Search Results (964)

Previous studies on single-polarized InSAR-based sub-canopy topography inversion have mainly relied on simplified or empirical models that only consider the volume scattering process. In a boreal forest area, the canopy layer is often discontinuous. In such a case, the radar backscattering echoes are mainly dominated by ground surface and volume scattering processes. However, interferometric scattering models like Random Volume over Ground (RVoG) have been little utilized in the case of single-polarized InSAR. In this study, we propose a novel method for retrieving sub-canopy topography by combining the RVoG model with multi-baseline InSAR data. Prior to the RVoG model inversion, a SAR-based dimidiate pixel model and a coherence-based penetration depth model are introduced to quantify the initial values of the unknown parameters, thereby minimizing the reliance on external vegetation datasets. Building on this, a nonlinear least-squares algorithm is employed. Then, we estimate the scattering phase center height and subsequently derive the sub-canopy topography. Two frames of multi-baseline TanDEM-X co-registered single-look slant-range complex (CoSSC) data (resampled to 10 m × 10 m) over the Krycklan catchment in northern Sweden are used for the inversion. Validation from airborne light detection and ranging (LiDAR) data shows that the root-mean-square error (RMSE) for the two test sites is 3.82 m and 3.47 m, respectively, demonstrating a significant improvement over the InSAR phase-measured digital elevation model (DEM). Furthermore, diverse interferometric baseline geometries and different initial values are identified as key factors influencing retrieval performance. In summary, our work effectively addresses the limitations of the traditional RVoG model and provides an advanced and practical tool for sub-canopy topography mapping in forested areas. Full article

The analysis of ground-penetrating radar data generally relies on the visual identification of structures on selected profiles and their interpretation in terms of buried features. In simple cases, inverse modelling of the acquired data set can facilitate interpretation and reduce subjectivity. These methods suffer from severe restrictions due to antenna resolution limits, which prevent the identification of tiny structures, particularly in forensic, stratigraphic, and engineering applications. Here, we describe a technique to obtain a high-resolution characterization of the underground, based on the forward modelling of individual traces (A-scans) of selected radar profiles. The model traces are built by superposition of Ricker wavelets with different polarities, amplitudes, and arrival times and are used to create reflectivity diagrams that plot reflection amplitudes and polarities versus depth. A thin bed is defined as a layer of higher or lower permittivity relative to the surrounding material, such that the top and bottom reflections are subject to constructive interference, determining the formation of an anomalous peak in the trace (tuning effect). The proposed method allows the detection of ultra-thin layers, well beyond the Rayleigh vertical resolution of GPR antennas. This approach requires a preliminary estimation of the instrumental uncertainty of common monostatic antennas and takes into account the frequency-dependent attenuation, which causes a spectral shift of the dominant frequency acquired by the receiver antenna. Such a quantitative approach to analyzing radar data can be used in several applications, notably in stratigraphic, forensic, paleontological, civil engineering, heritage protection, and soil stratigraphy applications. Full article

This study presents the application of ground-penetrating radar (GPR) in the investigation of historical metal ore mining sites in the Lower Silesia region of Poland. The paper outlines the principles of the GPR method and details the measurement procedures used during fieldwork. GPR has proven to be an effective, non-invasive tool for identifying inaccessible or previously unknown underground mining structures, such as shafts, tunnels, and remnants of mining infrastructure. This capability is particularly valuable in the context of extensive and complex post-mining landscapes characteristic of Lower Silesia. The research presents findings from selected sites, demonstrating how GPR surveys facilitated the detection and subsequent archaeological exploration of historical workings. In several cases, the method enabled the recovery of access to underground features, which were then subjected to detailed documentation and preservation efforts. Following necessary safety and adaptation measures, some of these sites have been successfully opened to the public as part of regional tourism initiatives. The study confirms the utility of GPR as a key instrument in post-mining archaeology and mining heritage conservation, offering a rapid and reliable means of mapping subsurface structures without disturbing the terrain. Full article

Synthetic Aperture Radar (SAR) is highly valuable for target detection due to its all-weather, day-night operational capability and certain ground penetration potential. However, traditional SAR target detection methods often directly adapt algorithms designed for optical imagery, simplistically treating SAR data as grayscale images. This approach overlooks SAR’s unique physical nature, failing to account for key factors such as backscatter variations from different polarizations, target representation changes across resolutions, and detection threshold shifts due to clutter background heterogeneity. Consequently, these limitations lead to insufficient cross-polarization adaptability, feature masking, and degraded recognition accuracy due to clutter interference. To address these challenges, this paper systematically reviews recent research advances in SAR target detection, focusing on physical constraints including polarization characteristics, scattering mechanisms, signal-domain properties, and resolution effects. Finally, it outlines promising research directions to guide future developments in physics-aware SAR target detection. Full article

