Search

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

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

Despite improvements in nondestructive testing (NDT) technologies, the quality assurance of concrete reinforcing bar placement is still primarily conducted with conventional methodologies, which can be time-consuming, ineffective, and damaging to the concrete components. This study investigated the performance of two commercially available cover meters and one groundpenetrating radar (GPR) device. A cover meter was found to have the greatest accuracy for depths smaller than 3.19 in. (81.0 mm), while the GPR performed better for greater depths. The effect of reinforcing bar depth, diameter, and type; neighboring reinforcing bars; and concrete conditioning on the performance of the devices was quantified. The use of epoxy-coated reinforcing bar, galvanized reinforcing bar, and stainless-steel reinforcing bar were found to have a negligible effect on cover meter accuracy. A model was developed to predict the precision of the GPR post-measurement analysis given a depth and concrete dielectric constant. [ABSTRACT FROM AUTHOR]

The RIMFAX ground‐penetrating radar (GPR) on Mars2020 Perseverance Rover is the first GPR operated on the Martian surface since February 2021, searching for stratigraphy beneath the Jezero crater. During its operations, GPR detected several strong reflectors extending from the exposed section of the Séitah formation down to depths of 15 m, with derived relative permittivity of ∼9.0 consistent with low‐porosity mafic rocks. We reprocessed all the SHAllow RADar (SHARAD) observations at a higher resolution and combined repeat‐passes coherently and/or incoherently for clutter mitigation. We then examined the bright returns searching for subsurface structures. The reprocessed data did not show any shallow reflectors like those detected by RIMFAX. We investigated possible factors influencing the lack of shallow reflectors in SHARAD radargrams, including the properties of the older volcanic lithologies and the significant variability of subsurface reflectors within the SHARAD km‐wide spatial footprint, which prevents the formation of coherent reflections. Plain Language Summary: The detection of subsurface sequences by RIMFAX beneath the Jezero crater has sparked renewed debate about the possibility of detecting the boundaries between the crater's units from orbit using the Shallow Radar instrument. We have taken full advantage of the entire SHARAD coverage to examine the near‐subsurface regions beneath the Jezero crater, looking for subsurface returns. The detection of the reflectors recorded by SHARAD may be compromised by the spurious radar returns, which tend to mask the subsurface signals. Therefore, advanced processing specifically designed for resolution improvement and clutter mitigation have been applied to the entire sounder data set prior to searching for interfaces. Despite the signal enhancements, our survey of 56 observations found no returns from the subsurface stratigraphy. The lack of detections may be ascribed to several aspects, including the properties of the rough ancient materials that characterize the crated floor and the physical limitations of the orbital observations. Key Points: RIMFAX‐detected interfaces are within SHARAD's ranges; however, no reflector can be identified in standard radar data at overlap regionsResolution improvements and clutter mitigation techniques still highlight a lack of detectable subsurface reflectionsRough surface, volume scattering and attenuation over older volcanics (>1.5 Ga) significantly inhibits orbital data interpretation [ABSTRACT FROM AUTHOR]

Assessing the compaction quality of rockfill materials is an essential link in the runway construction. However, traditional on-site limited sampling detection is not only time-consuming and labour-intensive, but also destructive. Common ground penetrating radar (GPR) calculates the relative permittivity of a material by measuring its thickness. Nonetheless, assessing the thickness of runway construction materials poses a significant challenge. To address this challenge, this paper focuses on the Hilbert -Huang transform (HHT) analysis of GPR signal of different compacting materials, and then replaces the traditional method of calculating the relative permittivity by measuring the thickness of materials. At the same time, taking the runway as an example, this work verified a crest factor (CF) index effectiveness in predicting the relative compaction of rockfill material through HHT analysis of GPR signals collected in the field, with an average error rate of 4.03%. Finally, Kriging interpolation method is used to estimate the compaction quality of any point, and the corresponding heat map of compaction quality evaluation is generated to determine the area of insufficient compaction in the construction process. Highlights The GPR method is used for non-destructive testing of the compaction quality of runway rockfill materials; Compaction quality evaluation by multiple indicators of GPR data signal processing. The influence of number of roller passes, materials mix ratios and different rockfill materials on the compaction quality of runway are analysed. The applicability of the GPR method is verified by runway construction tests. [ABSTRACT FROM AUTHOR]

Monitoring and determining the development of frozen walls is a critical aspect of ensuring safe tunnel construction during freezing periods. Traditional temperature measurements for estimating freezing front expansion are constrained due to the limited number of measurement points and their fixed distribution. Here we introduce an innovative non-destructive detection method to investigate freezing wall expansion behind tunnel shield segments. First, dielectric properties of frozen and unfrozen clay and sandy soil were measured through laboratory experiments. Second, a freezing front model was established using a combination of numerical simulations and laboratory experiments. A 600 MHz radar was employed for detection and identification. Results indicate substantial differences in the dielectric constants of frozen and unfrozen soil, which can be employed as a convincing parameter for GPR to distinguish between them. Detection results demonstrate that GPR possesses the capability to determine the development pattern and approximate location of the frozen front behind shield tunnel segments. This method exhibits significant potential as an efficient and rapid means of detecting freezing wall expansion in tunnel engineering. Highlights: The difference in dielectric properties of frozen & unfrozen soils depends on the unfrozen water content. The electromagnetic responses of freezing front were studied by numerical simulation and indoor experiment. The accuracy of detection results from ground-penetrating radar was verified by conventional thermal methods. A fast and non-destructive method for determining the development of freezing fronts behind shield tunnel segments was proposed. [ABSTRACT FROM AUTHOR]

The nondestructive testing of asphalt pavement using ground-penetrating radar (GPR) is based on studies on the dielectric properties of asphalt concrete. The equivalent circuit concept was used in this study, where different mediums are equivalent to a single multilayer medium. The capacitance of each layer medium was used to derive the composite dielectric constant. Using the open coaxial probe method, the dielectric constants of AC-13 and AC-16 concrete composed of No. 70, No. 90, and styrene-butadiene-styrene (SBS)–modified asphalt binder were measured. The results reveal that the dielectric constant of asphalt concrete falls as the binder-aggregate ratio increases, and the change in aggregate volume ratio is the most important element in determining its dielectric constant. For every 1% increase in the binder-aggregate ratio, the dielectric constant of asphalt concrete typically decreases by 0.094, while the volume ratio of asphalt binder increases by 1.39%. In addition, the volume ratios of aggregate and air decreased by 1.05% and 0.33%, respectively. When it comes to estimating the dielectric constant of asphalt concrete, the developed model outperforms the conventional dielectric model with an average relative error of roughly 1%. This model successfully improved the prediction accuracy of the dielectric properties of asphalt concrete materials, which is significant for GPR-based asphalt pavement quality assessment. [ABSTRACT FROM AUTHOR]

