG). We aimed to analyze the impact of grazing on carbon storage in shrublands and herbaceous communities. We utilized Real-Time Kinematic Positioning (RTK) and ground-penetrating radar (GPR) to detect and quantify aboveground / belowground biomass of Caragana microphylla. We also measured the biomass of grass. The resulting model for the aboveground biomass of C. microphylla was y = 22.45e4.5562x, where x denotes the canopy area (R² = 0.96). The resulting model for the root biomass of C. microphylla was y = 3×10-5x - 23.618 (R² = 0.99), where x is amplitude. The coarse root / shoot ratios of the shrub were 8.06 and 8.92 in the CK and HG treatments, respectively. Heavy grazing significantly reduced the density and biomass of both C.microphylla and the broader community (shrub + grass) (P<0.05). The carbon stock of vegetation was significantly greater in shrubby grassland than in Stipa breviflora grassland (P<0.05). Our results show that shrub encroachment increased carbon storage and plant diversity in the desert steppe, while, at the same time, heavy grazing affect the shrub encroachment by C. microphylla. [ABSTRACT FROM AUTHOR]

When it comes to hydropower dam safety, continuous and comprehensive monitoring is increasingly important. Especially for aging dams, this can pose a difficult challenge that benefits from a multimethod analysis. Here, we present the use and suitability of a geophysical method, Ground Penetrating Radar (GPR), for the non-invasive assessment of two distinct types of hydropower dams in Romania: Herculane (a concrete arch dam) and Gura Apelor (an embankment dam with a rockfill and clay core). Unlike traditional monitoring methods for dam safety in Romania, which might provide an incomplete overview, GPR offers a broader, non-destructive approach to evaluating some elements of dam integrity. Here, we present the results of surveys carried out with a 200 MHz antenna on the crests of both dams. The aim was to conduct a rapid assessment of the crest condition and identify the potential damage to the crest that may elude standard monitoring techniques. The surveys provide an imaging indicative of the structural integrity, although this is more challenging in the embankment dam, and additionally we provide significant information regarding the deformations in the upper layers. This complements data from routine topo-geodetical surveys, offering a potential explanation for the vertical displacements observed therein. We highlight several areas of potential deformation as well as degradation in subsurface structures such as rebars. The results underscore the value of GPR in supplementing established dam monitoring methods, highlighting its effectiveness in different contexts and dam types, as well as its potential in shaping future standards for dam safety management in Romania. [ABSTRACT FROM AUTHOR]

To increase the detection accuracy of soil structure and moisture content in reconstituted soils under complex conditions, this study utilizes a 400 MHz ground-penetrating radar (GPR) to examine a study area consisting of loess, sandy loam, red clay, and mixed soil. The research involves analyzing the single-channel waveforms and two-dimensional images of GPR, preprocessing the data, obtaining envelope information via amplitude envelope detection, and performing a Hilbert transformation. This study employs a least squares fitting approach to the instantaneous phase envelope to ascertain the thickness of various soil layers. By utilizing the average envelope amplitude (AEA) method, a correlation between the radar's early signal amplitude envelope and the soil's shallow dielectric constant is established to invert the moisture content of the soil. The analysis integrates soil structure and moisture distribution data to investigate soil structure characteristics and moisture content performance under diverse soil properties and depths. The findings indicate that the envelope detection method effectively identifies stratification boundaries across different soil types; the AEA method is particularly efficacious in inverting the moisture content of reconstituted soils up to 3 m deep, with an average relative error ranging from 2.81% to 7.41%. Notably, moisture content variations in stratified reconstituted soils are more pronounced than those in mixed soil areas, displaying a marked stepwise increase with depth. The moisture content trends in the vertical direction of the same soil profile are generally consistent. This research offers a novel approach to studying reconstituted soils under complex conditions, confirming the viability of the envelope detection and AEA methods for intricate soil investigations and broadening the application spectrum of GPR in soil studies. [ABSTRACT FROM AUTHOR]

We conducted large-depth Ground-Penetrating Radar investigations of the seismogenic Casamicciola fault system at the volcanic island of Ischia, with the aim of constraining the source characteristics of this active and capable fault system. On 21 August 2017, a shallow (hypocentral depth of 1.2 km), moderate (Md = 4.0) earthquake hit the island, causing severe damage and two fatalities. This was the first damaging earthquake recorded on the volcanic island of Ischia from the beginning of the instrumental era. Our survey was performed using the Loza low-frequency (15–25 MHz) GPR system calibrated by TDEM results. The data highlighted variations in the electromagnetic signal due to the presence of contacts, i.e., faults down to a depth larger than 100 m below the surface. These signal variations match with the position of the synthetic and antithetic active fault system bordering the Casamicciola Holocene graben. Our study highlights the importance of employing large-depth Ground-Penetrating Radar geophysical techniques for investigating active fault systems not only in their shallower parts, but also down to a few hundred meters' depth, providing a contribution to the knowledge of seismic hazard studies on the island of Ischia and elsewhere. [ABSTRACT FROM AUTHOR]