The detection of underground cavities and dissolution features is a critical component in assessing geohazards within karst terrains, particularly where natural processes interact with long-term human occupation. This study investigates two contrasting sites in the Sefrou region of northern Morocco: Binna, a rural travertine-dolomite system shaped by Quaternary karstification, and the urban Old Medina of Bhalil, where traditional cave dwellings are carved into carbonate formations. A combined geophysical and geological approach was applied to characterize subsurface heterogeneities and assess the extent of near-surface void development. Vertical electrical soundings (VES) at Binna site delineated high-resistivity anomalies consistent with air-filled cavities, dissolution conduits, and brecciated limestone horizons, all indicative of an active karst system. In the Bhalil old Medina site, ground-penetrating radar (GPR) with low-frequency antennas revealed strong reflection contrasts and localized signal attenuation zones corresponding to shallow natural cavities and potential anthropogenic excavations beneath densely constructed areas. Geological observations, including lithostratigraphic logging and structural cross-sections, provided additional constraints on cavity geometry, depth, and spatial distribution. The integrated results highlight a high degree of subsurface karstification across both sites and underscore the associated geotechnical risks for infrastructure, cultural heritage, and land-use stability. This work demonstrates the value of combining electrical and radar methods with geological analysis for mapping hazardous subsurface voids in cavity-prone Quaternary landscapes, offering essential insights for risk mitigation and sustainable urban and rural planning. Full article

After the completion of open-pit coal mining, land reclamation is implemented to restore the disturbed eco–hydrological system, for which accurate soil moisture characterization is essential. We evaluated the feasibility and performance of an Auto-Regressive Moving Average (ARMA)-based ground-penetrating radar (GPR) inversion scheme for estimating soil moisture in a reclaimed mine area. GPR data were acquired over a reconstructed three-layer soil profile in a reclaimed open-pit coal mine, and soil moisture content was independently determined using the oven-drying method on core samples. An ARMA model was used to describe the relationship between the GPR reflections and soil electromagnetic properties and to invert the vertical distribution of soil moisture. The ARMA-derived GPR estimates reproduced the measured moisture profile well within the depth interval of 1.4–3.0 m and revealed the clear vertical zonation of soil moisture associated with the engineered layering. Correlation coefficients between the ARMA-inverted GPR estimates and oven-drying measurements ranged from 0.64–0.78 for 0–1.4 m, 0.84–0.93 for 1.4–2.2 m, and 0.98–0.99 for 2.2–3.0 m, indicating that inversion accuracy improves systematically with depth. These results demonstrate that ARMA-based GPR inversion provides a reliable and non-destructive approach for quantifying soil moisture in reclaimed mine soils and offers practical support for monitoring and assessing the effectiveness of reclamation in open-pit coal mining areas. Full article

Ground settlement is readily induced by shield–tail gaps formed during tunneling, where soil loss must be compensated through backfill grouting. However, improper grouting control may trigger tunnel uplift, segment misalignment, and, after solidification, problems such as voids, cracking, and water ingress. Ensuring construction safety and long-term serviceability requires both reliable detection of grouting effectiveness and a mechanistic understanding of grout diffusion. This review systematically synthesizes sensing technologies, diffusion modeling, and intelligent data interpretation. It highlights their interdependence and identifies emerging trends toward multimodal joint inversion and real-time grouting control. Non-destructive testing techniques can be broadly categorized into geophysical approaches and sensor-based methods. For synchronous detection, vehicle-mounted GPR systems and IoT-based monitoring platforms have been explored, although studies remain sparse. Theoretically, grout diffusion has been investigated via numerical simulation and field measurement, including the spherical diffusion theory, columnar diffusion theory, and sleeve-pipe permeation grouting theory. These theories decompose the diffusion process of the slurry into independent movements. Nevertheless, oversimplified models and sparse monitoring data hinder the development of universally applicable frameworks capable of capturing diverse engineering conditions. Existing techniques are further constrained by limited imaging resolution, insufficient detection depth, and poor adaptability to complex strata. Looking ahead, future research should integrate complementary non-destructive methods with numerical simulation and intelligent data analytics to achieve accurate inversion and dynamic monitoring of the entire process, ranging from grout diffusion and consolidation to defect evolution. Such efforts are expected to advance both synchronous grouting detection theory and intelligent and digital-twin tunnel construction. Full article

