Ground Penetrating Radar Research Articles

Factors controlling peat soil thickness and carbon storage in temperate peatlands based on UAV high-resolution remote sensing

Peatlands store a large amount of carbon. However, peatlands are complex ecosystems, and acquiring reliable estimates of how much carbon is stored underneath the Earth’s surface is inherently challenging, even at small scales. Here, we aim to establish links between the above- and below-ground factors that control soil carbon status, identify the key environmental variables associated with carbon storage, as well as to explore the potential for using Unmanned Aerial Vehicle (UAV) remote sensing for spatial mapping of peatlands. We combine UAVs equipped with Red-Green-Blue (RGB), multispectral, thermal infrared, and light detection and ranging (LiDAR) sensors with ground-penetrating radar (GPR) technology and traditional field surveys to provide a comprehensive, 3-dimensional mapping of a peatland hillslope-floodplain landscape in the Belgian Hautes Fagnes. Our results indicate that both peat thickness and soil organic carbon (SOC) stock (top 1 m) are spatially heterogeneous and that the contributions from the surface topography to peat thickness and SOC stock varied from micro- to macro-scales. Peat thickness was more strongly controlled by macro-topography (R2 = 0.46) than SOC stock, which was more influenced by micro-topography (R2 = 0.21). Current vegetation had little predictive power for explaining their spatial variability. Additionally, the UAV data provided accurate estimates of both peat thickness and SOC stock, with RMSE and R2 values of 0.16 m and 0.85 for the peat thickness, and 59.25 t/ha and 0.85 for the SOC stock. However, similar performance can already be achieved by using only topographical data from the LiDAR sensor (for peat thickness) and a combination of peat thickness and topography (for SOC stock) as predictor variables. Our study bridges the gap between surface observations and the hidden carbon reservoir below. This not only allows us to improve our ability to assess the spatial distribution of SOC stocks, but also contributes to our understanding of the environmental factors associated with SOC storage in these highly heterogeneous landscapes, providing insights for environmental science and climate projections.

Characterization of Degraded Ballast Strength: A Field Application of Ground Penetrating Radar and Dynamic Cone Penetration

Characterization of Degraded Ballast Strength: A Field Application of Ground Penetrating Radar and Dynamic Cone Penetration

Investigation and monitoring of rotational landslides in El Mokkattam plateau Egypt, using integrated geological and geophysical techniques

Landslides are a rare but hazardous geological phenomenon in Egypt, with the El Mokattam plateau situated in the eastern part of Cairo covering approximately 64 km2 and ranging in elevation from 50 to 205 m. This study aims to identify and monitor landslides in the area using various geophysical methods. Twelve Electrical resistivity tomography (ERT) profiles,twenty-two P-wave Seismic Refraction profiles, twenty-two Refraction microtremors profiles, three ground penetrating radar (GPR) profiles and borehole data were utilized to analyze the occurrence of landslides in the El Mokattam Plateau. Additionally, we employed a relatively new geophysical method, studying high-frequency microtremor sounds emitted from landslide collapses at 22 stations. Our analysis identified steep slopes, jointed or fractured rocks, and irrigation water as primary factors contributing to landslides, with irrigation water acting as a lubricant for clays and promoting ground sliding. Examination of high-frequency microtremor sounds revealed a potential correlation between vertical high-frequency spectra at 100 Hz and landslide collapses, which aids in the identification of landslide-prone zones. Therefore, we conclude that seismological studies, particularly spectral analysis of high-frequency and low-amplitude sounds (microtremors) emitted from soil, offer a promising approach for investigating landslides.

Plant Root Characterization Using Ground-Penetrating Radar with Deep Learning

Plant Root Characterization Using Ground-Penetrating Radar with Deep Learning

Ground Penetrating Radar (GPR) Investigations in Urban Areas Affected by Gravity-Driven Deformations

Ground Penetrating Radar (GPR) Investigations in Urban Areas Affected by Gravity-Driven Deformations

Assessment of Soil Horizons and Their Matric Potential from Ground-Penetrating Radar Signal Attributes