In this paper, some of the basic information on Ground-Penetrating Radar (GPR), its applications (especially in the field of civil engineering) and limitations are presented. As a non-destructive technique, GPR is a powerful tool for the investigation of structures and structural members, roads, geological layers, archaeological sites and many more. The technology is based on electromagnetic radiation in the UHF/VHF range (10 MHz to 3 GHz). The choice of the frequency depends on the intended use, depth and size of the target and medium where the target is located. Joined with other testing methods (ultrasound method, dynamic methods with forced or ambient vibrations, electrical conductivity testing, etc.), GPR can provide a deep insight into the investigated object. However, like many other non-destructive methods, the choice of input parameters may affect the results. In this regard, a case study presented in this paper demonstrates not only different applications of GPR in civil engineering but also the determination (calibration) of one of those input parameters: the dielectric constant of glued laminated timber. The challenge here was not only to investigate the influence of the direction of measurements with regards to the direction of the fibers but also to acknowledge the contribution of the test antenna used during testing and dielectric constant calibration. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) has been widely used for subsurface detection and testing. Numerical simulations of GPR signal are commonly performed to aid the interpretation of subsurface structures and targets in complex environments. To enhance the accuracy of GPR simulations on heterogeneous medium, this paper proposes a hybrid modeling method that combines the discrete element method with a component fusion strategy (DEM–CFS). Taking the asphalt pavement as an example, three 3D stochastic models with distinctly different porosities are constructed by the DEM–CFS method. Firstly, the DEM is utilized to establish the spatial distribution of random coarse aggregates. Then, the component fusion strategy is employed to integrate other components into the coarse aggregate skeleton. Finally, the GPR response of the constructed asphalt models is simulated using the finite-difference time-domain method. The proposed modeling method is validated through both numerical and laboratory experiments and demonstrates high precision. The results indicate that the proposed modeling method has high accuracy in predicting the dielectric constant of heterogeneous media, as generated models are closely aligned with real-world conditions. [ABSTRACT FROM AUTHOR]

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

This study aims to develop a damage-detection algorithm based on the electromagnetic wave properties inside a reinforced concrete structure. The proposed method involves employing two algorithms based on data measured using ground-penetrating radar—a common electromagnetic wave method in civil engineering. The possible defect area was identified based on the energy dissipated by the damage in the frequency-wavenumber domain, with the damage localized using the calculated relative permittivity of the measurements. The proposed method was verified through a finite difference time-domain-based numerical analysis and a testing slab with artificial damage. As a result of verification, the proposed method quickly identified the presence of damage inside the concrete, especially for honeycomb-like defects located at the top of the rebar. This study has practical significance in scanning structures over a large area more quickly than other non-destructive testing methods, such as ultrasonic methods. [ABSTRACT FROM AUTHOR]

Bright basal reflections from the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) have been proposed to be consistent with permittivities characteristic of a wet material beneath the south polar layered deposits (SPLD). The characterization of a recently formed impact crater highlight the existence of a several meters thick ice‐poor layer associated to a unit blanketing a large portion of the SPLD. We revise the radar propagation model used to invert the basal permittivity by including a surficial thin layer. We find that the inverted basal permittivity is highly sensitive to the properties of such a layer, with solutions ranging from common dry rocks to an unambiguously wet base. We advocate toward a better characterization of the surficial cover to assess the wet or dry nature for the base, and possibly reconcile most of the literature on the topic. Plain Language Summary: A localized bright radar reflection has been detected from the base of the Southern Martian polar cap. This reflection has been attributed to salty water infiltrating the material present beneath the ice. However, this result is not yet reconciled with other radar analyses and a debate has emerged on how liquid brine could be sustained at Martian conditions. Recently a 5‐m thick layer of dust blanketing the surface of the ice cap has been detected from a recent crater excavation. This layer would act like a thin coating material that alters the apparent property of what is seen through a coated glass. At radar wavelengths, it can significantly modify the basal composition inferred from radar echoes. The bulk property of this layer is still unknown and a better characterization is necessary to inform the debate over a wet or dry base below the ice cap. Key Points: We re‐assess the radar inversion of the south polar layered deposits (SPLD) basal permittivity by incorporating the recently characterized dust layer mantling the iceThe inverted basal permittivity is highly sensitive to the property of the surficial dust layerBetter characterization of the dust layer is necessary in discriminating the nature of the SPLD bright basal reflector [ABSTRACT FROM AUTHOR]

The permittivity of soil-rock composite media is crucial for interpreting data originating from geophysical surveys such as ground-penetrating radar (GPR) imaging. At present, relevant theoretical research on the correlation analysis between the electrical and physical parameters of soil-rock composite media has not yet resulted in a complete reference system. This makes it difficult to characterize soil-rock composite media using the electromagnetic characteristic parameters of rock and soil media. In this study, the indoor electromagnetic testing method was used to extract electrical parameters such as the relative permittivity of soil-rock composite, and the influence of water content, density, soil-rock ratio, and other factors on the electrical parameters of soil-rock composite medium was analyzed. Furthermore, an artificial neural network prediction model was established, and the accuracy of the model was evaluated. The results showed that the relative permittivity tends to decrease with an increase in saturation when the moisture content of the mixture is high. The rock has a lower permittivity value than the surrounding soil particles. When the rock content increases, the average permittivity and conductive permittivity of the composite medium decreases. The main factors affecting the electrical properties of soil-rock composite media (in order of their influence, from most to least) are water content, compaction degree, and soil-rock ratio. These results provide a reliable reference for field geophysical exploration. [ABSTRACT FROM AUTHOR]

Detecting and determining the development of the frozen wall is a vital component of artificial freezing engineering. The electromagnetic parameters of frozen soil play a critical role in the fast and accurate identification using high-frequency electromagnetic waves. This study investigated the impacts of various factors on the dielectric properties of artificial frozen soil through laboratory experiments. The dielectric properties and evolutionary characteristics of sand were measured under different conditions, including temperature, probing frequency, initial moisture content, and salt content. The experimental results obtained characteristic curves of the relative dielectric constant of frozen sand in the frequency range of 300–2000 MHz under various conditions. Quantitative analysis was conducted to examine the differences in the dielectric properties of frozen sand. The analysis indicated that, under low-temperature and salt-free conditions, a reflection coefficient above 0.464 can be utilized to identify frozen/unfrozen sand. Furthermore, ground penetrating radar (GPR) is capable of effectively detecting and differentiating between salt-containing unfrozen soil and frozen soil. This study provides a solid theoretical framework for the quantitative identification of freezing front and frozen wall development. Additionally, the research findings have substantial theoretical implications for defect detection in frozen walls and the quantitative inversion of temperature fields in frozen soil. [ABSTRACT FROM AUTHOR]