The steep and unstable terrain found on debris-covered glaciers, rock glaciers, talus slopes, moraines and other proglacial features often make terrestrial ground-penetrating radar (GPR) surveys unsafe or cost-prohibitive. To address these challenges, this research introduces a novel approach for studying buried ice using multi-low-frequency drone-based GPR. Monostatic antennas of 50, 100, and 200 MHz were flown along a transect spanning a debris-covered glacier and an ice–debris complex at Shár Shaw Tagà (Grizzly Creek) in southwest Yukon, Canada. The drone-based results were compared to manual GPR at two locations along the transect. The two manual segments were conducted using the same radar system in a bi-static mode and included common mid-point (CMP) surveys. Overall, the drone-based radar successfully identified buried ice and enabled estimation of ice body thickness. Notably, CMP results confirmed layer characteristics and enabled depths to be measured across the entire drone-based transect. Discrimination of detail across a range of depths was made possible by comparing the three low frequencies, highlighting the possibility of using this method for future investigations of debris thickness in addition to quantifying buried ice. This study confirms the effectiveness of drone-based GPR combined with manual CMP for surveying ice beneath previously inaccessible terrain. [ABSTRACT FROM AUTHOR]

In the structure of composite pavement, the formation of voids beneath concrete panels poses significant risks to structural integrity and operational safety. Ground-Penetrating Radar (GPR) detection serves as an effective method for identifying voids beneath concrete pavement panels. This paper focuses on analyzing the morphological features of GPR echo signals. Leveraging the GprMax numerical simulation software, a numerical simulation model for void conditions in concrete pavement is established by setting reasonable pavement structure parameters, signal parameters, and model space parameters. The reliability of the numerical simulation model is validated based on field data from full-scale test sites with pre-fabricated voids. Various void conditions, including different void thicknesses, sizes, shapes, and filling mediums, are analyzed. The main conclusions of the study are as follows: the correlation coefficient between measured and simulated signals is above 0.8; a noticeable distinction exists between echo signals from intact and voided structures; signals exhibit similar phase and time delays for different void thicknesses and sizes but significant differences are observed in the A-scan signal intensity, the signal intensity, and the width of the B-scan signal; the impact of void shape on GPR echo signals mainly manifests in the variation of void thickness at different measurement points; and the relationship between the dielectric properties of the void-filling medium and the surrounding environment dictates the phase and time delay characteristics of the echo signal. [ABSTRACT FROM AUTHOR]

A new algorithm is developed to automatically detect rebar locations and diameters of reinforced concrete structures using the ground penetrating radar technique. The study uses two-way travel time and biquadratic equations to formulate electromagnetic wave speed in reinforced concrete structures where hyperbolic signatures are approximated. Leveraging an established algorithm, a computer code has been developed to offer automated analysis of ground-penetrating radar data obtained from survey grids. Four reinforced concrete slabs were designed, fabricated, and tested to validate the developed evaluation approach. The proposed methodology demonstrates outstanding signal processing proficiency and reliably and effectively identifies rebar information. [ABSTRACT FROM AUTHOR]

Nowadays, asteroid and comet exploration is one of the most important components of deep space exploration. Through asteroid and comet exploration missions, it is possible to reveal the history of the formation and evolution of the solar system, to understand the origin and evolution of the planets, and to improve scientific models and instruments. As a payload with the advantages of non-destructive, penetrating, and polarizing characteristics, ground-penetrating radar (GPR) has been widely used in lunar and Mars exploration, and will play an important role in planned asteroid and comet exploration missions. In this study, statistics on asteroid and comet exploration missions, scientific results, and space-based ground-penetrating radar (SB-GPR) utilization are presented for the three phases to date. According to the statistics, SB-GPR will play an important role in future Phase 2 and 3 missions. The focus of this study is on analyzing the mission flow, SB-GPR parameters, scientific objectives, and scientific results of the missions that have carried SB-GPR and those that are planned to carry SB-GPR, including the Hera, Rosetta, Castalia, and Tianwen-2 missions. On this basis, the development trends of asteroid and comet exploration missions, as well as the future development trends of SB-GPR design and signal interpretation, are discussed. [ABSTRACT FROM AUTHOR]

Non-destructive monitoring methods are crucial for the management and maintenance of assets, which include reinforced concrete (RC) structures. Steel reinforcing bar (rebar) corrosion is one of the main causes of deterioration of engineered reinforced structures as it decreases their strength and serviceability. The purpose of this study was to use nondestructive geophysical techniques to detect and monitor rebar corrosion phenomena. To achieve this, several laboratory tests were performed on RC samples partially immersed in water containing a 5% of sodium chloride (NaCl) solution. Moreover, an accelerated corrosion phenomenon was produced by supplying direct current power along the rebar. Ground-penetrating radar measurements were performed on the concrete surface using 2.0 GHz centre frequency antennas, self-potential acquisitions, and electrical resistivity tomography. Even though each technique provided specific information, a data integration approach, simultaneously using different sensors, would further improve the overall quality of the diagnosis. The data set collected was used for an integrated detection approach, effective in observing the evolution of the corrosion process along the reinforcement bar. Through these laboratory results, a multisensor approach and an integrated observation proved useful for observing the evolution of corrosion phenomenon in reinforcement bars, as a consequence of the steel rebar corrosion. [ABSTRACT FROM AUTHOR]

Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial preprocessing step. Currently, this represents a research gap. In the hyperbolic reflection region, manual segmentation not only demands professional expertise but is also time-consuming and error-prone. Automatic segmentation can aid in accurately determining the location and depth of the reflection region, thereby enhancing data interpretation and analysis. This study presents a deep residual Convolutional Neural Network (Res-CNN) that integrates skip connections within an encoder-decoder framework for ROI-binarized hyperbolic segmentation. The proposed framework includes designed downsampling and upsampling modules that facilitate feature computation sharing between these two modules through skip connections within network blocks. In the evaluation of both simple and complex models, our method attained PSNR, SSIM, and FSIM values of 57.1894, 0.9933, and 0.9336, and 58.4759, 0.9958, and 0.9677, respectively. Compared to traditional segmentation methods, the proposed approach demonstrated clearer segmentation results, enabling intelligent and effective identification of the ROI region containing abnormal hyperbolic reflection waves in GPR images. [ABSTRACT FROM AUTHOR]