Soil plays significant roles in different phases and in the continuous existence of human life. Its comprehensive knowledge, particularly as related to its physical characteristics, enhances its utilization, conservation, and management. The traditional methods of soil study are characterized with some pitfalls such as much time needed to perform such assessments. There are also issues of invasiveness that affect the soil structures and discrete sampling that may not reflect true spatial attributes in the outcome of such techniques. These problems are largely due to the concealing nature of soil layers that made its thorough evaluation difficult. In this study, an alternative geophysical approach has been adopted. The technique is the ground-penetrating method (GPR) that utilizes electromagnetic pulse energy via its equipment’s sensors, which can allow the investigation of soil properties, even in its concealing state. This study aimed at qualitatively evaluating the soil horizons and the matric potentials using the GPR signal attributes within the unsaturated zone with a view of having insight into the test field’s characterization. Field data measurements were obtained using MALA ProEX GPR equipment with its accessories manufactured by MALA Geosciences, Stockholm, Sweden. Evaluation of the processed field data results and computed attributes show soil characteristics variations with depth that was interpreted as the layers. This can be seen from the GPR data presentation as an image representing the subsurface of the zones of propagation of the pulse energy. Spectral analysis of the GPR signals allows for the delineation of two zones of contrasting features, which were tagged as high and low matric potentials. Although the conventional direct measurement of the matric potential was not made at the time of the study to complement and confirm the veracity of the approach, the results indicate the possibility of the approach towards a quick and in situ technique of soil investigations. Such evaluation may be valuable input in precision agriculture where accurate data are sought for implementation.

Three-Dimensional Subsurface Pipe Network Survey and Target Identification Using Ground-Penetrating Radar: A Case Study at Jilin Jianzhu University Campus

This study focuses on the application of ground-penetrating radar (GPR) in conducting field surveys and data processing at the northern campus of Jilin Jianzhu University. The research site’s geographical location and overall conditions are described. A detailed layout of the survey lines for 3D surveys is presented. The collected data undergo basic processing and interpretation, identifying multiple target bodies and their associated electromagnetic responses. Advanced analyses such as 3D imaging, common attribute analysis, and time-varying centroid frequency attribute analysis are employed to investigate underground features and potential pipe networks. The case study in this research demonstrates that the integration of 3D GPR surveys and time-varying centroid frequency analysis can effectively assess the attenuation characteristics of subsurface media and structures, thereby enhancing the overall prospecting and data interpretation capabilities of GPR.

Analysis and Design of an Airborne-Dangled Monopole-Antenna Symmetric Remote-Sensing Radiation Source for Airport Runway Monitoring

Traditional methods for monitoring the foundation settlement of airport runways predominantly employ equipment such as leveling instruments, total stations, layered settlement instruments, magnetic ring settlement instruments, ground-penetrating radar (GPR), and synthetic aperture radar. These methods suffer from low automation levels, are time-consuming, labor-intensive, and can significantly disrupt airport operations. An alternative electromagnetic detection technique, Controlled Source Audio-Frequency Magnetotellurics (CSAMT), offers deep-depth detection capabilities. However, CSAMT faces significant challenges, particularly in generating high signal-to-noise ratio (SNR) signals in the far-field region (FfR). Traditional CSAMT utilizes grounded horizontal dipoles (GHDs), which radiate symmetric beams. Due to the low directivity of GHDs, only a small fraction of the radiated energy is effectively utilized in FfR observations. Enhancing the SNR in FfR typically requires either reducing the transceiving distance or increasing the transmitting power, both of which introduce substantial complications. This paper proposes an airborne-dangled monopole-antenna symmetric remote-sensing radiation source for airport runway monitoring, which replaces the conventional GHD. The analytical, simulation, and experimental verification results indicate that the energy required by the airborne-dangled symmetric source to generate the same electric field amplitude in the FfR is only one-third of that needed by traditional CSAMT. This results in significant energy savings and reduced emissions, underscoring the advantages of the airborne-dangled monopole-antenna symmetric source in enhancing energy efficiency for CSAMT. The theoretical analysis, simulations, and experimental results consistently verify the validity and efficacy of the proposed airborne-dangled monopole-antenna symmetric remote-sensing radiation source in CSAMT. This innovative approach holds substantial promise for airport runway monitoring, offering a more efficient and less intrusive solution compared to traditional methods.

A Ground-Penetrating Radar-Based Study of the Structure and Moisture Content of Complex Reconfigured Soils

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

Semi-Conv-DETR: A railway ballast bed defect detection model integrating convolutional augmentation and semi-supervised DETR

Railway ballast bed defects, including subsidence, mud pumping, and abnormal water, pose significant safety risks by destabilizing the railway ballast beds. Timely detection and repair of railway ballast bed defects are vital for safeguarding the security of both the trains and their passengers. Ground-Penetrating Radar (GPR) is widely used for railway ballast beds inspection and evaluation owing to its high speed and non-destructive characteristics. However, GPR image data contain considerable noise, and the distinct shapes and sizes of each ballast bed defect renders it challenging to apply a unified data annotation standard, which hampers the development of railway ballast bed defect detection models. Considering the distinct wave-like characteristics of GPR data and the vaguely contours of the defects to be identified, we propose a convolutional augmentation operation tailored for GPR images. Furthermore, we also investigate Semi-Supervised Learning by employing limited annotated railway ballast bed inspection data along with a vast amount of unlabeled data to joint train the DETR detection model. To sum up, we proposed a semi-supervised DETR model supplemented with convolutional augmentation for railway ballast bed defect detection, termed as Semi-Conv-DETR model. Experimental outcomes indicate that Semi-Conv-DETR shows an improvement of 58.6 % in accuracy when compared to the classical Faster-RCNN model.

