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Pipe-jacking construction technology has become more prevalent with the development of urbanization. At the same time, non-destructive detection of the compactness around the pipe-jacking is a necessary step in the pipe-jacking engineering. Therefore, the present study intends to explore the non-destructive detection of pipe-jacking quality by using ground penetrating radar. Ground penetrating radar can also be called GPR for short, and it plays an irreplaceable role in our life through its efficient and nondestructive detection function. In the study, void models for plastic jacking pipes, reinforced concrete jacking pipes, and steel jacking pipes were constructed, and forward simulations of ground penetrating radar were conducted using the two-dimensional finite element method to investigate the parameter influences brought about by seasonal changes and the shape of the voids; furthermore, combined with the sewage jacking pipe project in Xiangtan City, Hunan Province, practical detection and experimental research on reinforced concrete jacking pipes and steel jacking pipes are carried out using ground penetrating radar. The research results indicate that ground penetrating radar is feasible for detecting the density conditions around cement jacking pipes, plastic jacking pipes, and reinforced concrete jacking pipes. However, when detecting the void conditions around steel jacking pipes, strong multiple reflections occur between the radar antenna and the pipe wall, making it impossible to achieve the goal of detecting the density behind the jacking pipe. Therefore, future research is needed to develop new methods and technologies for assessing the construction quality of steel jacking pipes. • The function of the GPR in detection voids of underground pipe jacking. • Practical and experimental detection studies on four different types of pipe jacking. • Performing 2D Finite Element Method Forward Modeling of Ground-Penetrating Radar. • Research on the non-destructive testing of pipeline construction quality using GPR. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a widely applied shallow geophysical exploration method. However, the huge amounts of collected data from high efficiency and sampling rate are extremely time-consuming and high-cost to interpret. As the basis of full waveform inversion (FWI) and reverse time migration (RTM), the numerical simulation of GPR directly affects the accuracy and speed of the data interpretation. Besides, calculating 3D large-scale models on the personal computer (PC) is still difficult with limited memory. Therefore, an efficient and low-cost forward algorithm is required urgently. Inspired by the footprint of the airborne electromagnetic method (AEM), we propose a GPR moving finite domain (MFD) forward algorithm based on the attenuation characteristic of GPR high frequency electromagnetic waves to avoid excessive computation by limiting the calculation to the finite domain. We explore the relation between speedup and precision, summarize the optimal range of the parameter and constrain the MFD to further ensure the acceleration according to the time window. The error source and factors affecting the algorithm's speedup are explored and discussed to demonstrate its performance fully. The extensive numerical experiments emphasize that the algorithm could improve speed efficiently with ignorable loss of accuracy. Finally, the forward modeling of a large 3D model is carried out with the memory decreased by 78 % and the speed increased by 33.88 times on the PC, which is impossible through the conventional FDTD. The reduction of costs lessens the requirements for computer equipment, which is expected to promote the practical process of FWI and RTM. • The MFD algorithm is developed for GPR forward modeling in 2D and 3D. • The simulated region is adjusted automatically according to model parameters. • The proposed method effectively reduces the cost with ignorable accuracy loss. • The MFD is expected to decrease the computing cost of RTM and FWI. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) techniques have gradually improved for imaging different types of subsurface sedimentary structures, particularly in the top zone of carbonate rocks. The majority of typical underlying sedimentary heterogeneities, such as karst in An Narjis district north of Riyadh, Saudi Arabia, were created by natural processes. Geologically, the investigated area is dominated by limestone formations of the Arab and Sulaiy groups, allowing sinkholes, fractures, and collapse structures to emerge. This study employs field investigations and GPR to identify and detect the karst feature crucial for the construction of new urban areas to withstand the predicted socioeconomic pressures caused by the dense population. The GPR profiles were measured with an antenna of nominal central frequency 400 MHz, which was selected based on clutter limitations, depth of investigation, and desired resolution. The survey parameters, including time window and spatial and temporal sampling intervals, were estimated based on the relative dielectric permittivity of the encountered geologic materials and the frequency of the utilized antenna. A dewow filter, automatic gain control (AGC), background removal, predictive deconvolution, muting, time cut, and time-depth conversion were successively applied to all GPR profiles. The GPR radargrams were carefully analyzed by picking reflectors using phase follower and continuous pick, which were then combined and shown in layer show models. The GPR cross-sections and the derived layer show models highlight distinct reflection patterns (departure or broken parts of low amplitude reflections, diffraction hyperbolas, steeply dipping reflectors, resonant oscillations with no diffracted edges, chaotic background with no lateral continuity, and quasi-hyperbolic form), indicating karst features with different dimensions and depths while suggesting potential risks of collapse in certain areas. On the other hand, visually inspecting the outcrops of the Sulaiy Formation carbonates revealed NE-oriented widening karst fractures filled with red clay-rich residual deposits (karstic residue). GPR radargrams were compared to direct observation of geological features of karst structures found in exposures of vertical road cutting and exhibited a strong correlation. The study underscores the significance of proactive measures to mitigate these hazards in urban planning and construction. Mapping the risk areas, identifying early signs of sinkholes, and implementing preventive measures, such as reinforcing infrastructure, emerge as essential strategies to avert potential disasters. Overall, the integrated approach of field study and GPR presents a valuable method for understanding and addressing karst-related hazards in urban expansions, offering insights to safeguard against future risks in Riyadh's development and other sites with similar conditions. • Field observation and GPR constrain interpretations in an urban area of Riyadh, Saudi Arabia. • The GPR approach proved highly effective in identifying subsurface karst features. • Fractures control dissolution in Sulaiy Formation limestones through structures. • Collapse and sagging are suggested to be the main subsidence mechanisms. [ABSTRACT FROM AUTHOR]

Numerical simulation techniques for ground penetrating radar (GPR) railway ballast inspection offer significant advantages, including the avoidance of extensive field surveys and excavation work. This helps minimize construction challenges and costs while providing crucial technical support and insights for railway maintenance. Nevertheless, the intricate nature of ballast particles and bed structures, combined with the challenges in discerning their patterns, present formidable obstacles to achieving high-precision modeling. This paper employs the Particle Flow Code (PFC2D) to extract and project 2D natural ballast particles from laser scanning, generating a clean ballast physical model considering mechanical interactions. As fouling arises from fine particles smaller than 25 mm, the discrete random medium theory is applied to validate the heavy ballast fouling. This involves filling the voids in the clean ballast to simulate and analyze the electromagnetic properties of the ballast fouling. The generated ballast physical model is converted into HDF5 files and simulated using a 2.0 GHz Rayleigh wave excitation through the Finite Difference Time Domain (FDTD) method. Through S-transform and Hilbert energy results, it becomes feasible to accurately differentiate the ballast fouling. The study reveals that highly fouling ballast predominantly exhibits frequency energy concentrated within the 1.0–3.0 GHz range. As depth increases, the energy experiences faster attenuation, and the distribution of Hilbert energy becomes denser and stronger. Field tests conducted on a specific railway line in southern China validate the method's effectiveness, making it a valuable tool for guiding GPR-based ballast fouling detection projects and providing a scientific basis for railway infrastructure maintenance. • High-Precision Ballast Modeling: Employs PFC2D and laser scanning for detailed 2D physical models of railway ballast, enhancing inspection accuracy. • Innovative FDTD Simulation: Simulates electromagnetic properties of ballast fouling using the FDTD method with 2.0 GHz Rayleigh wave excitation. • Accurate Fouling Detection: Introduces S-transform and Hilbert energy analysis for precise differentiation of clean and fouled ballast conditions. • Field Test Validation: Confirms the effectiveness of the proposed method through field tests on southern China railway lines. • Depth-Related Energy Attenuation: Reveals that ballast fouling exhibits faster energy attenuation with increasing depth, concentrated within 1.0–3.0 GHz. [ABSTRACT FROM AUTHOR]