In this paper, the combined use of Ground Penetrating Radar (GPR) and a Terrestrial Laser Scanner (TLS) is illustrated to highlight multiple advantages arising from the integration of these two distinct Non-Destructive Testing (NDT) techniques in the investigation of a historical wall. In particular, thanks to the TLS point cloud, a precise evaluation of the medium’s thickness, as well as its irregularities, was carried out. Based on this accurate geometrical constraint, a first-order velocity model, to be used for a time-to-depth conversion and for a post-stack GPR data migration, was computed. Moreover, a joint visualization of both datasets (GPR and TLS) was achieved in a novel tridimensional workspace. This solution provided a more straightforward and efficient way of testing the reliability of the combined results, proving the efficiency of the proposed method in the estimation of a velocity model, especially in comparison to conventional GPR methods. This demonstrates how the integration of different remote sensing methodologies can yield a more solid interpretation, taking into account the uncertainties related to the geometrical irregularities of the external wall’s surface and the inner structure generating complex GPR signatures. Full article

The detection of subsurface stromatolites remains challenging due to their complex morphology and heterogeneous composition. This study assesses the combined application of Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) for identifying microbialites in two contrasting geological and climatic settings: the Neoproterozoic Salitre Formation in Brazil and the Mesozoic microbialite-bearing limestones in northern Spain. High-resolution ERT profiles processed with raster-based blob detection algorithms revealed subcircular high-resistivity anomalies consistent with the studied microbialite morphologies, with strong resistivity contrasts observed between microbialites and host matrices despite variations in absolute values linked to lithology and soil moisture. In parallel, GPR surveys analyzed with a peak detection algorithm delineated domal reflectors and clusters of high-amplitude reflections that directly captured the internal architecture of stromatolitic buildups. With decimetric vertical resolution, GPR offered unrivaled insights into internal morphology, complementing the broader-scale imaging capacity of ERT. The complementary strengths of both methods are clear: ERT excels at mapping distribution and stratigraphic context, while GPR provides unparalleled resolution of internal structures. Crucially, this work advances previous efforts by explicitly demonstrating that integrated ERT-GPR approaches, when combined with algorithm-based interpretation, can resolve microbialite morphology, distribution and internal architecture with a level of objectivity not previously achieved. Beyond methodological refinement, these findings open new avenues for reconstructing microbialite development and preservation in ancient carbonate systems and hold strong potential for application in other geological contexts where complex carbonate structures challenge traditional geophysical imaging. Full article

This paper presents DeepMask-GPR, a novel deep learning framework for automatic detection and geometric estimation of buried cylindrical objects in ground-penetrating radar (GPR) B-scan images. Built upon Mask R-CNN, the proposed method integrates hyperbola detection, apex localization, and real-world coordinate mapping in an end-to-end architecture. A curvature-enhanced dual-channel input improves the visibility of weak hyperbolic patterns, while a quadratic regression loss guides the network to recover precise geometric parameters. DeepMask-GPR eliminates the need for raw signal data or manual post-processing, enabling robust and scalable deployment in field scenarios. On two public datasets, DeepMask-GPR achieves consistently higher TPR/IoU for spatial localization than baselines. On an in-house B-scan set, it attains low MAE/RMSE for radius estimation. Full article

Ground-penetrating radar (GPR) is widely used for thickness or compaction degree detection of asphalt pavement layers, where the dielectric properties of asphalt mixtures serve as a key parameter influencing detection accuracy. These properties are closely related to the composition of the mixture and are susceptible to environmental factors such as water or ice. To clarify the influence of various factors on the dielectric behavior of asphalt mixtures, an experimental study was conducted under controlled environmental conditions. Asphalt mixture specimens with different air void contents (5.49~10.29%) were prepared, and variables such as void fraction, moisture, and ice presence were systematically controlled. Air-coupled GPR was employed to measure the specimens, and the relative permittivity was calculated using both the reflection coefficient method (RCM) and the thickness inversion algorithm (TIA). Discrepancies between the two methods were compared and analyzed. Results indicate that the RCM is significantly influenced by surface water or ice and is only suitable for dielectric characterization under dry pavement conditions. In contrast, the TIA yields more reliable results across varying surface environments. A unified model (the optimized shape factor u = −4.5 and interaction coefficient v = 5.1) was established to describe the relationship between the dielectric properties of asphalt mixtures and their volumetric parameters (bulk specific density, air void content, voids in mineral aggregate, and voids filled with asphalt). This study enables quantitative analysis of the effects of water, ice, and mixture composition on the dielectric properties of asphalt mixtures, providing a scientific basis for non-destructive and accurate GPR-based evaluation of asphalt pavements. Full article