Determining ground‐penetrating radar (GPR) velocity has always been a critical problem. The GPR velocity estimation method based on common midpoint (CMP) data has been widely used because of its simplicity. However, we found that in sediment investigation and soil assessment, transversal heterogeneity is universal, which violates the basic assumption of velocity estimation through CMP data. Due to the rapid change of underground media and the limitation of the scope of some surveying areas, the CMP survey line will inevitably be selected in the area where the velocity changes laterally, making it difficult to obtain accurate velocity. To address this problem, we propose a velocity correction method. First, we determined the characteristics of CMP data and the corresponding velocity spectra acquired in transversely heterogeneous media through numerical simulations. Subsequently, we found that the simulated CMP data could determine the location of changes in the underground medium, and that the velocity obtained from the semblance analysis could be corrected according to the location where the medium changes laterally. We then used models wherein the thickness, relative permittivity and proportion of abnormal parts varied independently or simultaneously to verify the proposed velocity correction method. The results show that our method can control the GPR velocity error within 3.44%, and the precision is about 0.002 m/ns. Finally, we conducted a sediment investigation experiment on a channel bar in the lower reaches of the Yarlung Zangbo River. We determined the interface at which the sediment changed transversely and obtained the corresponding electromagnetic velocity using the proposed method. This study provides a reliable method for determining the GPR velocity in transversal heterogeneous media, which is of great significance for various practical applications. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a commonly used tool for railway trackbed inspection due to its ability to collect information about subsurface materials at high resolution and high speed. Although GPR recording systems allow for the collection of vast quantities of data (hundreds of kilometres per day), accurate ground truth information is difficult to obtain. Models of trackbed can be used to generate synthetic radargrams to provide a better understanding and predictability of GPR responses to a wide range of trackbed conditions. In this research, we produced models of ballast using randomly shaped 3D particles, with a range of particle size distributions to represent various stages of ballast breakdown. Additionally, void spaces are partially filled with a constant dielectric material to represent ballast contamination. We used gprMax to simulate the GPR response for a 2 GHz horn antenna over the trackbed models. These simulations resulted in radargrams that are visually indistinct from real recorded data in known conditions. These radargrams, along with their formative models, have provided valuable insights into how variations in trackbed conditions can impact GPR data. [ABSTRACT FROM AUTHOR]

Over the past few years, remote sensing, radar, and imaging applications have all utilized Ultra-Wideband (UWB) technology. This work provides a parametric study on monopole antennas for Ground Penetrating Radar (GPR) systems. The intended antenna has an incomplete ground plane and two circular radiating patches, known for their compact design, notable high gain, and high efficiency. Furthermore, it provides an advantageous bandwidth. The suggested antenna design show-cases its appropriateness for lower frequencies, attributed to its compact dimensions. The proposed design for antenna characteristics in ground-penetrating radar systems emphasizes a requirement for large bandwidth and high gain to achieve optimal image resolution. This article delineates the design methodology and simulation of a monopole antenna developed specifically for applications in Ground Penetrating Radar (GPR). In this design, we utilize the Roger RT Duroid 5880 substrate, characterized by a relative permittivity of 2.2, a height (h) of 1.575 mm, and a loss tangent of 0.0009. The dimensions of the substrate are miniaturized to 42 mm × 40 mm × 1.575 mm. The antenna results are obtained using CST computer simulation technology software to derive the results for this antenna, and the simulations yielded favorable outcomes. The proposed array antenna demonstrates commendable characteristics at an operating frequency of 5.6 GHz, with a reflection coefficient (S11) of – 46 dB, a bandwidth of 1.6 GHz, a gain of 8.302 dB, VSWR of 1.009, and an efficiency of 99.9 %. The obtained results are highly competitive, suggesting that the proposed antenna is well-positioned to fulfill the requirements associated with Ground Penetrating Radar (GPR) applications. [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 condition of the wood structure and its components should be carefully recorded before considering any action. The diagnosis of wood structures in heritage should precede any intervention. It is important to know preliminarily the construction and structural system, the decay condition and the causes. Furthermore, damage or structural failure should be considered. The diagnosis must be based principally on measurements of physical parameters using non-destructive testing, and if necessary on laboratory testing. Water is one of the principal causes of wood decay. In this study, ground-penetrating radar and time domain reflectometry were used to estimate the dielectric permittivity and successively the volumetric water content of several types of wood. An empirical relationship was found between the dielectric constant and volumetric water content. Results were applied to two case studies: the Cathedral of Foggia and the Castle of Carosino. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a non-destructive testing technique used to assess various civil structures, including pavements. It may be applied to predict asphalt concrete (AC) layer thicknesses and dry densities. Because moisture may exist in in-service AC pavement layers and hinder the density prediction, quantifying moisture content in AC would improve its layer-density prediction. In addition, quantifying moisture content of cold recycled pavements would allow monitoring of the curing process of the treatment. Hence, the proper time for opening roads to traffic and/or placing an overlay could be identified. In this study, data were collected from both field cold-recycling projects and laboratory test slabs. The combined dataset was used to correlate measured moisture content to the dielectric constant of AC mixes. The Al-Qadi–Cao–Abufares (ACA) model was derived based on the electromagnetic mixing theory. This model is a modification to the Al-Qadi–Lahouar–Leng (ALL) model; it incorporates the effect of moisture on the bulk dielectric constant to predict density of non-dry AC. The model predicts AC density with an average error of 2% and also predicts moisture content with a root mean square error of 0.5%. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) has been widely used in estimating the permittivity of mediums. The radar echo amplitude method is an important method used by GPR in this estimation, the basic step of which is to deduce the magnitude of the permittivity according to the relationship between the reflection coefficient and the permittivity. Based on the basic principle of the radar echo amplitude method, this paper proposes a full-wave inversion real-time permittivity estimation method that can be used for stepped-frequency GPR (SFGPR), which offers high efficiency, accuracy, and generalization ability. The characteristics of this method are mainly reflected in the following four aspects: Using the SFGPR system and introducing a layered media detection model, we can complete waveform compensation optimization with high precision. The distance between the antenna and the surface of the reflective medium is extracted from the time domain waveform without manual measurement, avoiding human measurement errors. The inversion of the total reflection waveform at the required height works under the principle of an electromagnetic field, eliminating the need for repeated metal plate calibration experiments and improving work efficiency and waveform accuracy. In a continuous measurement line, the total reflection waveform inversion on each measurement point can be efficiently completed, and the change of permittivity on the measurement line can be obtained. To evaluate the feasibility of the proposed method, we performed experiments on a wall of known thickness, and the permittivity estimation was basically consistent with that of the dielectric probe, physical model calculation, and wall penetration. We also successfully applied this method to the dielectric property analysis of adobe samples. The results indicate that the proposed method can help grasp the condition of a measured medium, which can ensure the accuracy of detection and improve subsequent data processing efficiency. [ABSTRACT FROM AUTHOR]