This article describes a series of steps to integrate multiple modes of archaeological mapping in arid and agricultural settings. We use the coastal region of Peru as a case study and share our recent field experience at Cerro San Isidro, a multicomponent hill site located in the agriculture-intensive and mid-elevation (about 500 m asl) Moro region of the Nepeña Valley. In June and July 2022, we spent eight weeks deploying a combination of drone aerial imagery, pedestrian GPS reconnaissance, and GPR survey to map the surface and subsurface features at the site and in the adjacent agricultural fields. Our efforts suggest that the ancient settlement extended over an area of at least 50 ha, well beyond the visible surface architecture. Using a multimodal approach to confirming the partial destruction of archaeological vestiges by modern agricultural encroachment is both time-effective and noninvasive. The article offers insights from our experience, including the sequence of field operations, technical troubleshooting, and the collection and integration of datasets. We discuss the methodological potential and implications of this combination of multimodal mapping and its deployment in coastal Peru, a region that, like many others in the world, is increasingly subject to rapid agricultural expansion and other anthropogenic developments. [ABSTRACT FROM AUTHOR]

Fusion-based imaging using ground-penetrating radar (GPR) and ultrasonic echo array (UEA) was employed to track damage progression in the columns of two full-scale reinforced concrete (RC) bridge column-footing subassembly laboratory specimens. The specimens had different lap-splice detailing and were subjected to reverse-cyclic lateral loading simulating a subduction zone earthquake. GPR and UEA scans were performed on the east and west faces of the columns at select ductility levels. Reconstructed images were obtained using the extended total focusing method (XTFM) and fused using a wavelet-based technique. Composite images of each column's interior were created by merging the images from both sides. A quantitative analysis based on the structural similarity (SSIM) index accurately captured damage progression. A backwall analysis using the amplitude of the backwall reflector was also performed. Changes as early as in the first measurement (μ = 0.5 displacement ductility level) could be detected. Damage variation along the column height was observed, consistent with greater damage at the base. The proposed analyses distinguished the structural behavior differences between the two specimens. In summary, the SSIM metric provides a valuable tool for detecting changes, while the backwall analysis offers simple yet informative insights into damage progression and distribution in full-scale RC members. [ABSTRACT FROM AUTHOR]

Timely and accurate detection and identification of cavities under urban roads is the key to road safety. Due to the inability to obtain accurate migration velocity and the difficulty of achieving complete convergence of diffraction signals of the cavity disease, traditional migration methods struggle to accurately identify and locate the subgrade cavity. This paper proposes a GPR image migration processing (TUFK method) based on 2D undecimated wavelet transform and the F–K method in accordance with the high-precision imaging of the subgrade cavity. The finite-difference forward models of subgrade cavity without and with noise are established, and the model test of cavity detection by GPR is carried out in the laboratory. Through the fine extraction and migration processing of the weak diffraction signals from the cavity, the optimal velocity required for migration is analyzed, and the TUFK method is applied to the migration process of GPR data acquired for the purpose of cavity detection. Furthermore, the proposed method is applied to the processing of GPR data acquired in the field with a cavity below the roadbed. The results show that the TUFK method can accurately extract the diffraction signals from the cavity and achieve the fine convergence of cavity diffraction signals whether in noiseless or noisy environments. Compared with the traditional Kirchhoff and F–K migration methods, this method can effectively obtain accurate migration velocity and the migration results can reflect the actual position and shape of the cavity. This study can provide a new idea and effective method for the imaging of subgrade cavity. [ABSTRACT FROM AUTHOR]

The traditional visual tree assessment method is subjective in evaluating tree risks and therefore not effective in precisely detecting internal decay in tree trunk and root systems. To improve the accuracy of street tree risk assessment, a new nondestructive testing method was proposed. This new tree risk assessment method combines different non-destructive testing technologies, such as sonic tomography and ground-penetrating radar, which could significantly increase the accuracy of risk assessment in tree trunks and roots. The method was applied to evaluate the risk of 1,001 street trees in Shanghai's historical feature protection area. The results revealed that despite most street trees having low branch and trunk risk levels, more than one-third had high root risk levels. The risk factors of street trees were mainly in the trunk and root system, with a significant correlation between the street tree risk level and tree cavities, diseases, and insect pests, as well as the depth and range of the root distribution, leaning, and internal decay in trunks. With the help of non-destructive testing and risk assessment analysis, as well as targeted prevention measures, the possibility of street risk damage was largely reduced, including street trees tilting and collapsing during typhoons, etc. [ABSTRACT FROM AUTHOR]

The Florida Everglades are a vast subtropical wetland that historically spanned over 1,000,000 hectares, but much of the Everglades has changed in the last 100 years due to anthropogenic activity. Collier County, situated in southwest FL and bordering the Gulf of Mexico, was subject to alteration in the form of logging, road building, and canal digging. These actions disrupted the natural sheet flow of water and had large environmental impacts on the region, impacts which are slowly being addressed by Everglades restoration efforts. The aim of this work was to observe environmental change at a cypress swamp forest in Collier County within the Big Cypress National Preserve. Using sediment core data including charcoal analysis, loss on ignition, and peat humification, as well as remote sensing techniques, this project uses a novel approach to assess local environmental conditions in the modern era for the region. While this analysis was based on a single core located at the study site, additional ground-penetrating radar profiles at the study site showed a consistent, laterally continuous distribution of subsurface interfaces, therefore suggesting that the core is representative of the conditions at this specific location. Historical records and contemporary data are used to evaluate environmental change over time, and satellite imagery is used to quantify vegetation health. Environmental change related to anthropogenic activity is noted, and evidence of progress from restoration efforts is observed from the last two decades in our study's data. [ABSTRACT FROM AUTHOR]