Exploring the Ground-Penetrating Radar Technique’s Effectiveness in Diagnosing Hydropower Dam Crest Conditions: Insights from Gura Apelor and Herculane Dams, Romania

Exploring the Ground-Penetrating Radar Technique’s Effectiveness in Diagnosing Hydropower Dam Crest Conditions: Insights from Gura Apelor and Herculane Dams, Romania

Refined Modeling of Heterogeneous Medium for Ground-Penetrating Radar Simulation

Refined Modeling of Heterogeneous Medium for Ground-Penetrating Radar Simulation

Data Pre-processing method of Ground Penetrating Radar based on HE-R2M

Data Pre-processing method of Ground Penetrating Radar based on HE-R2M

Road underground defect detection in ground penetrating radar images based on an improved YOLOv5s model

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

Classification of Practical Floor Moisture Damage Using GPR - Limits and Opportunities

Machine learning in non-destructive testing (NDT) offers significant potential for efficient daily data analysis and uncovering previously unknown relationships in persistent problems. However, its successful application heavily depends on the availability of a diverse and well-labeled training dataset, which is often lacking, raising questions about the transferability of trained algorithms to new datasets. To examine this issue closely, the authors applied classifiers trained with laboratory Ground Penetrating Radar (GPR) data to categorize on-site moisture damage in layered building floors. The investigations were conducted at five different locations in Germany. For reference, cores were taken at each measurement point and labeled as (i) dry, (ii) with insulation damage, or (iii) with screed damage. Compared to the accuracies of 84 % to 90 % within the laboratory training data (504 B-Scans), the classifiers achieved a lower overall accuracy of 53 % for on-site data (72 B-Scans). This discrepancy is mainly attributable to a significantly higher dynamic of all signal features extracted from on-site measurements compared to laboratory training data. Nevertheless, this study highlights the promising sensitivity of GPR for identifying individual damage cases. In particular the results showing insulation damage, which cannot be detected by any other non-destructive method, revealed characteristic patterns. The accurate interpretation of such results still depends on trained personnel, whereby fully automated approaches would require a larger and diverse on-site data set. Until then, the findings of this work contribute to a more reliable analysis of moisture damage in building floors using GPR and offer practical insights into applying machine learning to non-destructive testing for civil engineering (NDT-CE).

The hypertidal Santa Cruz–Chico River estuary (South Patagonia, Argentina): A hybrid ria-type system under extreme tides, arid climate and active uplift

The present study focuses on the morphosedimentary organization and sediment infilling stratigraphy of one of the largest estuaries of southern Patagonia in Argentina. With a tidal range up to 12 m, the area is subject to extreme tidal conditions, combined with moderate offshore wave climate, strong and constant westerly winds, and contrasted water and sediment discharges from the two tributaries of the estuary, the Santa Cruz and Chico rivers. The estuarine valley is entrenched in the Patagonian coastal plateau due to significant uplift. On the basis of sediment facies (sedimentary structures, grain size, geochemistry, mineralogy), meiofauna (foraminifera and testate amoebae), morphological changes and shallow geophysics (high-resolution seismic reflection, ground-penetrating radar) data, the Santa Cruz–Chico River system is defined as a hybrid system comprising a tide-influenced fluvial mouth (the Santa Cruz River) and a tide-dominated estuary (the Chico River estuary), both converging toward an elongated subtidal ria-type estuarine basin. River-supplied sands and muds by-pass the estuarine basin and are exported offshore where they settle and form an ebb-tidal delta. Sediments in the Santa Cruz–Chico River valley mainly consist of Pleistocene lowstand fluvial gravels resting on the regional Miocene substrate, and thin early Holocene transgressive deposits, deeply incised by a tidal ravinement surface that developed during the highest Holocene sea-level at ca 7500 y. BP. After the maximum stillstand, relative sea level fell and a competition occurred between erosion, promoted by water depth decrease, and deposition, favored by tidal prism reduction. At present, sediment by-passing and offshore sediment export are the dominant processes. The very large size of the ebb-tidal delta, which expands on the continental shelf, suggests that this situation has prevailed for a very long time.