Contrasts in electromagnetic properties between the target feature and surrounding soil are of importance for detection of archaeological features with Ground Penetrating Radar. These vary because of changing climatic conditions and soil type and are currently poorly understood. Long‐term in situ monitoring of apparent relative dielectric permittivity, bulk electrical conductivity and soil temperature over two archaeological ditch features on sites with different soil types (one clay and one free draining) was employed to understand the detection dynamics and processes by which these properties change over time. Results were correlated with geotechnical properties of the soil for both archaeological ditchfills and the surrounding natural soil matrix and previously derived laboratory relationships between water content, temperature and geophysical properties to find the timing and reasons for the optimum geophysical contrasts. Monitoring included two distinct, relatively stable periods: one wet and one dry. In contrast to previous perception that there are significant differences in infiltration between the ditch and surrounding natural soil, time‐lagged correlation analysis showed no significant differences in infiltration speed. The key differences between archaeological and natural soils were the amount of water held in a saturated state, the rates at which the different soils dried and the temperature. Thus, the optimum time for surveys was after a sustained period of several days of hot (>15°C) weather, which accentuates both water content and temperature contrasts. However, on freely draining sites that had a greater difference in the soil texture and therefore water holding capacity between the archaeological and natural soils, the timing is less critical. [ABSTRACT FROM AUTHOR]

Ground‐penetrating radar full‐waveform inversion is a high‐resolution method for inverting permittivity and conductivity; however, the issue of multi‐parameter crosstalk during the inversion process, in which perturbations of permittivity and conductivity can produce nearly identical observed data, poses a challenge. Additionally, full‐waveform inversion is highly nonlinear and computationally expensive. In this study, we conducted a sensitivity analysis based on permittivity and conductivity perturbations, which demonstrates that their sensitivities vary with frequency. Specifically, conductivity perturbation has a larger impact on low‐frequency data, whereas permittivity perturbation increasingly affects high‐frequency data. Based on the sensitivity analysis, we propose a modified stepped inversion strategy to mitigate multi‐parameter crosstalk. We also employed wavefield reconstruction inversion, which relaxes the wave‐equation constraint as a penalty term and, thus, can help us avoid local minima of the objective function. In contrast, full‐waveform inversion is more prone to get stuck owing to mismatches between modelled and measured data during the inversion process. Finally, we tested the proposed approach on crosshole synthetic data, which achieved significant computational savings and higher inversion efficiency for fewer forward simulations. Our results demonstrate that the proposed approach is a promising method for inverting subsurface structures and has the potential for practical applications in the future. [ABSTRACT FROM AUTHOR]

Depth‐based and radar‐based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in‐situ measurements or density models, to calculate SWE. However, in‐situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near‐coincident, lidar‐measured snow depths with ground‐penetrating radar (GPR) two‐way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in‐situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods. [ABSTRACT FROM AUTHOR]

• The first geophysical investigations on a Pomor site on Svalbard were made. • Weak anomalies related to small contrast in dielectric constant of drifts and soil. • Near-surface stratigraphy studied based on reflectors down to ca. 2 m depth. • Below the thaw depth (ca. 2 m) no reflectors are present. • An approach to tackle climate change effects on cultural heritage sites in the Arctic. With climate warming, the cultural heritage sites of the Arctic are in great danger. Extensive research is needed to study such sites. The archaeological site at Russekeila – Kapp Linné, Svalbard was selected for the survey as previous research had highlighted its vulnerability to cryospheric hazards. The main objectives of the survey were (i) to register the precise surface and subsurface locations of cultural heritage (CH) (remains of an 18th century Russian Pomor trapper's hut) objects within the study area, (ii) to determine the impact of coastal erosion on the CH objects and (iii) to understand the near-surface stratigraphy of the site. The geophysical surveys were carried out using a Ground Penetrating Radar (GPR) instrument with two shielded antennas of 500 MHz and 800 MHz centre frequencies. Only weak anomalies were observed at the intersections with wooden drifts, which can be explained by the low contrast between the relative dielectric constant values of the driftwood and the background soil. The depth extent of the driftwood within the soil was understood from the processed GPR data to a depth of approximately 25 cm. A near-surface stratigraphy of the site morphology, including thaw depth, saturated and unsaturated sediments and soil cover, was established based on multiple reflectors observed to 2 m depth. Loose sediments are indicated by reflectors to a depth of approximately 20 cm. Unsaturated fine sediments, which show a stronger signal compared to the underlying saturated sand layers, can be observed from about 1.2 m depth. No reflectors are shown below the thaw depth. [ABSTRACT FROM AUTHOR]

Many Iraqi provinces had collective cemeteries, especially in the middle and southern regions of Iraq, but many of those cemetery locations are undefined yet. Ground penetration radar has two features that make it optimal from a geophysical perspective for shallowly detecting sensitive materials near the surface. First, the instantaneous image is formed upon scanning, called a radargram. Second, the nondestructive inference of the scanned materials. For these two reasons, this technique was chosen to conduct a simulation process to reveal the old human remains in Iraq's central and southern areas using another model with the same physical feature (old burial) at the AL-Khamisiya site, Thi-Qar province. The demanded stages for completing the simulation process can be divided into two parts. The first part is that before the model's burial, a field survey was conducted to identify the site subsurface features and test the penetration extent of the selected antennas, in addition, to accurately calculating the speed of radar phase waves to obtain a calibrated measurement of the target's depth of burial. After the model's burying (the remains of old Iraqi humans), the second part included individual scanning and grouping at a depth of two m and four m. This simulation proved that the variation value in the dielectric constant between the burial medium (host medium) and the buried body (target) is the most critical determinant of detecting the remains' success or failure. Also, the work proved that low frequencies between (100-250) MHz are optimal for detecting human remains in a saline environment. [ABSTRACT FROM AUTHOR]

This paper presents a detection method of DCAM-YOLOv5 for ground penetrating radar (GPR) to address the difficulty of identifying complex and multi-type defects in tunnel linings. The diversity of tunnel-lining defects and the multiple reflections and scattering caused by water-hearing defects make GPR images quite complex. Although existing methods can identify the position of underground defects from B-scans, their classification accuracy is not high. The DCAM-YOLOv5 adopts YOLOv5 as the baseline model and integrates deformahle convolution and convolutional block attention module (CBAM) without adding a large number of parameters to improve the adaptive learning ability for irregular geometric shapes and boundary fuzzy defects. In this study, dielectric constant models of tunnel linings are established based on the electromagnetic simulation software (GPRMAX), including rebar and various structural defects. The simulated and field GPR B-scan images show that the DCAM-YOLOv5 method has better results for detecting different types of defects than other methods, which validates the effectiveness of the proposed detection method. [ABSTRACT FROM AUTHOR]