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SARs) and Ground-Penetrating Radars (GPRs) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency-Modulated Continuous Wave (FMCW) operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device. In one scenario, it was able to detect and locate a 15 cm-diameter fire-extinguisher located at a distance of 3.5 m from the scanning system and 1.6 m behind a 3 cm-thick MDF (medium-density fiberboard) wall with cm-level accuracy. In a second test, the proposed system was used to perform interferometric displacement measurements, and it was capable of determining the displacement of a metal case with sub-millimeter accuracy. In a third experiment, the system was used to construct a 3D image of the inside of a wood table with cm-level resolution. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method. [ABSTRACT FROM AUTHOR]

This paper demonstrates the utility of ground‐penetrating radar (GPR) to inform forensic archaeology recovery efforts of missing service members from historic conflict‐related aircraft crash sites. This approach is becoming more common and improving recovery strategies by pinpointing potential subsurface anomalies prior to excavation. Two examples of recovery efforts at WWII aircraft crash sites are presented, revealing the diversity of landscape upheaval signatures that result from aircraft impacts. In both situations, the GPR successfully located feature boundaries and identified aviation impacturbation. The landscape signature varied in both cases due to factors including the trajectory and velocity of the aircraft crash and the topography of the impacted landscape. Notably, a 'halo' effect was identified in association with one crash site, revealing the force of the impact on sandy soils. Recognition of these anthropogenic signals is important to promote effective recovery strategies, thus saving time, labour and funds, particularly in historic sites where postincident taphonomic conditions have severely altered the morphology of the landscape. [ABSTRACT FROM AUTHOR]

Electromagnetic wave velocity in ground‐penetrating radar (GPR) constant offset data can be estimated via the diffraction hyperbola fitting method. This method is applicable when radargrams contain diffraction events (hyperbolic patterns) caused by scatters in the host smaller or equal to the dominant wavelength. An alternative method for velocity estimation, if no intrusive information is available for a direct correlation, requires the collection of multi‐offset data. The method is quite common for broad geophysical applications; however, it seems not to be fully utilized for engineering applications, such as slabs/walls where thickness estimation and depth of the embedded features are critical requirements for structural assessments. This method would also overcome the limitations in velocity calibration in environments with no hyperbolic signal signatures. The aim of this study is to explore multi‐offset high‐frequency GPR applications, specifically the wide‐angle reflection and refraction method, for structural engineering, to understand whether it is feasible, possible limitations, and advantages. Numerical models reproducing reinforced concrete elements and a cavity wall were analysed to understand the wave behaviour and predict the response prior to testing on real cases. The main purpose is to explore how reinforcing bars can affect the velocity spectra derived from semblance analysis and what the response would be in a case of multi‐layered structure with increasing velocity with depth (cavity wall). The comparison with real cases showed that, despite some intrinsic limitations, high‐frequency multi‐offset approach could be part of standard workflow for all those surveys where no other velocity estimation method is feasible. [ABSTRACT FROM AUTHOR]

Scattering is often detected when ground‐penetrating radar (GPR) surveys are performed on glaciers at different latitudes and in various environments. This event is often seen as an undesirable feature on data, but it can be exploited to quantify the debris content in mountain glaciers through a dedicated scattering inversion approach. At first, we considered the possible variables affecting the scattering mechanisms, namely the dielectric properties of the scatterers, their size, shape and quantity, as well as the wavelength of the electromagnetic (EM) incident field to define the initial conditions for the inversion. Each parameter was independently evaluated with forward modelling tests to quantify its effect in the scattering mechanism. After extensive tests, we found that the dimension and the amount of scatterers are the crucial parameters. We further performed modelling randomizing the scatterer distribution and dimension, critically evaluating the stability of the approach and the complexity of the models. After the tests on synthetic data, the inversion procedure was applied to field datasets, acquired on the Eastern Gran Zebrù glacier (Central Italian Alps). The results show that even a low percentage of debris can produce high scattering. The proposed methodology is quite robust and able to provide quantitative estimates of the debris content within mountain glaciers in different conditions. [ABSTRACT FROM AUTHOR]

Numerical modelling of a ground‐penetrating radar (GPR) has been widely used for predicting and assessing its performance. As the transmitter and the receiver are the most essential components of a GPR system, an accurate representation of them should be included in a model. Simulating a real system is particularly challenging, especially when it comes to commercial GPR systems. A three‐dimensional model based on a 2000 MHz 'palm' antenna from Geophysical Survey Systems, Inc. (GSSI) is presented in this paper. The geometric features of the transducers were modelled via visual inspection, whereas their unknown dielectric properties were estimated using global optimizers in order to minimize the differences between real and synthetic measurements. In particular, the antenna was calibrated in free space and on top of a metal plate. Subsequently, the resulting model was successfully tested in various case studies to assess its performance. Models of two units of the same transducer were developed, showing that units of the same system in general are not identical. The results support the premise that global optimizers can be used to provide information on key aspects of the dielectric structure of the transducer and allow us to accurately model its behaviour in various environments. [ABSTRACT FROM AUTHOR]