The archaeological area of Augusta Bagiennorum (NW Italy) is a well-known archaeological site, extensively investigated since the end of the 19th century. Previous archaeological studies in the area allowed a preliminary definition of the urban setting through several direct soundings. However, the current knowledge of the site needs to be more complete due to the limited extension of previous archaeological surveys. To obtain a more complete description of the urban setting, we analysed aerial and satellite images and multichannel Ground Penetrating Radar (GPR) surveys, which are two remote-sensing technologies often used in this field. Aerial images showed vegetation cropmark patterns in different areas, depending on the time of the day, year, and camera acquisition setup. The GPR surveys locally confirmed the presence of buried structures in correspondence with the cropmarks, and provided a more detailed view of the underground features. The GPR was also helpful in adding Ground Control Points for georeferencing hot-air balloon images. The combined view of aerial and GPR images allowed us to confirm the overall extent of the archaeological area, as reported in past archaeological reports. However, some of the observed structures were located outside the previously known extent of the town. The orientation of some suggests that the streets of Augusta Bagiennorum are not always arranged in a perpendicular pattern, typical of the Roman orthogonal town planning. The adopted methodological approach could be a valuable tool in similar case studies, since it provides multiscale information composed of both overall views and detailed images. • Different multitemporal images from satellites, planes, and balloons showed different vegetation cropmarks; • Multichannel GPR confirmed that the cropmarks are related to archaeological buried structures; • Updates on the urban layout of the Roman town of Augusta Bagiennorum. [ABSTRACT FROM AUTHOR]

Ground Penetrating Radar (GPR) is widely used for detecting buried utilities, but data interpretation remains challenging due to noise and clutter. Although various methods exist for processing GPR data, the Kalman Filter (KF) has been underutilised despite its strength as an estimator. Traditional KF-based algorithms in GPR studies often rely on chi-squared hypothesis testing, which requires expert-defined thresholds and can lead to biased or uncertain outcomes. This paper introduces a novel KF-based framework that addresses these limitations. The framework employs Kalman Filters for noise reduction, with an optimisation algorithm based on a genetic algorithm to fine-tune KF input parameters. A Normalised Innovation Squared (NIS) parameter is used to generate an NIS signal function for identifying anomalies. Additionally, discrete wavelet transforms are applied to the NIS signal function for anomaly detection and localisation, using varying decomposition levels and vanishing moments. Results demonstrate a proportional relationship between wavelet decomposition levels, selected wavelets, and the detection rates of true and false positives. Statistical analysis using receiver operating characteristic curves shows that the optimal detection rate for all tested wavelets occurs at decomposition levels 5 and 6. This framework enhances GPR data interpretation with minimal user interaction, representing a step forward toward autonomy in GPR data processing and interpretation. • Hybrid Kalman Filter and Wavelet approach improves GPR data interpretation. • Kalman Filter optimised using a genetic algorithm for noise reduction. • Wavelet Transform applied to detect and locate buried utilities. • Achieved high detection rates with minimal user intervention. • Proven in diverse real-world GPR datasets for accurate anomaly detection. [ABSTRACT FROM AUTHOR]

Different palaeoenvironmental features that pose natural geological, environmental, and engineering hazards to human operations occur frequently around the Nile Valley. Moreover, where these features were initially created, their relevance focuses on how the urban communities responded to the processes. So, a ground penetrating radar (GPR) field survey was carried out on different paleoenvironments of Pre-Quaternary and Quaternary sediment around Assiut. Deep and critical analyses of georadar facies were made to obtain clear images of these features with unprecedented resolution. The main objective of this study is to find some reasonable geological interpretations for these features. From this study, it is possible to identify and differentiate these features originating from different geological environments and climatological conditions in arid regions such as those around Assiut. In addition, the study serves as guidelines for environmental management and climatic changes for enhancing knowledge of urban development. Also, the study demonstrates how georadar can be used to create precise images of intricate shallow subsurface anatomy with possible palaeoenvironmental and palaeoclimatic indicators. • Create precise images of different subsurface features by GPR reflections. • Differentiate some features originating from different geological environments. • Trace of buried structures that may cause further damage in arid regions. • Significance of GPR imaging for minimizing potential risks facing future developments. [ABSTRACT FROM AUTHOR]

Ground-Penetrating Radar (GPR) has been widely used for non-destructive testing of earth dam disease. However, the forward simulation of GPR for earth dam disease often employs layered homogeneous models, neglecting the influence of medium randomness on its wave field characteristics. Therefore, considering the randomness of the medium, a geoelectrical model for earth dam disease is established, which is based on the mixed-type autocorrelation function and the finite element time-domain method. The influence of random medium model parameters on the single-channel wave of GPR is analyzed. The electromagnetic wave propagation characteristics under different medium models are explored. The forward simulation of GPR for earth dam disease such as panel voiding, concentrated seepage, and loosening are performed. The differences in propagation characteristics for earth dam disease between uniform medium model and random medium model are compared. Compared to the calculation results of the uniform medium model, the propagation speed and amplitude of electromagnetic waves in the random medium model changes, and a number of diffraction waves are present. When performing forward simulation of GPR for earth dam disease, considering medium randomness can deepen the understanding of the GPR section view and help improve the accuracy of image interpretation. • Constructing the random medium model with the mixed-type autocorrelation function. • Adopting the finite element time-domain method for the numerical simulation. • Considering medium randomness can improve the accuracy of GPR image interpretation. • Random medium model parameters have an effect on GPR single-channel waves. • Applying the random medium model for earth dam disease GPR simulation is valuable. [ABSTRACT FROM AUTHOR]

Canossa Castle is located in the municipality of Canossa 18 km South of Reggio Emilia (North Italy). It was constructed in 940 by Adalberto Atto, son of Sigifredo of Lucca. Lombard chieftains needed this strategic hill to defend their lands against intrusions of other barbarian tribes. Subsequent improvements made the stronghold one of the best-defended castles in the country. Canossa Castle became particularly famous as a site of reconciliation between King Henry IV and Roman Pope Gregory VII during the Investiture Controversy in 1077. To redevelop the area and create an easy tourist route, the Superintendence of Archaeology, Fine Arts and Landscape for the Metropolitan City of Bologna and the Provinces of Modena, Reggio Emilia and Ferrara planned excavations in the area close to the Castle. To get precise information on where to carry out excavations geophysical surveys were undertaken in the spring of 2021. The castle stands on a rock with a steep slope and dense vegetation and this makes it very difficult to carry out geophysical prospecting. This guided the choice of geophysical methodologies to be used. For this reason, electrical resistivity tomography was used along the steep slope, while in the narrow flatter area, the ground penetrating radar methodology was used. The results demonstrate the effectiveness of the chosen geophysical methodologies. • the Castle of Canossa. • geophysics on a rock with a steep slope and dense vegetation. • 3D resistivity anomaly probability tomography method. [ABSTRACT FROM AUTHOR]

Road underground defect detection plays a crucial role in assessing transportation infrastructure. Ground penetrating radar (GPR) serves as a widely used geophysical tool for this purpose. However, the traditional manual interpretation of GPR images heavily relies on the experience of the practitioner, leading to inefficiency and inaccuracies. To tackle these challenges, this paper proposes an automatic detection method for underground defects of roads based on an improved YOLOv5s model. First, the dense connection structure is integrated in the C3 module of the backbone to form the Dense-C3 module to enhance the capability of feature extraction. Subsequently, a convolutional block attention module (CBAM) is incorporated after each Dense-C3 module to refine features and enhance efficiency. Furthermore, the focal loss function is employed for the confidence loss to mitigate the impact of sample imbalance on detection performance. Experimental results demonstrate that the proposed model achieves a mean average precision (mAP) of 96.4% for synthetic data and 91.9% for real data, outperforming seven other models. The detection speed of the proposed model for real data reaches 51 frames per second, meeting the real-time detection requirements of road underground defects. • The Dense-C3 module is constructed to improve the feature extraction ability. • The CBAM is added after each Dense-C3 module to refine features. • The focal loss function is used to mitigate the impact of sample imbalance. [ABSTRACT FROM AUTHOR]