Efficient soil moisture monitoring is fundamental to precision agriculture, enabling improved irrigation management, enhanced crop productivity, and sustainable water use. This review comprehensively evaluates soil moisture sensing technologies, classifying them into invasive and non-invasive approaches. The underlying operating principles, strengths, and limitations, as well as documented practical applications, are critically discussed for each technology. Invasive methods, including dielectric sensors, matric potential devices, heat-pulse sensors, and microstructured optical fibres, offer high-resolution data but require careful installation and calibration to account for environmental and soil-specific variables such as texture, salinity, and temperature. Non-invasive technologies—such as microwave remote sensing, electromagnetic induction, and ground-penetrating radar—enable large-scale monitoring without disturbing the soil profile; however, they face challenges in terms of resolution, cost, and data interpretation. Key performance factors across all sensor types include installation methodology, environmental sensitivity, spatial representativeness, and integration with decision-support systems. The review also addresses recent innovations such as biodegradable and Micro–Electro–Mechanical Systems sensors, the incorporation of Internet of Things platforms, and the application of artificial intelligence for enhanced data analytics and sensor calibration. While sensor deployment has demonstrated tangible benefits for irrigation efficiency and yield improvement, widespread adoption remains constrained by technical, economic, and infrastructural barriers, particularly for smallholder farmers. The analysis concludes by identifying research gaps and recommending strategies to facilitate the broader uptake of soil moisture sensors, with a focus on cost reduction, calibration standardisation, and integration into climate-resilient agricultural frameworks. Full article

This study examines the feasibility of incorporating cathode-ray tube (CRT) panel glass as a partial replacement of natural aggregate in concrete, aiming to promote sustainable material utilization without compromising structural performance. Nine mixtures were prepared using three cement types—Normal 42.5 N, PC 50M(S-V-L) 42.5 N; Profi 42.5 R, PC 20M(S-L) 42.5 R; and Cement without additions, CEM I 42.5 R—and three CRT contents (0%, 5%, and 10%). A comprehensive experimental program was conducted, including tests on natural aggregates, mortars, and concrete in both fresh and hardened states, as well as flexural testing of reinforced concrete beams, supported by ground-penetrating radar (GPR) and digital image correlation (DIC) measurements. The results revealed that replacing up to 5% of natural aggregate with CRT glass had negligible effects on workability and density while slightly improving compressive and flexural strength. At 10% replacement, a minor reduction in strength and ductility was observed. Durability-related parameters, such as water absorption and carbonation depth, increased slightly but remained within acceptable limits. Flexural tests confirmed that beams with 5% CRT content exhibited comparable load capacity and crack propagation to reference beams. This study represents the first combined application of Digital Image Correlation (DIC) and Ground-Penetrating Radar (GPR) in evaluating reinforced concrete beams with CRT-modified concrete across different cement types. The results showed that incorporating 5% CRT glass increased flexural tensile strength by up to 15% compared with the control mix, confirming both the structural feasibility and sustainability of such composites. Overall, the findings indicate that CRT panel glass can be effectively utilized up to a 5% replacement level, offering both environmental and structural advantages for sustainable concrete production. Based on the experimental results, a replacement level of up to 5% CRT glass is recommended for structural concrete applications under the tested conditions, where CRT particles replaced the 4–8 mm medium aggregate fraction, as it ensures a balance between strength, ductility, and durability. Full article

Coal mining faces critical challenges due to variable geological conditions that affect intelligent mining and safe production. Ground-penetrating radar (GPR), a high-resolution and non-destructive sensing technology, is essential for precise geological detection. However, underground electromagnetic interference, multiple reflections, and complex media significantly degrade the signal-to-noise ratio (SNR), causing reflection signals to be obscured and geological interfaces to become blurred, thereby hindering accurate subsurface interpretation. Traditional denoising methods struggle to extract weak reflection signals under such complex noise conditions. To address these challenges, this study proposes a physics-guided conditional diffusion model that integrates physical constraints with deep learning to achieve intelligent denoising and weak-signal recovery for high-noise GPR data. Specifically, a dual-path GMM probabilistically models both feature signals and complex noise, while incorporating the wave equation ensures physical consistency with electromagnetic propagation. Experiments using a hybrid dataset combining field-measured noisy data and simulated features—evaluated using SSIM, PSNR, MAE, peak alignment, and structural continuity—demonstrate that the proposed method outperforms existing techniques in both noise suppression and signal reconstruction. Field tests in underground coal mines further confirm its practical applicability. Full article

To overcome the limitations of conventional methods, this study developed a novel aerial-ground collaborative framework for multi-dimensional quality assessment of asphalt pavement. The quality inspection of asphalt pavement in the whole construction process is realized. Multiple non-destructive testing (NDT) techniques were integrated, including drone-based infrared thermography, ground-penetrating radar (GPR), and a nuclear-free density gauge. Results showed a strong correlation (R² > 0.95) between the radar-derived dielectric constant and core samples, enabling rapid, full-coverage characterization. The density gauge achieved less than 3% error. Furthermore, a compactness prediction model based on the dielectric constant and an air void content evaluation model based on temperature parameters are further constructed. This system enables aerial screening, point verification, and ground diagnosis, significantly enhancing inspection efficiency and comprehensiveness. Full article