As the transmitter and receiver (Tx and Rx, respectively) are located in close proximity during a typical ground‐penetrating radar (GPR) survey, the powerful signal generated by the Tx and which is then recorded by the Rx at various time delays, can be saturated at early times (i.e., this is the direct wave (DW) signal reaching the Rx). This often causes the masking of shallow targets, complicating data interpretation. In this study, our aim is to examine the spatial distribution of the electromagnetic signals around the Tx, attempting to locate areas where the DW becomes minimum, whereas the signal strength from subsurface targets (i.e., reflected wave – RW) remains ideally unchanged. The position of these local minima in the DW signal could give rise to advantageous Tx–Rx configurations, where clear reflections from subsurface targets lying at shallow depths can be obtained with the least possible involvement of the DW. To perform such a study, we carried out static field measurements over a flat lying reflector as well as numerical simulations in a reflection, common‐offset mode around a transmitting antenna. In the field, we also collected wide‐angle reflection–refraction data to determine the GPR wave velocity in the uppermost layer. GPR signals were recorded by the Rx around the Tx in three concentric circles of various radii (i.e., varying the Tx/Rx separation), using a specific angular step and varying the Tx/Rx polarization each time. The synthetic data were produced using a three‐dimensional finite‐difference time‐domain modelling tool. Field and numerically simulated data were analysed and compared to study the behaviour of both the DW and RW events around the Tx when changing the Tx/Rx distance, their respective angular position, as well as their relative polarization/orientation. [ABSTRACT FROM AUTHOR]

Ground‐penetrating radar is an effective tool for detecting landmines and improvised explosive devices, but its performance is strongly influenced by subsurface properties as well as the characteristics of the target. To complement or replace labour‐intensive experiments on test sites, cost‐efficient electromagnetic wave propagation simulations using the finite‐difference time‐domain method are being increasingly used. However, to obtain realistic synthetic data, accurate modelling of signal alteration caused by dispersion, scattering from soil material, target contrast, shape, and inner setup, as well as the coupling effects of the antenna to the ground is required. In this study, we present a detailed three‐dimensional model of a shielded ground‐penetrating radar antenna applied to various scenarios containing metallic and non‐metallic targets buried in different soils. The frequency‐dependent intrinsic material properties of soil samples were measured with the coaxial transmission‐line technique, while a discrete random media was used to implement the heterogeneity of a gravel based on its grain‐size distribution. Our simulations show very good agreement with experimental validation data collected under controlled conditions. We accurately reproduce the amplitude, frequency, and phase of target signals, the subsurface background noise, antenna crosstalk and associated interference with target signals, and the effect of antenna elevation. The approach allows for a systematic investigation of the effects of soil, target, and sensor properties on detection performance, providing insight into novel and complex ground‐penetrating radar scenarios and the potential for a wide range of simulation possibilities for demining with ground‐penetrating radar. These investigations have the potential to improve the safety and effectiveness of landmine and improvised explosive device detection in the future, such as building a database for training deminers or developing automatic signal pattern recognition algorithms. [ABSTRACT FROM AUTHOR]

Timely and accurate detection of water pipe leakage is critical to preventing the loss of freshwater and predicting potential hazards induced by the change in underground water conditions, thereby developing mitigation strategies to improve the resilience of pipeline infrastructure. Ground‐penetrating radar (GPR) has been widely applied to investigating ground conditions and detecting pipe leakage. However, due to uncertainties of complex underground environments and time‐lapse change, a proper interpretation of GPR data has been a challenging task. This paper aims to leverage hydromechanical (HM) modelling to predict electromagnetic (EM) responses of water leakage detection in diverse leakage cases. A high‐fidelity 3D digital model of an actual pipeline network, hosting pipes with various sizes and materials, was reconstructed to represent the complex geometry and various mediums. The interoperability between the digital model and the numerical models utilized in HM and EM simulations was improved to better capture the irregular pipelines. Based on Kriging interpolation and the volumetric complex refractive index model, a linking technique was employed to replicate material heterogeneity caused by water intrusion. Thus, a framework was developed to accommodate the interoperability among digital modelling, HM modelling and finite‐difference time‐domain forward modelling. Moreover, sensitivity studies were conducted to evaluate the influences of different time stages, leak positions and pipe types on GPR responses. In GPR B‐scans, the presence of hyperbolic motion and horizontal reflections serve as indicators to estimate the location and scale of water leakage. Specifically, a downward‐shifting hyperbola indicates that the pipeline is submerged by leaked water, whereas the emergence of horizontal reflection is linked to the wetting front of saturated areas. The developed framework can be expanded for complicated applications, such as unknown locations and unforeseen failure modes of pipelines. It will increase the efficiency and accuracy of traditional interpretations of GPR‐based water leakage detection and thus enable automated interpretations by data‐driven methods. [ABSTRACT FROM AUTHOR]

Cost‐effective computing capabilities have paved the road for the use of numerical modelling to develop advanced methods and applications of ground‐penetrating radar (GPR). Realistic synthetic data and the corresponding modelling techniques, respectively, should consider all subsurface and above‐ground aspects that influence GPR wave propagation and the characteristics of recorded signals. Critical aspects that can be realized in modern GPR modelling tools include heterogeneous and frequency‐dependent material properties, complex structures and interface geometries as well as three‐dimensional antenna models, including the interaction between the antenna and the subsurface. However, realistic noise related to the electronic components of a GPR system or ambient electromagnetic noise is often not considered, or simplified by assuming a white Gaussian noise model which is added to the modelled data. We present an approach to include realistic noise scenarios as typically observed in GPR field data into the flow of modelling synthetic GPR data. In our approach, we extract the noise from recorded GPR traces and add it to the modelled GPR data via a convolution‐based process. We illustrate our methodology using a modelling exercise, where we contaminate a synthetic two‐dimensional GPR dataset with frequency‐dependent noise recorded in an urban environment. Comparing our noise‐contaminated synthetic data with field data recorded in a similar environment illustrates that our method allows the generation of synthetic GPR with realistic noise characteristics and further highlights the limitations of assuming pure white Gaussian noise models. [ABSTRACT FROM AUTHOR]