In this study, we propose an accurate gain method for ground-penetrating radar (GPR) signals based on the characteristics of refined time-frequency analysis and translation invariance offered by the Stationary Wavelet Packet Transform (SWPT), combined with the conventional signal gain approach. This method aims to address the issue of low signal resolution resulting from the direct gain processing of GPR signals with a low signal-to-noise ratio (SNR). Specifically, the GPR signals are initially decomposed into appropriate wavelet packet coefficients using SWPT, wherein only those coefficients with high SNR undergo gain processing, followed by reconstruction of the signals through SWPT. By employing accurate gain processing on low SNR GPR signals acquired during concrete crack detection tests, we have confirmed that the proposed method effectively distinguishes the target reflected signals from most noise, thereby achieving accurate amplification of the desired reflected signals and significantly enhancing the GPR signals resolution under low SNR conditions. • Introduced an accurate gain method for ground-penetrating radar (GPR) signals. • The method effectively separates target reflection signals from most noise and achieves accurate gain for these signals. • The method significantly enhances the accuracy of GPR signal analysis under low SNR conditions. [ABSTRACT FROM AUTHOR]

The municipality of Castrocielo (Frosinone, Italy) is a historically significant center which includes several centers of great archaeological importance, including part of the archaeological site of the ancient Roman city of Aquinum. In this work, we show the results of geophysical surveys performed in two different areas: the first area is close to the Monacato of Santa Maria al Palazzolo, built on the foundation slab of a Roman villa dating back to the 1st-2nd century BCE; the second area is close to the charging station Casilina Est, where several burials, dating back to different periods from 4th century BCE to 4th century CE, were found. The aims of geophysical investigations is to identify structures linked to the ancient Roman villa (Villa Eucheria) in Area 1, and to identify the tombs of the necropolis in Area 2. The two areas were investigated in two different days, on 27th and 28 th March 2023 respectively, through a multi-channel georadar system (GPR). In the second area, an electro-magnetometric survey was also performed. This choise is to address the heavy rain developed during the night before the acquisition. Infact the GPR survey performed during the second day of the geophysical campaign did not provide good results.Based on the geophysical results, the archaeological excavation in Area 1 confirmed the detected anomalies, documenting a section of wall and other structures and elements brought to light over a length of approximately 9 m. The results obtained in Area 2 confirmed the cropmarks visible in the aerial photo, highlighting the traces of buried structures. • Locating archaeological features with multichannel and multidepth instruments. • Multi-metodological geophysical acquisition in the study of ancient city. • Resistivity inversion of low-frequency EM data to enhance archaeological features. [ABSTRACT FROM AUTHOR]

At present, the detection of geological structural plane that give rise to rockburst is a complex problem. A large number of rockburst cases show that the geological structural plane influence the boundary of rockburst, especially the slip-type rockburst of structural plane. However, there is no set of comprehensive geological prediction methods at home and abroad for the detection of geological structural plane that give rise to rockburst, or low accuracy and high workload. Therefore, a method that can effectively detect geological structural plane is needed. Based on the analysis of regional engineering geological characteristics and geostress, this paper puts forward that based on the characteristics of rockburst cases in high geostress area and the rockburst prone to occur in the position with large geostress difference, the method of "combination of long and short geophysical prospecting method first, drilling and engineering geological analysis combination" is adopted to detect geological structural plane finely. The results show that: (1) Under the background of regional engineering geological survey and geostress inversion, the comprehensive geological prediction is carried out to detect the geological structural plane. The TSP has the detection accuracy of 10 m to meters, the ground penetrating radar has the detection accuracy of meters to decimeters, and the borehole imaging has the observation accuracy of centimeters. The combination of the three can multi-scale clearly reflect the distribution of geological structural plane compared with the previous single geological prediction. (2) Due to the complex distribution of surrounding rock mass interface and geological structural plane, the collapse phenomenon will also appear in the rockburst area on site, and the collapse causes a number of structural plane, which means that these structural plane becomes an important factor in slip-type rockburst of structural plane. In short, accurate detection of geological structural plane has certain reference significance for predicting the boundary of slip-type rockburst of structural plane and collapse. • The comprehensive geological prediction. • Detect the geological structural plane. • Predicting the boundary of the slip-type rockburst of structural plane and collapse. [ABSTRACT FROM AUTHOR]

The Roman Villa at Piazza Armerina, which has been on the UNESCO list of sites since 1997, is an exceptional example of a prestigious residence from the Late Antiquity period. Most of the area around the villa has not yet been explored and it is thought there are structures linked to the layout of the villa itself and subsequent modifications from the medieval era. With these assumptions and to plan an excavations campaign in the area close to the villa's warehouses geophysical surveys were undertaken in the summer of 2022 and 2023. Ground penetrating radar, magnetometry (in gradiometric configuration), and inductive electromagnetometry geophysical methods were used. These different methods were taken into consideration depending on the more or less conductive subsoil matrix. Results show the presence of several structures of archaeological interest. • the Villa del Casale (Unesco Site) • integrated geophysics • 3D methodological approach [ABSTRACT FROM AUTHOR]

A multi-method geophysical investigation was carried out in the context of a recovery project for the "Annunziata" Garden located in the town of Cammarata (Sicily), near to the homonymous church (Chiesa dell'Annunziata). In this area, according to the scarce historical sources, there was a Benedictine convent, probably demolished in the 18th century, but the area was probably inhabited even in earlier periods. Preliminarily, a series of 2D electrical resistivity tomographies (2D-ERT) were carried out approximately parallel to each other, some of which highlighted resistivity anomalies that could be attributed to buried archaeological structures. Consequently, in a smaller area where these anomalies were evident, a 3D electrical resistivity tomography (3D-ERT) and Ground Penetrating Radar (GPR) parallel profiles were carried out aimed at a detailed 3D reconstruction of the subsoil. Despite the unclear correspondence between the 3D-ERT inverse model and the GPR one, the 3D ERT confirmed the anomalies found with the previous 2D-ERT surveys, better defining its contours and geometries. The geophysical reconstructions served to indicate to the archaeologists the most promising areas for excavation tests that were carried out subsequently and confirmed the presence of archaeological structures, such as defensive walls whose origin and dating are still the subject of further archaeological studies. • 2D- and 3D-ERT and GPR surveys were carried out in Cammarata (Sicily), to detect the remains of an ancient monastery. • 2D-ERT sections provided initial resistivity anomalies, which could be ascribed to subterranean archaeological structures. • In a smaller area with anomalies a 3D-ERT and GPR surveys were carried out aimed at a 3D reconstruction of the subsoil. • 3D-ERT and GPR substantiated the anomalies enhancing the delineation of its contours and geometries. • Geophysical results were fundamental in planning targeted excavations. • The first archaeological excavation allowed to bring to light parts of a defensive wall. • Geophysical interpretation will be useful to plan further archaeological investigations. [ABSTRACT FROM AUTHOR]

Soil water content (SWC) is significant for understanding and evaluating the conditions of soils and plants. Since traditional methods such as time domain reflectometry (TDR) and neutron probes have significant drawbacks such as limitations in spatial resolution, detection depth, efficiency, and non-destruction, ground penetrating radar (GPR) has become a potential method in SWC estimation. Many features extracted from GPR data in the time and frequency domain have been proven to be sensitive to the SWC and can further achieve the estimation of it. However, the methods based on these features are easy to be interfered with by noise and the heterogeneity in soils. This article aims to solve this problem by including more features and integrating these features for a joint estimation. Firstly, we study the relationships between SWC and seven features extracted from GPR data. Consequently, we propose to include new features, i.e. the loss tangent feature and the time-frequency features, in the SWC inversion. Secondly, we achieve the multi-feature ensemble learning based on the Adaboost R. method, which largely enhances the accuracy of SWC inversions compared to the single-feature estimations. During the numerical test, we establish the stochastic medium to model the heterogeneity in the real soil. The test verifies the effectiveness and the robustness of the proposed method. Finally, a field experiment is performed on the transition zone of no-tillage and deep-ploughing croplands. A 2-D SWC map is obtained which distinctly presents the SWC difference between the two regions. Our study provides a new approach to improve the accuracy of SWC estimation using GPR. • A multi-feature ensemble learning method is proposed to inverse soil water content. • Loss tangent is used for the inversion of soil water content. • A two-dimensional soil water content map of a cropland is obtained. • Soil water content in no-tillage and deep-ploughing zones are strongly different. [ABSTRACT FROM AUTHOR]