Reinforced concrete (RC) parts, which are exposed to high temperatures for a certain period, undergo physical-chemical changes that deteriorate their mechanical properties. To this end, this study investigates the efficiency of using ground-penetrating radar (GPR) as a non-destructive testing (NDT) method in assessing the core compressive strength loss, elastic modulus decrease, and tensile strength loss of concrete under extreme temperatures. RC specimens are fabricated and exposed to elevated temperatures (300°C, 400°C, 500°C, 600°C, and 700°C). GPR measurements are performed to estimate the relative dielectric permittivity (RDP) of the specimens before fire exposure (BFE) and after fire exposure (AFE). The compressive strengths of the cores extracted from the specimens are obtained also for BFE and AFE cases. Attained RDP values are in conformance with the previous research results and contribute additional evidence to the published literature. As the temperature levels are increased, RDP and strength-related values of concrete specimens decrease. The strength-related parameters are more sensitive to elevated temperatures, whereas the change in the RDP values is rather gradual. Through multiple correlations within 95% confidence intervals, core compressive strength loss, elastic modulus decrease, and tensile strength loss are, individually, estimated as a function of relative RDP change and applied temperature levels. Empirical formulas are obtained with high coefficients of correlation. The relative effects of RDP change and temperature are more pronounced in elastic modulus decrease and tensile strength loss compared to compressive strength loss. Overall, the outcomes of this paper confirm the viability of using the GPR technique for concrete dielectric characterisation in estimating the strength-related properties of concrete exposed to high temperatures, which might reduce the need for destructive testing. [ABSTRACT FROM AUTHOR]

Root diameter is an important indicator of plant growth and development to a large extent. However, the field monitoring of these parameters is severely limited by the lack of appropriate methods, and some traditional methods may harm the plant and its growing environment. Ground-penetrating radar (GPR) is a new nondestructive detection method for underground root systems. A new method for the estimation of the diameter of coarse roots using GPR with 900 MHz frequency was proposed in this paper. First, a simulation model was established to simulate the root system under natural conditions, and the root diameter estimation model based on the scanning results of GPR was obtained. Secondly, by studying the influence of soil and root relative permittivity on the diameter estimation model, a method was found to devise a coarse root diameter estimation model under different soil and root conditions of relative permittivity. Thirdly, the applicability of the diameter estimation model to roots with different growth orientations was tested by simulating roots with different growth orientations. Finally, the practical applicability of the estimation method was verified by field experiments. The results suggest that the root diameter estimation model can be constructed by extracting the pixel distance (∆p) of waveform parameters from the 900 MHz scanning results. This method can be used to estimate the diameter of coarse roots with diameters of no less than 2 cm and a relative permittivity greater than 5, and to estimate the diameter of roots in any orientation and soil environment effectively. At the same time, the application in the field experiment also resulted in a good estimation effect. This method provides a new opportunity to achieve more reliable root diameter estimation in complex situations. The estimation of coarse root diameter provides an experimental basis and data support for the healthy growth of trees, and also provides some information for the study of coarse root ecology. [ABSTRACT FROM AUTHOR]

Abstract The mapping of available water–ice is a crucial step in the lunar exploration missions. Ground penetrating radars have the potential to map the subsurface structure and the existence of water–ice in terms of the electromagnetic properties, specifically, the permittivity. Slight differences in permittivity can be significantly important when applied in a dry environment, such as on the Moon and Mars. The capability of detecting a small fraction of putative water–ice depends on the permittivity changes in terms of its dependent parameters, such as the frequency, the temperature, the porosity, and the chemical composition. Our work aims at mitigating false detection or overlooking of water–ce by considering their conditions that previous researches did not cover. We measured the permittivity of different lunar regolith relevant analogue samples with a fixed 40 % porosity in the ultra-high-frequency–super-high-frequency band. We used the coaxial probe method to measure anorthosite, basalt, dunite and ilmenite at $$20\,^\circ \hbox {C}$$ 20 ∘ C , $$-20\,^\circ \hbox {C}$$ - 20 ∘ C and $$-60\,^\circ \hbox {C}$$ - 60 ∘ C , and we find that, at $$-60\,^\circ \hbox {C}$$ - 60 ∘ C , the permittivity decreases about 6–18 % compared with the values at $$20\,^\circ \hbox {C}$$ 20 ∘ C . Within this temperature range, the permittivity is quite similar to the permittivity of water–ice. We find that the conventional calculation would overestimate the permittivity in the low temperature areas, such as the permanently shadowed regions. We also find that each component in the lunar regolith has different temperature-dependent permittivity, which might be important for radar data analysis to detect lunar polar water–ice. Our results also suggest that it should be possible to estimate the water–ice content from radar measurements at different temperatures given an appropriate method. Graphical Abstract

The main objective of this study is to simultaneously evaluate the surface and existing layers of composite pavement using PMS data and GPR equipment. The distribution of dielectric constants according to the existing concrete conditions and the relationship between dielectric constants and surface distress was evaluated. As a result, the dielectric constant distribution of the existing concrete showed a significant difference depending on the AAR. In addition, the correlation between surface distress and the dielectric constants of the existing layers was low. [ABSTRACT FROM AUTHOR]

The Mars 2020 Perseverance Rover is equipped with the RIMFAX ground penetrating radar instrument which has been continuously surveying the shallow subsurface during the rover's journey. Within the first 379 mission days, we identify hyperbolic patterns in the data, which are thought to be caused by objects such as buried boulders or cavities located in the upper 5 m of the subsurface. To obtain the first detailed estimates of radar wave propagation velocity, we match these scatterer‐generated patterns with theoretical traveltime hyperbolas, which take into account the refraction at the surface. We employ these estimates to derive the average dielectric permittivity and bulk rock density of the volume above the scatterer source. The parameters compare well with those obtained through orbital radar measurements and those measured by other instruments onboard the Perseverance Rover. Our findings are consistent with a subsurface dominated by solid rock and mafic material. Plain Language Summary: The Mars 2020 Perseverance Rover carries the RIMFAX instrument, the first ground‐penetrating radar on a NASA Mars rover. It continuously transmits radar waves into the subsurface while the Rover moves and records the reflected echoes to create images of the subsurface and determine material properties. Here, we use arch‐shaped patterns in the data to find out about the dielectric permittivity and rock density. Both properties match well with estimates from other instruments onboard the Perseverance Rover and from Mars orbiters. Our findings indicate a solid‐rock subsurface composed of volcanic material. Key Points: First detailed dielectric permittivity and bulk rock density measurements of the shallow Martian subsurface with ground penetrating radar are presentedPermittivities and densities are consistent with a solid‐rock, mafic subsurfacePermittivities are in the range of Mars orbital radar results; densities compare with surficial densities from other Rover instruments [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel framework to reconstruct image of a buried object from the ground-penetrating radar (GPR) reflection hyperbolas. The first reflection hyperbola of a buried object determines its relative permittivity and shape class (circle, rectangle, and triangle). The first two reflections are used to reconstruct image of a homogeneous buried object. After five preprocessing steps to extract region of interest of b-scan corresponding to a buried object, a Convolutional Neural Network (CNN) estimates the relative permittivity value of the buried object. Then, two novel schemes, including CNN and Dictionary Learning (DL) methods are proposed to reconstruct 2D image of the buried object. Image reconstructors are trained with a dataset containing buried objects with different shapes, sizes, and reference relative permittivity. Since two objects having the same size but different materials lead to different hyperbola reflections in their B-scans, we propose a novel transformation to convert the reflection scans from the estimated relative permittivity to a reference relative permittivity so that the image reconstructors can be used to rebuild images. The results reveal that the DL method outperforms the CNN method on noisy measurements, while the CNN method performs better when dealing with fewer traces. [ABSTRACT FROM AUTHOR]