Eruptions of the Askja Volcano in Northern Iceland in 1875 and 1961 blanketed the caldera with rhyolitic and basaltic tephra deposits, respectively, which preserved layers of seasonal snowpack as massive ice. Askja serves as an operational and geophysical analog to test ground‐penetrating radar field and analysis techniques for in situ resource utilization objectives relevant to the martian and lunar environments. We conducted ground‐penetrating radar surveys at center frequencies of 200, 400, and 900 MHz to map the thickness and extent of tephra deposits and underlying massive ice at three caldera sites. We identified up to 1 m of tephra preserving up to 4.4 m of massive ice. We measured the real dielectric permittivity of the overlying tephra and the total attenuation at each frequency of the tephra and ice. A key objective of our investigation was to determine if attenuation (or loss) could be used as an additional diagnostic signature of massive ice preserved at depth when compared to ice‐free stratigraphy. Loss rates of the ice‐rich subsurface decrease with increasing ice thickness relative to the overburden, which may constitute a possible signature. Attenuation also increased with increasing frequency. The tephra, ice, and other volcanic deposits at each of our three caldera sites and the ice‐free, pumice‐mantled 1961 Vikrahraun lava flow exhibited consistently low loss rates at all frequencies. This result highlights the ambiguity associated with identifying the unique signature of ice within low‐loss stratigraphies, a possible challenge for its identification in the martian or lunar subsurface using radar. Plain Language Summary: The Askja Volcano in Northern Iceland is considered to be a planetary analog for other terrestrial worlds such as Mars and the Moon. We conducted ground‐penetrating radar surveys of the Askja caldera where seasonal snowpack was buried by eruptions of low‐density ash and tephra in 1875 and 1961. This erupted volcanic material protected the snowpack long‐term, where it later densified into thick layers of ice. We successfully mapped up to 4.4 m of this ice preserved beneath up to 1 m of erupted material. Transmitted radar signals decay naturally as their distance from the source increases. Some materials such as water ice are less conducting than others and are therefore less lossy to this transmitted signal. We used the ice and tephra deposits at Askja as a test case to determine if large quantities of buried ice would result in a detectable signature that indicates its presence when compared to ice‐free regions. We found that increases in ice layer thickness relative to the overlying volcanic material result in columns of material with bulk properties that are less dissipative to the radar signal and therefore may indicate a signature of buried ice in the subsurface. Key Points: Multi‐frequency Ground‐penetrating radar (GPR) surveys identified massive ice up to 4.4 m thick buried by up to 1 m of tephra from two Holocene eruptions of AskjaGPR readily maps vertical and horizontal extents of subsurface ice and tephra overburden; ice concentration transitions are not detectedIce‐rich and ice‐free sites present a similar attenuation; ice‐rich sites demonstrate a lower attenuation rate with increasing ice thickness [ABSTRACT FROM AUTHOR]

Interest in locating and preserving the cemeteries of enslaved African Americans has increased the need for new methodologies coupled with efficient, noninvasive geophysical techniques, such as ground-penetrating radar (GPR). The southeast United States, where many of such sites are located, provides many challenges to using GPR due to its humid climate and abundant vegetation, which deteriorates burial remains, disguises burial shafts, and forms physical obstacles to GPR surveys. These challenges make it difficult for GPR alone to locate caskets or burial shafts. To address these issues, this study uses GPR combined with an innovative spatial and observational methodology to locate burials in a humid climate and determine their burial conditions. These mixed methods led to the location of 129 potential graves. Knowing the spatial distribution of burial features provides a context for analyzing the relative burial ages and decay rates within the cemetery site. [ABSTRACT FROM AUTHOR]

Wetland environments pose unique challenges to archaeological research due to their waterlogged conditions, which can preserve organic materials exceptionally well but also hinder traditional excavation techniques. In recent years, interdisciplinary approaches combining geophysics and geochemistry have emerged as powerful tools for investigating archaeological sites in wetland contexts. This article explores the integration of geophysical survey methods such as ground-penetrating radar (GPR), magnetometry, and electrical resistivity tomography (ERT) with geochemical analyses including sediment coring and isotopic analysis. By synergistically applying these techniques, researchers can obtain a comprehensive understanding of wetland archaeology, uncovering hidden features, mapping subsurface structures, and elucidating past human activities and environmental conditions. This article discusses the theoretical foundations, methodological considerations, and case studies highlighting the effectiveness of geophysics and geochemistry in wetland archaeology. Furthermore, it addresses the challenges and future directions of this interdisciplinary strategy, emphasizing its potential to reshape our understanding of human history and environmental dynamics in wetland landscapes. [ABSTRACT FROM AUTHOR]