The polar ice sheet is vital for the global climate system, influencing the Antarctic ice sheet's mass balance, sea level rise, and Earth's surface energy. Accurate measurement of its thickness and internal structure is imperative for comprehending glacier evolution and climate change. Ground Penetrating Radar (GPR) is a high-resolution and non-invasive exploration instrument that is extensively used for glacier detection, but only limited geometric information can be identified in the GPR profile, making it difficult to capture the quantitative distribution of physical parameters. To image the fine dielectric parameters of GPR data acquired from the Amery Ice Shelf in East Antarctica in 2003, we propose a multi-trace GPR impedance inversion approach. This method utilizes the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm for GPR data, coupled with weighted L 2 -norm total-variation multiplicative regularization (MR) to quantitatively estimate the dielectric parameters in the interior of the ice sheet. This scheme adaptively adapts regularization parameters, enhances vertical resolution, and suppresses noise. Additionally, considering lateral correlation, we integrate directional differences to enhance lateral continuity. After validating with complex test data, we apply the method to Amery Ice Shelf GPR data, quantitatively imaging the ice sheet's geometric and dielectric parameters. The inversion results reveal ice thickness, internal features, and the interface between freshwater ice and refrozen sea ice. This allowed us to determine the ablation and refreezing zone boundary at the bottom of ice shelf, providing insights into melting/freezing mechanisms, ice shelf stability, and simulating ice shelf interior dynamics. • Introduction of a novel Ground Penetrating Radar (GPR) multi-trace impedance inversion scheme. • Successful inversion of GPR data for Amery Ice Sheet sounding data. • Imaging of relative permittivity through high-resolution inversion results. • Valuable insights into melting/freezing mechanisms and ice shelf stability provided by inversion results. [ABSTRACT FROM AUTHOR]

The accurate detection of early-age cracking and voids in concrete structures is vital for assessing their health and preventing deterioration, ultimately saving lives and resources. While Ground Penetrating Radar (GPR) offers a widely used method for concrete inspection and defect detection, its accuracy is subject to various limitations influenced by factors such as software, defect dimensions, moisture, and the analysis and interpretation of data. This study presents comprehensive techniques for detecting, delineating, and analyzing hidden cracks and voids in concrete using GPR, validated through laboratory testing and real-world applications. Introducing "DefectInspectoPy," Python software, aims to address limitations in traditional GPR data processing software and defect detection methods. Results demonstrate the effectiveness of the proposed methods in accurately locating, analyzing, and delineating defects, aiding in determining coring locations for concrete structures. These findings have significant implications for enhancing safety, reducing maintenance costs, and preserving concrete infrastructure integrity. • GPR was effectively utilized to detect and delineate concealed cracks and voids within concrete structures. • Laboratory observations were conducted over a 24-day period to assess detection methods, data quality, and antenna limitations. • "DefectInspectoPy," Python software, was introduced to automate defect extraction and calculation, streamline the analysis process, and mitigate shortcomings in existing GPR data processing software. • The results demonstrated the high effectiveness of the employed methods in detecting, analyzing, and delineating defects within concrete structures. [ABSTRACT FROM AUTHOR]

The Ground Penetrating Radar (GPR) data interpretation of dam defects detection by manual process is a heavy task. Although the convolutional neural network (CNN) is applicable to detect road defects with the help of other auxiliary instruments and techniques, it is still a challenge for the dam defect detection by deep learning. To overcome this problem, a multi-output CNN model for GPR response recognition of dam defects is proposed. The training dataset and test dataset are produced from a large number of GPR responses with different types of dam defects forward modeling by GPRMax3.0 software. The results show that the highest recognition accuracy of the training dataset and test dataset is about 98% and 97%, respectively. To verify the effectiveness, the new proposed method is used to recognize the hidden GPR responses of dam defects in the real GPR data. The results show that the types and locations of dam defects in the real GPR data can be recognized and classified automatically, efficiently and accurately. • We propose an effective method for accurately identifying dam defects in GPR images. • We solve the problem that traditional method relies on complex manual operation. • We proposed method can assist in daily dam maintenance and repair projects. [ABSTRACT FROM AUTHOR]

Estimating resistivity distributions within dam structures accurately using electrical resistivity tomography (ERT) poses a significant challenge due to the limited 2D acquisition scheme, the 3D heterogeneity of near-surface materials, and the dynamic nature of fluid transport. To address this challenge, we present a new strategy by simultaneously inverting multiple collinear ERT datasets acquired from various standard electrode arrays. This strategy incorporates the coupling of the intrinsic parameter relationship into the objective function, constraining the inversion solution space by balancing the distinct resolution characteristics associated with the electrode arrays involved in the joint inversion process. Using synthetic Karst and infiltration models, we demonstrate that our strategy can yield accurate inversion results for both the Wenner and dipole-dipole datasets. Application of this strategy to a field case at Nanshan Dam in southeast China, which involves Wenner, Wenner-Schlumberger, and dipole-dipole surveys, successfully delineates preferential fluid seepage pathways. These findings are further corroborated by consistent inferred reflectors in Ground Penetrating Radar (GPR) profiles. The synthetic and field examples highlight the effectiveness of our strategy in achieving accurate and unified resistivity estimates by integrating multiple ERT datasets. • Multiple ERT datasets from various electrode arrays are jointly inverted. • A unified resistivity distribution across all resultant models is recovered. • The joint imaging of multiple ERT datasets is applied to detect a dam in China. • Preferential seepage pathways at the dam are delineated. [ABSTRACT FROM AUTHOR]

The analysis and utilization of frequency spectrum in emission signals from ground penetrating radar (GPR) are essential for investigating soil characteristics and enhancing urban management. This research focuses on assessing the effectiveness of earthworm activities in improving rainwater management in regions with clay soil, where water infiltration is challenging. Specifically, we examined the impact of two earthworm species, Lumbricus terrestris and Aporrectodea caliginosa , as well as a combination of both, on the permeability of clay loam soil. To evaluate earthworm activities, a gravity-driven film flow setup with nine tanks was employed. Furthermore, we suggest utilizing the Discrete Fourier transform to analyse the frequency spectra of different data types (traces, transects, and the average of tanks) and comprehend their influence on earthworm behavior. To investigate the relationship between fluctuations in infiltration velocity and the frequency spectrum signal over time and activity position, a correlation analysis was performed using linear regression models. The goodness of fit was assessed based on the coefficient of determination (R2). For the earthworm species L1, L2, and L12, the R2 values were determined as 0.8858, 0.847, and 0.9493, respectively. These results demonstrate that the frequency domain was derived from variations in ground penetrating radar signals, and a significant correlation exists between infiltration velocity and spectra influenced by earthworm activity. • Assess the effectiveness of earthworm activities in improving rainwater management. • Examine the impact of two earthworm species. • Analyse the frequency spectra of different data types. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) has become a widely used trackbed inspection tool for railroad maintenance engineers. The method is attractive, due to the ability to autonomously collect continuous trackbed quality related information at traffic speeds (20–200 km/h). Information on ballast quality, formation settlement and the presence of saturated materials is used to plan maintenance and budget material replacement. Ballast fouling levels and moisture content are key properties of ballast quality that affect the stiffness of the trackbed. Due to the often highly heterogeneous composition of trackbed materials, extracting related information from GPR data alone is challenging. This paper considers in detail how electromagnetic properties of trackbed materials, relating to varying degrees of ballast fouling and moisture content, affect attributes derived from reflected and scattered GPR signals. It is shown that the inherent ambiguities in the response of GPR to fouling and moisture within the trackbed mean that neither can be uniquely determined by the method. We present the less ambiguous measure of the thickness of clean ballast, a useful proxy for the overall fouling condition of the trackbed. • GPR attributes are modelled for a range of ballast fouling and moisture conditions • Ballast fouling and moisture content each influence the GPR response of a trackbed • Proportions of moisture and ballast fouling cannot be independently measured by GPR • Clean ballast thickness measured by GPR avoids the ambiguity from water content [ABSTRACT FROM AUTHOR]