Investigations into the dielectric properties of concrete form the basis for concrete quality testing and condition monitoring of internal structures when using ground penetrating radar (GPR). The formula used to describe the quantitative relationship between the dielectric properties of composite materials and the dielectric constant and volumetric ratio of each component is known as a dielectric model. In this study, a new dielectric model was deduced from circuit theory on the basis of structural abstraction to calculate the dielectric permittivities of concrete. To validate the applicability and accuracy of the proposed model, because concrete consists of matrix (cement mortar), aggregates (limestone), and air, the dielectric traits of concrete and its components were acquired using an open-ended coaxial probe. Compared with the Brown model, complex refractive index method (CRIM) model, Looyenga model, and Rayleigh model (i.e., the classical models), the results showed that among overall predictions of concrete dielectric constants, the proposed model has the greatest accuracy, because the theoretical results matched the test results more closely. This indicates that the proposed model can better interpret the dielectric properties of concrete, which lays the groundwork for future research into the application of GPR to the nondestructive testing and evaluation of concrete pavement. [ABSTRACT FROM AUTHOR]

The measurement of soil moisture is important for a wide range of applications, including ecosystem conservation and agricultural management. However, most traditional measurement methods, for example, time‐domain reflectometry (TDR), are unsuitable for mapping field scale variability. In this study, we propose a method that uses an unmanned aerial vehicle (UAV) to support a ground penetrating radar (GPR) system for spatial scanning investigation at different elevations above ground level. This method measures the surface reflectivity to estimate the soil moisture, exploiting the linear relationship between the ratio of the reflected and the direct wave amplitudes along with the reciprocal of GPR antenna height. This relationship is deduced in this study based on the point source assumptions of a transmitter antenna and ground reflections, which is confirmed by numerical simulation results using the gprMax software. Unlike previous air‐launched GPR methods, the UAV‐GPR method presented here removes the limitations of a steady transmitter power and a fixed GPR survey height and the need for calibration of antenna transfer functions and geophysical inversion calculations, and thus is simpler and more convenient for field applications. We test the method at field sites within the riparian zone and a river‐island grassland adjacent to the Yangtze River. The results from the field study illustrate comparable measured soil moisture to those obtained invasively using TDR. The root mean square error of surface reflectivity and soil moisture values between UAV‐GPR with eight antenna height investigations and TDR in the grassland are 0.03 and 0.05 cm3/cm3, respectively. Key Points: A multi‐elevation unmanned aerial vehicle‐ground penetrating radar (UAV‐GPR) system is proposed to measure soil moisture at the field (and larger) scaleA linear relationship between the ratio of the reflected and the direct wave amplitudes along with the reciprocal of GPR height is deducedThe UAV‐GPR method offers a higher resolution than remote sensing method and better working efficiency than conventional in situ methods [ABSTRACT FROM AUTHOR]

The mapping of available water–ice is a crucial step in the lunar exploration missions. Ground penetrating radars have the potential to map the subsurface structure and the existence of water–ice in terms of the electromagnetic properties, specifically, the permittivity. Slight differences in permittivity can be significantly important when applied in a dry environment, such as on the Moon and Mars. The capability of detecting a small fraction of putative water–ice depends on the permittivity changes in terms of its dependent parameters, such as the frequency, the temperature, the porosity, and the chemical composition. Our work aims at mitigating false detection or overlooking of water–ce by considering their conditions that previous researches did not cover. We measured the permittivity of different lunar regolith relevant analogue samples with a fixed 40 % porosity in the ultra-high-frequency–super-high-frequency band. We used the coaxial probe method to measure anorthosite, basalt, dunite and ilmenite at 20 ∘ C , - 20 ∘ C and - 60 ∘ C , and we find that, at - 60 ∘ C , the permittivity decreases about 6–18 % compared with the values at 20 ∘ C . Within this temperature range, the permittivity is quite similar to the permittivity of water–ice. We find that the conventional calculation would overestimate the permittivity in the low temperature areas, such as the permanently shadowed regions. We also find that each component in the lunar regolith has different temperature-dependent permittivity, which might be important for radar data analysis to detect lunar polar water–ice. Our results also suggest that it should be possible to estimate the water–ice content from radar measurements at different temperatures given an appropriate method. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) of ground-penetrating radar (GPR) data has received particular attention in the past decade because it can provide high-resolution subsurface models of dielectric permittivity and electrical conductivity. In most GPR FWIs, these two parameters are regarded as frequency independent, which may lead to false estimates if they strongly depend on frequency, such as in shallow weathered zones. In this study, we develop frequency-dependent GPR FWI to solve this problem. Using the τ-method introduced in the research of viscoelastic waves, we define the permittivity attenuation parameter to quantify the attenuation resulting from the complex permittivity and to modify time-domain Maxwell's equations. The new equations are self-adjoint so that we can use the same forward engine to back-propagate the adjoint sources and easily derive model gradients in GPR FWI. Frequency dependence analysis shows that permittivity attenuation acts as a low-pass filter, distorting the waveform and decaying the amplitude of the electromagnetic waves. The 2-D synthetic examples illustrate that permittivity attenuation has low sensitivity to the surface multioffset GPR data but is necessary for a good reconstruction of permittivity and conductivity models in frequency-dependent GPR FWI. As a comparison, frequency-independent GPR FWI produces more model artefacts and hardly reconstructs conductivity models dominated by permittivity attenuation. The 2-D field example shows that both FWIs reveal a triangle permittivity anomaly which proves to be a refilled trench. However, frequency-dependent GPR FWI provides a better fit to the observed data and a more robust conductivity reconstruction in a high permittivity attenuation environment. Our GPR FWI results are consistent with previous GPR and shallow-seismic measurements. This research greatly expands the application of GPR FWI in more complicated media. [ABSTRACT FROM AUTHOR]