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

Determination of the physical properties of subsurface geological bodies is essential for georesource management and geotechnical applications. In the absence of direct measurements, this usually passes via geophysical methods such as seismic and ground‐penetrating radar. These require conversion to physical properties, and measurements at different scales to test for consistency. This approach is non‐trivial in geobodies with heterogeneous patterns of properties. Tufa mounds—in‐situ terrestrial carbonate buildups precipitating from geothermal waters—are characterised by high contrasts in facies and petrophysical properties from microscale to macroscale, and are therefore ideally suited to test the ability of non‐invasive geophysical methods to estimate such contrasts, and to develop petrophysical models based on geophysical properties. Here, a laboratory‐based study of a Pleistocene tufa mound in Spain is presented that combines (1) petrography, (2) digital 2D pore network analysis, (3) gas porosity and permeability measurements, (4) acoustic velocity measurements and (5) electromagnetic wave velocity and porosity determination from ground‐penetrating radar, to develop empirical petrophysical models. These results show the consistency of petrophysical properties determined with different methods across various observational scales. Electromagnetically derived porosity positively correlates with gas porosity. Petrophysical properties depend on measurable rock fabric parameters and the degree of cementation, which provide predictive tools for subsurface geobodies. Strongly cemented peloidal‐thrombolitic fabrics with intergranular and intercrystalline pores, and a dominance of small complex pores best transmit acoustic waves. Weak cementation and a significant fraction of large simple pores (framework, vegetation moulds) increase porosity and permeability of shrubby fabrics, while causing lower acoustic velocity. This study demonstrates that ground‐penetrating radar models can be used in combination with direct measurements of physical subsurface properties to capture highly contrasting physical properties associated with different sedimentary facies that would not be achievable with other methods, thus improving the understanding of formational processes. [ABSTRACT FROM AUTHOR]

Fracture zones in front of tunnel faces can easily cause falling blocks and landslides during the construction process. Using seismic waves and ground-penetrating radar (GPR) data, we extracted the features of fracture zones and achieved the advanced prediction of tunnel fracture zones. The energy variation in the reflected waves propagated by seismic waves at interfaces with different impedances of contact waves was found to manifest as positive and negative reflections, and the amplitude of reflected signals within the fracture zone areas thus increased. We designed a superimposed velocity spectrum, divided the areas of variation in wave velocity, and constructed the three-dimensional spatial distribution of the tunnel fracture zones. Based on the phase change, increase in amplitude, and increase in the center-frequency characteristics of the one-dimensional time waveform of the electromagnetic waves in the fault zone area (A-scan), we located the characteristic points of the fracture zones and observed the occurrence of in-phase axis misalignment in two-dimensional scanning (B-scan). We then implemented the identification of fracture zones. This method predicted the fractured area in the rock surrounding the Liangwangshan Tunnel, and during the tunnel excavation, the fracture zones appeared in the recognition area. [ABSTRACT FROM AUTHOR]

Fan‐deltas are formed in mountain lakes, contributing to changes in shorelines' shapes and filling lake basins with sediments. Factors that condition sediment delivery to the lakes and the formation of fan‐deltas are not fully understood. This study aims to identify processes forming fan‐deltas in mountain lakes based on the sedimentary architecture of a fan‐delta filling Zelené Kežmarské Lake (Slovakia). Our study is based on ground‐penetrating radar and seismic refraction surveys conducted over the lake and debris‐flow fans in its vicinity, and grain‐size analyses of the surface deposits. The internal structure of the fan‐delta comprises foreset deposits representing the fan‐delta lobes. Mouth bars were identified in the near‐shore zone. The fan‐delta is built of sands and silty sands whereas an alluvial debris‐flow dominated fan west of the lake contains gravels. A general trend of downslope fining of the fan surface sediments, disturbed by zones of coarse gravels, was identified in the surficial sediments forming the fan. The fan‐delta was formed by depositional events in humid periods, alternating with dry periods featured with a small or no deposition. The lake‐level steadily rose in the humid periods and remained stable in dry periods. The contrast between the coarse sediments forming the alluvial debris‐flow dominated fan, and sands forming the fan‐delta was caused by a selective deposition. Coarse gravels in the fan were mobilized in humid periods but did not reach the lake‐shore. The reason was a basement rock ridge situated parallel to the lake shore. Sands were delivered to the fan‐delta in humid periods owing to frequent high‐energy flows. A deposition of silts took place in dry periods. The studied fan‐delta might preserve the sedimentary record of three humid periods from the last 200 years. However, further studies are needed to establish the timing of the fan‐delta formation. [ABSTRACT FROM AUTHOR]

Ground‐penetrating radar (GPR) technology is widely used in tunnel engineering detection; however, various factors, such as environmental interference and low signal‐to‐noise ratio characteristics of the echo data, limit the detection accuracy. A noise and interference suppression algorithm based on improved singular value decomposition is proposed in this paper. Compared with traditional filtering methods, the proposed method has the advantages of thorough denoising, no clutter, efficient improvement of profile resolution and less dependence on parameters. The main features of the proposed algorithm are as follows. (1) Given the global characteristics of the noise disturbance on the signal space, the minimum mean square error estimation is employed to approximate the effective signal, introducing the correction factor to suppress the larger singular value from the noise output in the reconstructing process of the effective signal subspace and to eliminate the strong direct wave interference to avoid producing false signals. (2) A positive difference sequence search algorithm based on rank order variance as well as the method of selecting correction factors are proposed to improve the processing accuracy. In order to verify the design, the tunnel lining simulation model and the actual tunnel lining detection data are used. The results show good performance for noise and interference suppression, providing technical support for improving GPR data quality and tunnel detection accuracy. [ABSTRACT FROM AUTHOR]