Investigation into the electromagnetic response of porous media and the porosity distribution is a key for improved porous material. However, current ground penetrating radar (GPR) simulation techniques based on the finite difference method (FDM) or even finite element method (FEM) are mainly focused on describing macro characteristics of porous property. In this regard a novel methodology combining the FEM and B-spline wavelet on the interval (BSWI) is proposed to specify the propagation characteristics of GPR echoes in a micro-perspective way. A line current radiation model is first introduced to validate the high precision and multi-scale trait of BSWI. In addition, two representative models with different porosity distributions are established using the spectral decomposition and hybrid auto-correlation functions based on the stochastic process. By using this technique, specifically, can reduce the uncertainty of model error, making the models built more in line with the actual circumstances. Next, the multi-scale technique is developed based on the scaling function using tensor product of BSWI, by which the porous model can be split into multi-resolution space both temporally and spatially. Through this integration of parameter-oriented porous modeling and multi-resolution calculation technique, a set of examples and comparisons are presented, which validates the effectiveness and reliability of the proposed methodology for analyzing complex kinematic nature of diffraction and corresponding electromagnetic characteristics to different porosity distributions. • Proposing an approach to construct the model with specifiable porosity distributions for GPR testing. • Comparing the electromagnetic response of porous media using the proposed approach with that of the finite element method. • Investigating the kinematic nature of diffracted waves under different porosity distributions. • Presenting the shape function and element matrix of the B-spline wavelet on the interval. [ABSTRACT FROM AUTHOR]

The excavation-loose zone (ELZ) is a key factor for the design and construction of underground engineering. In this study, ground-penetrating radar (GPR), non-invasive and effective method, is utilized to estimate the ELZ around an excavated soft coal seam with roof and floor consisting of soft rock seams. The picture segmentation method based on the 2D maximum between-cluster variance is conducted to optimize the radargrams obtained GPR, and then the ELZ can be accurately estimated by analyzing the radargrams. The results show that the magnitude of the ELZ different along different survey lines and is distinct at different positions along the same survey line. A large ELZ is concluded to surround the two sides of the coal walls and most of the floor. Meanwhile, due to complicated geological structure, local fractured zones exist widely within the ELZ. This case study is quite useful for understanding the range of the ELZ around a soft coal seam with soft rock seams in the coal wall and floor. • GPR was used to detect the ELZ of soft coal seams with soft roofs and floors. • The picture segmentation method was conducted to optimize the radargrams. • The ELZ extent is not only different at different survey lines, but also distinct at different distances along the same survey lines. [ABSTRACT FROM AUTHOR]

GPR has been widely acknowledged as an effective and efficient technique for imaging the subsurface. But the process of constructing 3D GPR images (C-scans) is still subjective and mainly relies upon the operator's knowledge and experience. This study reviews the parameters that affect GPR imaging quality: namely, profile spacing (PS), slice thickness (ST) and interpolations. Feature characteristics that have a crucial influence on imaging quality were also identified. Through conducting 25 carefully designed empirical experiments on concrete as well as subsurface structures, the relationship between 3D imaging parameters and feature characteristics were observed. A general workflow was derived for GPR C-scan generation, which is analogous to the typical signal processing steps used in 2D radargram signal processing (Jol, 2009). Empirical values in workflow were based on the retrieval of known ground-truth data and comparison with the processed images, i.e. the closest to reality. Unlike 2D processing, the workflow for 3D is feature-oriented and case-specific, and the proposed workflow gives guidelines on suitable ranges for 3 major parameters when used in a variety of applications. It was identified that feature shapes and the ratios of feature size to radar footprint are of vital importance. With the proposed flowchart, the often vague "survey experience" is parametrized and standardized, and the upper and lower limits governing the generation of objective and trustworthy 3D GPR images are defined. This workflow for GPR 3D slice imaging also paves the way for GPR feature extraction and change detection commonly adopted in remote sensing. • Empirical experiments that quantified the relationship between GPR imaging parameters and feature characteristics. • Key parameters for 3D GPR imaging parameters are profiles spacing, slice thickness and interpolation methods. • Feature size and GPR wavelength are considered for determining suitable range defined for each imaging parameters. • Create a standardized workflow for 3D GPR depth slice generation. [ABSTRACT FROM AUTHOR]

This work describes and investigates the cavities detection in urban areas using a non-destructive method by using the ground penetrating radar (GPR) and modeling by developing a numerical code based on the FDTD method. Moreover, based on the change in the amplitudes of the reflection and diffraction waves from the surfaces of the cavities, through which we can distinguish the cavities with high accuracy. In order to implement the experimental study, we chose different sites with different physical properties located in Morocco: mountainous (Quarries Khamis Anjara) and coastal area prepared for construction. The main objective of the geophysical detection of the cavities and the fragile areas to avoid significant risk on the construction works. The experimental results show a big similarly to the modeling simulation results. Ended, the simulation results using the amplitude signal of the waves show a high accuracy compared to Radargram. • Modeling of GPR signals to detect the cavities location and to distinguish them for other targets. • Using GPR for the investigation and detection for cavities in different areas in physical properties. • The detection of cavities and their precise identification is very important to avoid the risk of landslides. [ABSTRACT FROM AUTHOR]

Electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) are frequently used geophysical methods in the exploration of near-surface targets such as archaeological features. These methods were therefore applied to investigate a wide area in the Side ancient city located nearby the Manavgat district of Antalya, Turkey. The main purpose of the geophysical survey was to answer several questions about the design or usage of a huge area located nearby the episcopal residence of Side that belongs to the late antique/early Byzantine times (5th/6th century A.D.). The investigation area is thought to be the garden (hortus) of the residence. The survey site covering an area of approximately 6000m2 was investigated in detail by two geophysical survey campaigns carried out in 2016 and 2017. During the two survey campaigns, 102 ERT and 127 GPR lines were measured. ERT data were collected by dipole-dipole electrode array, and a 500 MHz shielded antenna was used to measure the GPR data. By evaluating both data sets with appropriate processing steps, 3D geophysical models of the survey area were produced. These models provided obvious traces of buried anthropogenic structures, characterized by relatively high resistivity and high EM wave reflection amplitude. The identified structures lay on the NS-EW axis, which is consistent with the major layout of the city; however, they do not correctly align with the late period structures currently standing on the ground. Therefore, it is interpreted that these buildings belong to a predecessor period (i.e. Roman Imperial Period). Further information such as the purpose of the use of these buildings can only be revealed by an archaeological excavation. • A huge amount of ERT data were inverted with open source program BERT in 3D. • Integrated use of GPR and ERT methods reveals very clear image of the subsurface. • The reconstructed archaeological features contributes to understand the urban architecture of ancient Side. [ABSTRACT FROM AUTHOR]

Ground Penetrating Radar (GPR) is extensively employed for underground target detection, and the advancements in this domain have been hastened by the utilization of deep learning methodologies. Despite advancements in deep learning, practical engineering applications often face challenges in meeting the demanding requirements of large-scale data and high data quality. In this study, based on cross-domain transfer learning, we proposed a method for underground target classification of weak labeled GPR data. It combined strongly annotated source domain data with weakly annotated target domain data through adversarial learning to leverage the prior knowledge from existing large datasets and addressed the classification problem of small-sample GPR data. By employing adversarial learning to integrate strongly labeled source domain data with weakly labeled target domain data, we leverage prior knowledge from existing large datasets, thereby enhancing classification accuracy on a small GPR dataset. This approach reduces the requirements for annotation quality and sample quantity in the GPR B-scan dataset of underground targets. Additionally, it exhibits robustness to label noise, making it applicable to real-world GPR data classification tasks. Compared to some well-known networks, the proposed model achieved the best classification performance in multiclass classification problems, with an accuracy of 87.62% and an F1 macro score of 87.60% on the test set. These results represent an improvement of 17.14 and 17.20 percentage points, respectively, over the best-performing classical classification networks. Additionally, the proposed model's reliability in cross-domain transfer learning, even with limited data, was substantiated through a comparison with other few-shot learning networks. • Innovating GPR B-scan classification via weak-shot learning • Cross-domain transfer learning boosts accuracy, addressing data limitations effectively. • Highly practical, SimTrans-GPR outperforms traditional algorithms. • Resilient to label noise, applicable to real-world GPR data classification tasks. [ABSTRACT FROM AUTHOR]