Geophysical Mapping of Cemented Subsoils for Agricultural Development in Southern Peru

Cemented subsoils, commonly referred to as caliche, pose a regular challenge for agricultural development in arid and semi-arid regions like coastal southern Peru. These subsurface features restrict root penetration, limit water infiltration and hinder essential soil processes, ultimately reducing crop yields and agricultural productivity. Accurate and efficient mapping of caliche is important for optimizing land-use planning and implementing sustainable agricultural practices. This study presents the application of near-surface geophysical techniques for mapping caliche deposits in the context of agricultural development at the future Majes II site in the Arequipa region of southern Peru. Specifically, we employed high-frequency ground-penetrating radar (GPR) and frequency-domain electromagnetics (FDEM) at a testbed on the Majes II site to evaluate their ability to delineate the extent, thickness, and depth of caliche within the local geology. GPR offers high-resolution imaging, effectively capturing sharp contrasts between caliche and surrounding materials, providing detailed information on the thickness (approximately 0.4 m) and the depth (up to 1.5 m) of the caliche layers. FDEM provides valuable insights into the presence of caliche at a faster rate of data acquisition and processing, enabling rapid assessment of the extent of caliche deposits, although with the tradeoff of lower resolution and depth information. We demonstrate that these two geophysical methods can be used separately or in an integrated manner for collaborative interpretation at the Majes II site to inform land management decisions, including identifying areas with favorable conditions for crop production and implementing targeted interventions to mitigate the adverse effects of caliche on agricultural productivity.

Atypical holocene ctenomyid rodent burrows related to eolian dune deposits from central Argentina

This contribution aims to interpret the producers and the paleoenvironmental context of Holocene rodent burrows (and associated ichnofossils) from the Late Pleistocene-Holocene Meaucó Formation, central La Pampa Province, Argentina. Fieldwork included a detailed sedimentary logging, a ground penetrating radar survey and documentation of the fossil burrow systems. The Meaucó Formation rest on erosive unconformity over the Upper Miocene-Lower Pliocene Cerro Azul Formation and display a vertical change from eolian dunes that become sparser up section (as imaged by ground penetrating radar) and were capped by a paleosol. The paleosol is covered by recent eolian dune and interdune deposits. The fossil rodent burrows occur up to 1.7 m below the paleosol and share several morphologic features with modern Ctenomyidae burrows, except the high slope of entrance and internal ramps, and the maximum depth of burrows. To explain these differences, it is proposed that the burrows were excavated by ctenomyids in ancient sloping surfaces of eolian dunes, which resulted in increased inclination and apparent maximum depth of the systems. This is an example of influence of environmental conditions on the architecture of rodent burrows, and can be of help when interpreting other similar fossil examples. The paleosol community associated with the fossil vertebrate burrows also included other organisms recorded by their trace fossils: horses (Hippipeda isp.), a scrubby vegetation (rhizoliths), coleoptera or spiders (Skolithos linearis), cicada nymphs (Feoichnus challa) and ants (Daimoniobarax isp.).

Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity

The analysis of ground-penetrating radar (GPR) data is of vital importance for detecting various subsurface features that might manifest as hyperbolic peaks, which are indicators of a buried object or grayscale variation in the case of contrast in the soil texture. This method focuses on identifying exaggerated patterns through a series of image-processing steps. Two GPR images are initially read and preprocessed by extracting channels, flipping, and resizing. Then, specific regions of interest (ROIs) are cropped, and the Fourier transform is further applied to turn them into the frequency domain. With the help of their frequency signatures, these patterns are extracted from the images, and binary masks are constructed to obtain features of interest. These masked images were reconstructed and merged to make hyperbolic features visible. Finally, Local Binary Pattern (LBP) analysis is used to emphasize these hyperbolic peaks, thereby facilitating their recognition across the whole image. The proposed approach improves the detection of performance subsurface features in GPR data; hence, it is an important tool for geophysical surveys and other related applications. The results prove the high performance of the proposed procedure in improving GPR image characteristics.

Characterization of leakage signatures in buried water pipes by ground penetrating radar(GPR) and instantaneous frequency analysis

The aging and complexity of underground water pipes pose significant challenges to modern society, necessitating long-term monitoring and maintenance to prevent the socioeconomic costs. So, effective nondestructive and geophysical methods are needed to localize the possible leakage areas before any rational opening up is conducted. In this study, we propose a workflow to generate instantaneous frequency slices (IFS) from raw ground penetrating radar (GPR) data. Characterizing the horizontal and vertical patterns of water leakage using IFS revealed two key mechanisms—reflections due to the dry and wet interface and absorption due to water distributed in soil—that significantly influence instantaneous frequency. A well-designed real site (Q-Leak), in collaboration with the Water Supplies Department (WSD) of the government, was conducted to replicate typical leakage scenarios, validating the effectiveness of our IFS-based approach. The results of proposed method demonstrate that IFS is a reliable approach for characterizing leakages in buried water pipes, effectively addressing several challenges associated with amplitude slice analysis. Our proposed methodology enriches the toolkit available for large-scale, future-oriented water leakage detection.