Identification of slope subsurface strata for natural soil slopes is essential to assess the stability of potential landslides. The highly variable strata in a slope are hard to characterize by traditional boreholes at limited locations. Ground-penetrating radar (GPR) is a non-destructive method that is capable of capturing continuous subsurface information. However, the accuracy of subsurface identification using GPRs is still an open issue. This work systematically investigates the capability of the GPR technique to identify different strata via both laboratory experiments and on-site examination. Six large-scale models were constructed with various stratigraphic interfaces (i.e., sand–rock, clay–rock, clay–sand, interbedded clay, water table, and V–shaped sand–rock). The continuous interfaces of the strata in these models were obtained using a GPR, and the depths at different points of the interfaces were interpreted. The interpreted depths along the interface were compared with the measured values to quantify the interpretation accuracy. Results show that the depths of interfaces should be interpreted with the relative permittivity, back-calculated using on-site borehole information instead of empirical values. The relative errors of the depth of horizontal interfaces of different strata range within ±5%. The relative and absolute errors of the V–shaped sand–rock interface depths are in the ranges of [−9.9%, 10.5%] and [−107, 119] mm, respectively. Finally, the GPR technique was used in the field to identify the strata of a slope from Tanglang Mountain in China. The continuous profile of the subsurface strata was successfully identified with a relative error within ±5%. [ABSTRACT FROM AUTHOR]

The Bomarzo Cathedral, also known as the Duomo di Bomarzo, is a remarkable historical and architectural masterpiece situated in Bomarzo (VT), Italy. Constructed in the 16th century under the sponsorship of the Orsini family, the cathedral's design is a harmonious blend of Renaissance and Baroque styles. Despite enduring numerous challenges, including damage from the Italian Wars, extensive restoration efforts were undertaken to preserve its cultural legacy. Driven by a deep appreciation of the cathedral's historical context, a ground-penetrating radar (GPR) investigation was deployed to gain insights into its foundations and potentially uncover buried remains beneath the floor and altar. The GPR investigation focused on the cathedral's interior, specifically the central and left naves, altar, and oratory. This revealed the presence of disclosed rectangular chambers beneath the floor and altar, along with unique foundation structures. These findings, coupled with historical insights and architectural understanding, emphasize the cathedral's cultural importance. [ABSTRACT FROM AUTHOR]

Unmanned aerial systems have a wide range of uses in studying the impacts of climate change over several fields. Recently, its combination with a ground-penetrating radar (GPR) technology has been demonstrated to be highly effective for surveying glaciers, especially in difficult and inaccessible terrains like Antarctica. In this context, this study focused on exploring the potential of using an unmanned aerial vehicle (UAV)-GPR to measure the depth of glaciers on Horseshoe Island, West Antarctica. The data were collected during the seventh Turkish Antarctic Expedition (TAE-VII) in February and March 2023, within the scope of the international project titled "Glacier monitoring and 3D modeling in Horseshoe Island Antarctica based on UAV-GPR observations", carried out by the bilateral cooperation of İstanbul Technical University and the Bulgarian Academy of Sciences. In order to determine the depth of the glacier, this investigation utilized both terrestrial GPR and UAV-GPR data. The UAV-GPR depth was determined as 9 cm root mean square error as a consequence of comparison with terrestrial GPR results. Furthermore, it was demonstrated that measurements performed with the UAV were completed approximately 25 times faster than those conducted with the terrestrial GPR, demonstrating a significant efficiency benefit. As a result, it can be concluded that using the airborne GPR approach offers a beneficial and effective way to undertake surveys of glaciers quickly and affordably with promising accuracy. [ABSTRACT FROM AUTHOR]

This paper deals with ground‐penetrating radar prospecting and exploits a semi‐heuristic strategy to account for inhomogeneous background media, empty cavities or topography of the surface. We assume here that no more than a commercial processing software is available. Customarily, commercial codes assume a homogeneous soil and a flat interface in order to achieve the focusing of the data. Therefore, this is also the model exploited here, whereas the data are referred to an inhomogeneous soil or to a non‐flat interface. The proposed strategy exploits the principle that 'the data speak', even if with some ambiguity and some reticence, and they can reveal or at least suggest important features of the underground scenario. On this basis, heuristically, we exploit features derived from the data themselves as a posteriori information, improving the available focusing and time–depth conversion even having at disposal a basic model of the background scenario. [ABSTRACT FROM AUTHOR]

A survey was conducted to investigate buried archaeological walls near the Ishtar temple in the ancient Babylon city using electrical resistivity tomography and ground‐penetrating radar methods. The survey includes applying 12 electrical resistivity tomography profiles using dipole–dipole array and a ground‐penetrating radar grid of 55 m × 50 m using 250 MHz antenna. Although the buried walls are consisting of mudbrick masonry and are embedded in a clayey environment, the electrical resistivity tomography method is still able to differentiate the tiny differences between the host materials and the buried walls, which show distinctive wall‐like features with resistivity values ranging between 9 and 15 Ω m. These features may reflect underground‐buried walls with a general width reaching about 2.5 m. The comparison of ground‐penetrating radar profiles and their corresponding electrical resistivity tomography profiles presents that the main architectures are coinciding well. The analysis of the geometry and composition of the walls around the Ishtar temple suggested that the wall‐like reflections on the ground‐penetrating radar slice at a depth between 140 and 150 cm (may be shallower) are underground‐buried walls. These wall‐like reflections show a special trend and orientation that indicate that they may be the remains of rooms belonging to two small houses or the remains of one big private house. [ABSTRACT FROM AUTHOR]