We carried out a geophysical research project in the Middle Bronze Age village of Ustica (Palermo, Sicily, Italy), named "Faraglioni Village" after the stack formations which detach from the coast north of the archaeological site. The investigation, which comprised Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) techniques, allowed us to discover the buried foundations of an outwork fortification system never evidenced by previous archaeological studies, only hypothesised from the observation of aerial photography and partially outcropping boulders, which align roughly parallel to the main defensive wall of the Village. Our geophysical prospection involved the entire 250 m-long arc of the outward village defensive wall, with the acquisition of eleven ERT profiles and 27 GPR scans. The techniques were selected based on both favourable logistics and methods applicability: ERT sections allowed us to trace a series of high-resistivity anomalies arranged to form an arc-shaped structure along the perimeter of the defensive wall. GPR investigation was localised in the most accommodating patch of terrain of the site, with the effort of intercepting clear enough sections of the target, to determine more accurately its shape, depth, and overall dimensions. Our discovery paves the way for new investigations, mainly aimed at defining the timing of construction of the fortification system, as well as the function of the remains of other architectural structures identified close to the wall, which could represent the target of further geophysical investigations. • Middle Bronze Age fortification at Ustica discovered with a multidisciplinary study. • ERT and GPR reveal an arc-shaped path of buried fortification anomalies. • Discovery opens up future investigations to determine the timing of the fortification. [ABSTRACT FROM AUTHOR]

A method is required for accurate determination of the location (horizontal position and depth) of subsurface utilities with minimal digging. Ground-penetrating radars (GPRs) have been used at many sites as a nondestructive method for detecting underground structures. Previous studies have examined the accuracy and resolution of GPRs based on comparisons of exploration images and factors such as soil, pipe material, and environmental conditions. Although numerous studies have explored the influence of relative permittivity and water content on GPR surveys, only a limited number have conducted direct measurements of water content using a moisture sensor within a soil tank and applied this data to enhance the precision of depth estimation. In this study, the effects of pipe material, relative permittivity, and antenna frequency on the depth estimation accuracy were quantitatively investigated using an artificial soil tank made of silica sand with known physical properties. In addition to the GPR survey, relative permittivity measurements were conducted using time-domain reflectometry (TDR) sensors located at different water levels. The results show that the antenna frequency and pipe material affect the resolution but not the depth estimation accuracy. Furthermore, the GPR survey using the relative permittivity from the TDR sensors estimated the pipe depths with a relative error of <7.33% relative to the actual burial depth under the following conditions: a volumetric water content of 2.6–38.2%, the relative permittivity of 2.9–23.2, and buried pipes of 0.1 m in diameter and 1.5 m in depth. The study findings show that the selection of an appropriate frequency and the estimation of soil permittivity using a moisture sensor can improve the depth estimation accuracy of buried pipes by GPR survey. • A soil tank with variable water levels and buried pipe type was constructed. • The depth of the pipes was estimated using GPR and TDR sensors. • The antenna frequency and pipe type did not affect the depth estimation accuracy. • The TDR sensors improved the accuracy of the buried pipe depth estimation. [ABSTRACT FROM AUTHOR]

A precise and detailed knowledge and characterization of the subsurface is crucial for urban engineering planning and implementation. Drillings/boreholes are most widely used to provide the spatial distribution of blocks and boulders, but it is difficult to determine with high spatial-resolution location and distribution of the blocks and boulders in the soil stratum. We evaluate the applicability and the effectiveness of borehole Ground Penetrating Radar (GPR) for urban engineering purpose and we test attribute analysis to help the subway construction in the city center of Hangzhou, China. A multi-attribute approach exploiting amplitude-based and textural attributes is used. The results demonstrate that attribute analysis can visualize and quantitatively characterize borehole GPR features in an automatic and objective manner, maximizing the information for urban engineering. The integrated results from drillings and borehole GPR can provide an effective distribution of localized heterogeneities (such as blocks/boulders, sand/gravel lenses, cavities) in the potential target area. • We evaluate the applicability and the effectiveness of borehole Ground Penetrating Radar (GPR) for urban engineering. • We test borehole GPR attribute analysis to help the subway construction. • A multi-attribute approach exploiting amplitude-based and textural attributes is used. • The integrated results can provide an effective distribution of localized heterogeneities. [ABSTRACT FROM AUTHOR]

Abstract Cross-hole radar is an effective method for geophysical exploration of underground geological conditions. Besides cross-hole radar, some geological information can be obtained by other methods, such as preliminary geological survey, geologic drilling and long-distance geophysical prospecting. This information can work as priori information to help interpret cross-hole radar detection data. Full waveform inversion (FWI) is an advanced inversion method for cross-hole radar, having higher resolution than the ray tomography method. However, traditional FWI for cross-hole radar takes less advantage of the prior information. Therefore, the introduction of the priori information into FWI is worthy of in-depth study to improve inversion quality. Here we use a special penalty function to impose inequality constraint on the objective function, and then calculate a new update step length value different from the traditional FWI. We use this method to introduce the priori information and improve the accuracy and stability of FWI. The reasonable value range of weight coefficients is discussed, which can control the strength of inequality constraint. Several inversion examples are designed to validate the effect of this improved FWI method. The inversion examples for synthetic and field data show that FWI based on inequality constraint for cross-hole radar has better inversion effect than the traditional FWI. To a certain extent, the introduction of inequality constraint can constrain inversion process, improve the inversion stability and provide higher precision inversion results. Highlights • Introduce priori information into full waveform inversion to improve its accuracy and stability. • Apply the inequality constraint representing the parameter variation range into full waveform inversion. • The inversion examples for synthetic and field data has good inversion results. [ABSTRACT FROM AUTHOR]

Abstract Ground penetrating radar (GPR) is increasingly used for subsurface detection of structures due to nondestructive characteristics. Use of the method for studies of coastal depositional environments is growing in southern Brazil. In this paper, we propose an analysis of traces in the variation in signal amplitude, integrated with the degree of compaction determined in the drill hole, for the characterization of depositional environments in regressive barriers. Signal attenuation with depth was interpreted from the least squares method for two cases of contrast in volume of rainfall. Results show efficiency of the methodology in determination of contacts between different deposits at depth, showing an increase in signal amplitude at the contact between the original deposits from low to high compaction. Precipitation is not relevant for signal attenuation in the sandy deposits studied. Highlights • Trace analysis from GPR and compaction is an effective tool for recognition of regressive barriers. • 200 MHz central frequency antenna has the highest resolution for interpretation of four deposits. • Low-frequency antennas (80 and 100 MHz) identify lower shoreface deposits. • Signal attenuation by rainfall is not relevant deposits with water level close to the surface. [ABSTRACT FROM AUTHOR]

Abstract Ground-penetrating radar (GPR) is a non-destructive tool that can be used to help detecting water leaks in water distribution networks. This study, which was undertaken in the framework of the SENSPORT project (Walloon Region, Belgium), aims at improving GPR-based detection of pipes and leaks in water distribution networks using advanced, integrated radar data processing and visualization strategies. The proposed method includes a physically-based near-field antenna effects removal approach and reflection detection and segmentation algorithms. It also involves a quantitative estimation of the medium properties using full-wave inversion. Finally, a specific human-computer interface allowing the end-user to visualize buried utilities and 2-D/3-D processed data on mobile devices is proposed. We successfully validated the general methodology through a laboratory experiment with near-field measurements performed at different times over a leaky pipe buried in a sandbox. This integrated tool appears to be promising to help detecting and monitoring water leaks. Future research will focus on more complex real cases. Index Terms—Full-wave inversion (FWI), ground-penetrating radar (GPR), human-computer interaction, subsurface imaging, water leaks detection. Highlights • A novel integrated tool taking advantage of advanced radar data processing strategies is proposed to help detecting leaks in water distribution network. • Qualitative data processing strategies improve the interpretation of the 3-D subsurface radar images and also the automatic detection of underground utilities. • Quantitative data analyses provide an estimation of the pipe depth and the relative permittivity/water content around it. • The results evidence the interest of combining the qualitative information (GPR data visualization) with thequantitative information (GPR data inversions). [ABSTRACT FROM AUTHOR]