The increasing length of subsurface pipe causes overlapping, accumulation, and occasionally the old pipe layout is not also available. Consequently, accidents, damages, time delays, and financial losses occur during construction of new structures or installation of new pipes. Therefore, depth, radius, material of the existing pipe, and map of pipe are indispensable for knowing proper construction planning. In this article, an algorithm is proposed to estimate the properties of subsurface pipes and show 3D maps. Using this algorithm, the radius of the field pipes was estimated with 83, 67, and 89 % accuracy and depth with 95, 95, and 98 % accuracy. The effect of pipe radius should be considered to assess the pipe depth with higher accuracy. The material of the field pipe was successfully determined using the evaluated relative permittivity. A 3D map of the field pipe was developed by applying the tracing algorithm and linear regression on estimated depth. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a non-destructive and non-invasive geophysical survey method that has been used to characterize soil volumetric water content (VWC) dynamics. An array antenna GPR system was used to collect nearly seamless, time-lapse multi-offset GPR data during an in-situ infiltration test on sand dunes with limited traces. Because the data volume was significant, an approach was utilized to automatically determine electromagnetic wave velocities from sparse common midpoint (CMP) data using standard velocity analysis, such as semblance analysis. The objective of this study was to develop a methodology that allows one to automatically perform velocity analysis by interpolating sparse CMP data obtained with the array GPR system. The proposed method determined the optimal normal moveout velocity values and the removal range of the F-K zone pass filter that minimized errors between the original and interpolated CMP data using leave-one-out cross-validation (LOOCV). After interpolating the sparse CMP data with the F-K zone pass filter, semblance analysis was used to determine the time-lapse velocity structure of the soil profile during water infiltration. The velocity data were converted to VWC data based on the Topp equation, which relates the soil VWC to the soil dielectric constant. The proposed method was tested using CMP data obtained via numerical simulation and experiments. The VWC profile from the proposed approach matched well with the independently observed VWC profiles obtained from an invasive probe-type soil moisture sensor. [ABSTRACT FROM AUTHOR]

Determining the material properties and existing capacity of concrete infrastructure using nondestructive techniques is challenging due to evolving design requirements, unknown as-built conditions, and ongoing maintenance and deterioration. Concrete's material properties, including density, porosity, and compressive strength, are usually determined by mechanical testing, but being able to measure these properties noninvasively could aid engineers in maintaining and designing concrete infrastructure. Research into nondestructive methods for determining material properties of concrete has shown relationships between mechanical properties and ground penetrating radar (GPR) properties such as dielectric constant, attenuation, and instantaneous amplitude. We investigated direct relationships between dielectric constant and the density, porosity, and compressive strength of 23 mature concrete samples with varying mix designs using a commercial 1 GHz GPR. In normal-weight concrete, weak trends were seen between a dielectric for compressive strength ( R 2 = 0.76 ) and one for density ( R 2 = 0.64 ), whereas no significant trend was found with porosity ( R 2 = 0.52 ). The GPR unit used provides acceptable data but has limited resolution for analyses and utility. The dielectrics showed distinct clustering by mix type—particularly the inclusion of materials such as blast furnace slag. While demonstrating that the dielectric constant is a candidate for rapid concrete comparisons, there is also a demonstrated need for further investigation of the complex relationships between mechanical and electromagnetic properties in concrete. [ABSTRACT FROM AUTHOR]

The dielectric permittivity of common soils is mainly controlled by water content and porosity, while the latter is closely related to the characteristics of compaction. By studying the changes in dielectric permittivity of soil samples with different soil water content and compaction levels, the influence of the controlling factors on the relationship model between soil water content and dielectric permittivity can be evaluated. In this paper, network analyzer was used to measure the dielectric permittivity of 7 groups of soil samples with gravimetric water content ranging from 8.09% to 14.52% and dry density ranging from 1.61 g/cm3 to 1.96 g/cm3. The results show that the dielectric permittivity increases with the increase of water content and dry density, and the effect of water content on permittivity is more significant for soils with higher dry density. Furthermore, when the water content is less than or equal to the optimal water content, Topp formula and the complex refractive index model (CRIM) can better predict the soil dry density. When the water content approaches the saturated state of soil, there is a deviation between the predicted value and the actual value. At last, the modified Topp formula and the complex refractive index model (CRIM) can accurately predict soil compactness. This provides an important basis for rapid detection of water content and compactness of highway subgrade soil by ground penetrating radar. [ABSTRACT FROM AUTHOR]

Due to its high resolution and deep penetration depth, microwave ultrawideband radar is a promising tool for the non-destructive testing (NDT) of transportation infrastructure. Microwave radiation can also be used to reconstruct the dielectric properties of objects and therefore can be used to detect an air cavity or metallic rust in concrete. We used contrast source inversion (CSI) as one of the most promising inverse scattering schemes. To resolve the observation domain limitations of NDT, radar imaging method, also known as range points migration (RPM) method, was first incorporated into the inverse scattering algorithm based on CSI as a region of interest (ROI) estimator, which substantially improves the accuracy of complex permittivity reconstruction. In addition, the ROI optimizing scheme based on the CSI cost function is used to enhance ROI accuracy. The effectiveness of the proposed methods is validated via finite-difference time-domain (FDTD)-based numerical simulation, which assumes typical NDT model. [ABSTRACT FROM AUTHOR]

This paper presents the numerical studies of the bowtie antenna with and without metasurface in cross-coupled arrays. The single bowtie antennas coated by the metasurface are designed at the neighboring frequency of 3 GHz. The cloaked frequency of the cloaked bowtie antennas is controlled by components that consist of metasurface such as the dielectric constant of the supporting material, slot width, spacing of the slot, and the height of the material. The cloaked bowtie antenna has characteristics such as the minimum reflection from the antenna at the designed frequency and, at the same time the radar cross section has a low value at the cloaked frequency. This characteristic has been intensively explored with closely cross-coupled antennas in array configurations as 1D lateral arrangement and 2D arrangement for the sequential and simultaneous modes. It is numerically confirmed that the characteristics of the cloaked coupled arrays are kept the same as those of a standalone array, which is shown to have robust characteristics to the mutual coupling between strongly coupled elements. [ABSTRACT FROM AUTHOR]