Abstract Breaching of coastal barriers, leading to the formation of ephemeral or permanent inlets, is an essential element of coastal evolution in many parts of the world. With time, some inlets migrate or close, leaving no surface signature of their existence. The challenge of locating these channels through coring is exacerbated by high gravel content and widespread anthropogenic development. Subsurface imaging using ground-penetrating radar (GPR) is often the only viable technique to overcome this limitation. GPR surveys carried out across microtidal baymouth barriers fronting salt ponds on Cape Cod, Massachusetts reveal a series of paleo-channels that lack any geomorphic expression. In some areas, channel fill comprises 50–80% of the barrier lithosome. The pre-closure dimensions of historic channels identified on charts and photographs spanning the past 170 years were measured in these subsurface records. In addition, records revealed eight previously unknown relict channel structures with widths (W) ranging between 25 and 100 m and depths (D) of 1–4 m. Based on minimum cross-sectional dimensions in GPR images, a W/D ratio of ~40 is characteristic of the natural (unstabilized) paleo-channels. This study demonstrates that the distribution and morphometry of buried channels based on high-resolution geophysical images can be used to reconstruct past equilibrium hydrodynamic conditions, with implications for locating important alongshore anomalies and assessing coastal vulnerability to breaching. Highlights • This paper presents a comprehensive georadar investigation of buried coastal channels along a formerly glaciated coastline. • Due to coarse-grained nature and widespread anthropogenic structures, traditional geological methods e.g., coring and trenching) are not feasible, making GPR the only viable methodology. • This study not only confirmed the locations of historic inlet channels, but also discovered several unknown features. • Geophysical records allowed quantification of gross channel geometry, with implications for reconstructing hydraulic conditions. [ABSTRACT FROM AUTHOR]

Abstract Data processing techniques for Ground Penetrating Radar (GPR) image mining provide essential information to optimize maintenance management of Water Supply Systems (WSSs). These techniques aim to elaborate on radargrams in order to produce meaningful graphical representations of critical buried components of WSSs. These representations are helpful non-destructive evaluation tools to prevent possible failures in WSSs by keeping them adequately monitored. This paper proposes an integrated multi-criteria decision making (MCDM) approach to prioritize various data processing techniques by means of ranking their outputs, namely their resulting GPR image representations. The Fuzzy Analytic Hierarchy Process (FAHP) is applied to weight various evaluation criteria, with the purpose of managing vagueness and uncertainty characterizing experts' judgments. Then, the ELimination Et Choix Traduisant la RÉalité III (ELECTRE III) method is used to obtain the final ranking of alternatives. A real case study, focusing on a set of four GPR images as outputs of different data processing techniques, is presented to prove the usefulness of the proposed hybrid approach. In particular, the GPR images are ranked according the evaluation of five criteria namely visualization, interpretation, identification of features, extraction of information and affordability. The findings offer a structured support in selecting the most suitable data processing technique(s) to explore WSS underground. In the presented case, two options, namely the variance filter and the subtraction methods, offer the best results. Highlights • An integrated multi-criteria approach is proposed to prioritize various data processing techniques for GPR image mining. • Four representations of WSSs are produced by analysis of raw images, multi-agent system, subtraction and variance filter. • The FAHP is applied to weight five evaluation criteria by considering judgments of an expert in the field. • The ELECTRE III method is proposed to rank GPR images and establish the technique more suitable for maintaining WSSs. [ABSTRACT FROM AUTHOR]

Abstract In order to improve the resolution of GPR profiles from the perspective of data processing, the migration technique is applied to forward and measured GPR profiles. Given the shortcomings of the conventional GPR migration velocity determination method, such as strong randomness, uncontrollable error and massive consumption of workforce and material resources, therefore, a new method for determining GPR migration velocity based on cross-correlation analysis and least square fitting has been proposed. According to the determined velocity, three migration algorithms (Kirchhoff, F K and finite difference) can be used to process the forward and measured GPR profiles (pipes detection), the migration effect evaluated from image display, the information entropy, the operational efficiency and the accuracy of the determined velocity also reflected from migration results. At the same time, the radius inversion is realized under the coordinate points picked up by the cross-correlation analysis and fitted equations. In addition, a series of studies show that the migration velocity determined by the method proposed in this paper can be used not only to migrate GPR profiles, but to converge the hyperbola in profiles completely, which indicates the velocity is relatively accurate. By analyzing the effect of the GPR profiles migrated by three algorithms, it can be found that the F K algorithm has a strong applicability to the forward and measured profiles of GPR and that the radius inversion errors of forward and measured profiles can be controlled below 6% and 12% respectively. Highlights • Put forward a new method to estimate GPR migration velocity accurately • Use three algorithms and velocity for GPR profiles migration • Realize the inversion of target radius • Determine the more suitable algorithm for the radar profiles of typical buried pipes detection [ABSTRACT FROM AUTHOR]

This paper develops a fully automatic algorithm to detect steel rebar in concrete bridge decks from ground penetrating radar (GPR) data. The surveyed GPR data from reinforced concrete (RC) structure carries significant amount of information, such as rebar locations, dielectric constant of concrete, corrosion conditions of steel rebars, and etc. However, current methodologies to identify rebar hyperbolas from GPR data are either manual or interactive, which is time-consuming. An unsupervised, efficient, and robust pattern recognition algorithm is proposed. In the algorithm, a template of a rebar hyperbola is selected first from the data, and is updated automatically to achieve better performance of rebar detection during the data processing. A rebar hyperbola in the GPR data is detected from the local minimum points on the map of sum of square difference (SSD) with an adaptive threshold value. An output file is created and saved, containing the amplitudes of rebar reflection and the geometrical locations of the detected rebars. The proposed algorithm is tested with ground-coupled GPR data collected from several decommissioned RC slabs, which are saw cut from a highway bridge. The overall percentage of accurate rebar hyperbola detection is 88.0%. Finally, the reflection amplitudes extracted from the rebar hyperbolas are used to create a corrosion indicator map, which shows good agreement with the results of half-cell potential maps. [ABSTRACT FROM AUTHOR]

With the increasing coal mining efficiency, the geological disaster sources in the mine serious threat production safety, Ground Penetrating Radar (GPR) is widely used for detecting those hidden disaster sources attributed to the advantage of high precision, but with detection distance limitation. We describe a novel GPR system for large-depth disaster detection in mine, which has the large-depth detection ability and the explosion-proof capability to adapt to the mine working environment. In comparison with the existing GPR systems, this novel system is improved with the following design features: the step acquisition accuracy of the host control unit is improved to be 2 ps; the split pluggable low frequency combined antenna realizes the impedance matching and improves the detection distance; a photoelectric conversion module is developed to guarantee 6000 m transmission distance for the high-frequency synchronization signal in mine. We carry out some experiments to validate the design, and the measurement results show that: the proposed GPR system can realize the fine exploration and the accurate identification; the maximum detection distance can be up to 80 m with reflection method and 300 m with transmission method under uniform geological conditions, which break the current detection distance limitations within 30 m, simultaneously, the detection accuracy can be up to 3 m. We expect that the proposed GPR system will be used for the large-depth geological disaster sources detection and provide an advanced approach for improving mining safety. [ABSTRACT FROM AUTHOR]

This article describes the use of non- or minimally destructive methods to study damp in San Juan Bautista Church at Talamanca de Jarama in the Spanish province of Madrid. The combination of Electrical Conductivity Meter (EC), Infrared Thermography (IR), RH/T monitoring by means of Data Loggers (DL) and Wireless Sensor Networks (WSN), Electrical Resistivity Tomography (ERT) and Ground-Penetrating Radar (GPR) techniques provided sub-surface and surface information. The respective findings and ranges of observation were inter-related to identify the decay associated with the damp and determine the advantages and drawbacks of each instrumental method. Electrical conductivity meters and infrared thermography deliver rough estimates of the extent of damp on wall surfaces, furnishing qualitative information on the areas where depth studies should be undertaken using techniques such as ERT or GPR or where long-term WSN monitoring sensors should be positioned to determine variability in a given number of points. When the purpose is to distinguish between areas with widely varying moisture contents, ERT is more precise. However, GPR proved to be the most accessible and efficient technique for studying the under-floor profiles. [ABSTRACT FROM AUTHOR]