In order to improve the prediction accuracy regarding low compaction level of asphalt pavement, this paper carries out indoor tests to detect the voids and dielectric constants of AC-13, AC-16 and AC-25 asphalt mixtures, obtaining their relationship equations via linear fitting and determining the dielectric constant judgment threshold of low compaction level segregation risk points ε 1 . Based on the common mid-point method, three-dimensional ground-penetrating radar is used to obtain the dielectric constant of the physical engineering test section. The researcher can draw the distribution map of the low compaction level segregation risk area according to the judgment threshold ε 1 of the rough segregation risk points; divide the connected risk areas; determine the regional convex hull; and calculate the regional indicators such as the regional area, the ratio of the convex risk points and the mean value of the regional dielectric constant. The response surface analysis method is used to acquire the model of risk area index and core void ratio. The model is employed to predict and verify the core void ratio in the risk area of the road section and verify the accuracy of the model. The results show that the error range between the predicted voids and the measured voids is −0.4%~+0.4%, and the mean absolute value of the error is 0.25%. Compared with the mean measured voids of 6.63%, the relative error is 3.77%, indicating that the model can accurately predict the regional low compaction level segregation degree. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) ground-penetrating radar (GPR) is an important research topic for target detection in many fields. In this paper, we develop a UAV-mounted GPR system with a frequency band at 150 MHz–309 MHz. However, the received signal in the complex background is covered by various clutter and interference, leading to the serious obscuring of the target. To meet this challenge, a cross-correlation-based background subtraction (CCBS) method and an interference suppression technique are adopted in combination for more accurate detection. The CCBS method processes the raw echo by establishing a background-removal model and using the similarity between each A−Scan and a reference wave. In addition, a Butterworth filter is adopted to get rid of the active electromagnetic interference beyond the working frequencies of the system; then, a lateral Doppler filtering (LDF) technique is introduced to suppress the passive interference generated by the rotation of the UAV rotor itself. Moreover, a practical method for estimating the dielectric constant is introduced by the calibration process of the measured radar echo. Numerical simulations and experimental results by our UAV-GPR system demonstrate that the proposed method has presented a better performance than the traditional methods, and the system has great potential in detecting deeply buried targets. [ABSTRACT FROM AUTHOR]

The geophysical quality Q involved in anelastic energy absorption plays a significant role in the study of subsurface pressure, rock types and temperature distributions. However, with respect to newly collected lunar penetrating radar (LPR) data, scholars mainly focused on related parameters such as the permittivity, density and chemical composition, while the Q-value has rarely been estimated. In this study, we improved the multi-channel quality factor Q estimation method to study shallow underground structures of the Moon, interpret the geological structure and provide reference for the exploration of lunar resources. The improved windowed multi-channel Q estimation method uses multiple spatial coherence-constrained inversion to achieve a stable Q estimation of the whole direction profile in the presence of noise. An example of a numerical simulation of the simple lunar geological model illustrates the feasibility of the proposed method in estimating the Q-value. Partial lunar data collected by Chang'e-4 LPR were used to demonstrate its value with respect to the interpretation of the stratigraphic development history, dislocation and relative permittivity of the Moon. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Study on the impact of physical characteristics of soil-rock composite medium on its relative permittivity based on laboratory experiments." The corrigendum addresses errors in the affiliation, conflict of interest statement, and funding statement of the published article. The authors apologize for these errors and state that they do not affect the scientific conclusions of the article. The corrigendum also includes a note from the publisher stating that the claims expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. [Extracted from the article]

Correctly estimating the relative permittivity of buried targets is crucial for accurately determining the target type, geometric size, and reconstruction of shallow surface geological structures. In order to effectively identify the dielectric properties of buried targets, on the basis of extracting the feature information of B-SCAN images, we propose an inversion method based on a deep neural network (DNN) to estimate the relative permittivity of targets. We first take the physical mechanism of ground-penetrating radar (GPR), working in the reflection measurement mode as the constrain condition, and then design a convolutional neural network (CNN) to extract the feature hyperbola of the underground target, which is used to calculate the buried depth of the target and the relative permittivity of the background medium. We further build a regression network and train the network model with the labeled sample set to estimate the relative permittivity of the target. Tests were carried out on the GPR simulation dataset and the field dataset of underground rainwater pipelines, respectively. The results show that the inversion method has high accuracy in estimating the relative permittivity of the target. [ABSTRACT FROM AUTHOR]

Traditional ground-penetrating radar (GPR) data inversion leverages iterative algorithms that suffer from high computation costs and low accuracy when applied to complex subsurface scenarios. Existing deep learning-based methods focus on the ideal homogeneous subsurface environments and ignore the interference due to clutters and noise in real-world heterogeneous environments. To address these issues, a two-stage deep neural network (DNN), called DMRF-UNet, is proposed to reconstruct the permittivity distributions of subsurface objects from GPR B-scans under heterogeneous soil conditions. In the first stage, a U-shape DNN with first multi-receptive-field convolution (MRF-UNet1) is built to remove the clutters due to inhomogeneity of the heterogeneous soil. Then, the denoised B-scan from MRF-UNet1 is combined with the noisy B-scan to be inputted to the DNN in the second multi-receptive-field convolution (MRF-UNet2). MRF-UNet2 learns the inverse mapping relationship and reconstructs the permittivity distribution of subsurface objects. To avoid information loss, an end-to-end training method combining the loss functions of two stages is introduced. A wide range of subsurface heterogeneous scenarios and B-scans are generated to evaluate the inversion performance. The test results in the numerical experiment and the real measurement show that the proposed network reconstructs the permittivities, shapes, sizes, and locations of subsurface objects with high accuracy. The comparison with existing methods demonstrates the superiority of the proposed methodology for the inversion under heterogeneous soil conditions. [ABSTRACT FROM AUTHOR]

In this study, we analyzed low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil electrical conductivity mapping. Indeed, in the lowest GPR frequency ranges, the soil surface reflexion coefficient depends more on the soil electrical conductivity than on its permittivity. Numerical experiments were conducted within the frequency range 15–45 MHz to analyze parameter sensitivities, the well-posedness of the inverse problem as well as the depth of sensitivity. The results show that the soil surface reflexion is significantly more sensitive to the soil electrical conductivity than the soil permittivity. Therefore, the conductivity can be retrieved using full-wave inversion within this frequency range, with a characterization depth varying from 0.5 to 1 m, depending on the soil properties. Yet, the permittivity also affects the results and should be accounted for in the inversion strategy. Field measurements were performed using low-frequency drone-borne radar with a 5-m half-wave dipole antenna, and electromagnetic induction (EMI) measurements with different depth sensitivities were conducted for comparison. Kriging interpolation was used to get maps from measurement points. The soil conductivity maps obtained by the proposed GPR and EMI are compliant in terms of absolute values and spatial patterns. This study demonstrated the capacity of low-frequency drone-borne GPR for fast, field-scale soil electrical conductivity mapping. [ABSTRACT FROM AUTHOR]