In this study we explored the possibility and effectiveness of applying ground penetrating radar (GPR) to investigate underwater cultural relics. Using this innovative technique, the remains of Yue kiln and anomalous objects buried beneath the lake or suspended in the water can now be fully studied. Although GPR has been widely used for imaging archaeological sites, skepticism remains over its use in underwater archaeology. Nevertheless, some scholars suggest that it is possible to use GPR to detect underwater remains, which has been further confirmed by the use of electrical parameter measurement (EPM) in Shanglinhu Lake. An experiment was performed by our team using antennas of different frequencies in a particular area of the lake. The results showed that the penetrability of the 100 MHz antenna was enough to examine the targets, while the 400 MHz antenna performed well in shallow water (depth <4 m) due to higher resolution. Hence, for our underwater investigation, we used both antennas for maximum coverage. We used a radargram as a topographic map which shows cross sections of the lake. Analysis of the Shanglinhu Lake data indicated that GPR is a superior tool for underwater archaeological investigation. Traditional methods including sonar, underwater cameras and salvage dives were used as supporting techniques in the study. [ABSTRACT FROM AUTHOR]

Orthogonal scanning is a ground penetrating radar (GPR) method recently developed for underground inspection. Orthogonal scanning GPR is based on measuring the anisotropy of soil layers. When applied to detection of underground narrow rectilinear objects, this method allows for improving the signal-to-noise ratio ( SNR ) and selectivity of GPRs. (Noise refers to signals reflected from large voids, debris of construction materials, etc.). In this work, we further develop the orthogonal scanning method and show that this method can increase the SNR also when the reflected signal is very small, and also significantly improve the horizontal resolution. We show that the SNR improving factor can reach several tens of dB. To improve selectivity we have developed a method of dual orthogonal scanning (the so-called differential method) which can significantly improve the selectivity in the horizontal plane. It is shown that for an object depth of 20 m, it is possible to achieve a selectivity of 0.55 m, which is much smaller than that of commercial GPR (5 m). [ABSTRACT FROM AUTHOR]

Nowadays, there is growing interest in controlling and reducing the amount of water lost through leakage in water supply systems (WSSs). Leakage is, in fact, one of the biggest problems faced by the managers of these utilities. This work addresses the problem of leakage in WSSs by using GPR (Ground Penetrating Radar) as a non-destructive method. The main objective is to identify and extract features from GPR images such as leaks and components in a controlled laboratory condition by a methodology based on second order statistical parameters and, using the obtained features, to create 3D models that allows quick visualization of components and leaks in WSSs from GPR image analysis and subsequent interpretation. This methodology has been used before in other fields and provided promising results. The results obtained with the proposed methodology are presented, analyzed, interpreted and compared with the results obtained by using a well-established multi-agent based methodology. These results show that the variance filter is capable of highlighting the characteristics of components and anomalies, in an intuitive manner, which can be identified by non-highly qualified personnel, using the 3D models we develop. This research intends to pave the way towards future intelligent detection systems that enable the automatic detection of leaks in WSSs. [ABSTRACT FROM AUTHOR]

Non-destructive investigation of monuments can be an extremely valuable tool to evaluate potential structural defects and assist in developing any restoration plans. In this work, both Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) techniques were applied to a tower wall of the Heptapyrgion fortress located in Thessaloniki, Greece, which was facing significant moisture problems. GPR cross sections, mainly obtained with a 500 MHz centre frequency antenna, and ERT profiles were collected along the same survey grid on the tower wall. The gprMax numerical solver was used for the GPR forward modelling. In addition, an auxiliary program was used to design and import into gprMax complicated structures and this allowed to simulate more realistically the wall defects and moisture. The GPR simulator was used to assess and optimize the field data acquisition and processing parameters, and to assist in interpreting the GPR cross sections. The ERT sections were inverted as individual 2D lines and also, as a full 3D dataset. The final GPR and ERT data were jointly interpreted in view of the studied problem as results of both methods are highly correlated. A high moisture content area at the eastern part of the wall was identified in both GPR and ERT data, along with the interface between different phases of construction. Through the GPR data we were also able to delineate possible structural defects (cracks, small voids) which was not possible with just using the ERT data. Furthermore, a very good matching was evident between the simulated GPR modelling results incorporating field-interpreted features, and the actual field GPR results, thereby validating the proposed data interpretation. The overall survey and modelling approach produces results that are in a very good agreement between them and proved very useful in accessing the wall structure. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is applied for the characterization of the ground conditions in urban areas. In addition, time domain reflectometry (TDR) and dynamic cone penetrometer (DCP) tests are conducted for the accurate analyses of the GPR images. The GPR images are acquired near a ground excavation site, where a ground subsidence occurred and was repaired. Moreover, the relative permittivity and dynamic cone penetration index (DCPI) are profiled through the TDR and DCP tests, respectively. As the ground in the urban area is kept under a low-moisture condition, the relative permittivity, which is inversely related to the electromagnetic impedance, is mainly affected by the dry density and is inversely proportional to the DCPI value. Because the first strong signal in the GPR image is shifted 180° from the emitted signal, the polarity of the electromagnetic wave reflected at the dense layer, where the reflection coefficient is negative, is identical to that of the first strong signal. The temporal-scaled GPR images can be accurately converted into the spatial-scaled GPR images using the relative permittivity determined by the TDR test. The distribution of the loose layer can be accurately estimated by using the spatial-scaled GPR images and reflection characteristics of the electromagnetic wave. Note that the loose layer distribution estimated in this study matches well with the DCPI profile and is visually verified from the endoscopic images. This study demonstrates that the GPR survey complemented by the TDR and DCP tests, may be an effective method for the characterization of ground conditions in an urban area. [ABSTRACT FROM AUTHOR]

Ballast plays an important role in the stability of railway track systems. The effectiveness of the ballast in maintaining the track stability is very much dependent on its mechanical conditions. The available ballast maintenance approaches are mainly based on only track geometry conditions (such as track profile) which do not sufficiently reflect the ballast mechanical behaviors. That is, the ballast potential of degradation (i.e., ballast long term behaviors) has been omitted. This makes the effectiveness of the current ballast maintenance approach questionable, indicating a need for a more comprehensive and effective ballast conditions assessment technique. In response to this need, two ballast condition indices based on ballast geometry degradation (BGI) and the level of ballast fouling (BFI) as the main indicators of ballast mechanical behavior were developed. The BGI is a function of the standard deviations of track alignment, unevenness and twist. The BFI was developed based on the data obtained from the ground penetration radar (GPR). Making use of the new indices, a more reliable maintenance algorithm was developed. Through illustrations of the applicability of the new maintenance algorithm in a railway line, it was shown that the new algorithm causes a considerable improvement in the maintenance effectiveness and an increase in the life cycle of railway tracks by making more effective allocation of resources and more accurate maintenance planning. [ABSTRACT FROM AUTHOR]

Full waveform inversion (FWI) of ground penetrating radar (GPR) is a promising technique to quantitatively evaluate the permittivity and conductivity of near subsurface. However, these two parameters are simultaneously inverted in the GPR FWI, increasing the difficulty to obtain accurate inversion results for both parameters. In this study, I present a structural constrained GPR FWI procedure to jointly invert the two parameters, aiming to force a structural relationship between permittivity and conductivity in the process of model reconstruction. The structural constraint is enforced by a cross-gradient function. In this procedure, the permittivity and conductivity models are inverted alternately at each iteration and updated with hierarchical frequency components in the frequency domain. The joint inverse problem is solved by the truncated Newton method which considering the effect of Hessian operator and using the approximated solution of Newton equation to be the perturbation model in the updating process. The joint inversion procedure is tested by three synthetic examples. The results show that jointly inverting permittivity and conductivity in GPR FWI effectively increases the structural similarities between the two parameters, corrects the structures of parameter models, and significantly improves the accuracy of conductivity model, resulting in a better inversion result than the individual inversion. [ABSTRACT FROM AUTHOR]