Ground Penetrating Radar Research Articles

Enhancing full-waveform inversion of field GPR data: A source-independent approach with dynamic reference selection via SE-Wave-U-Net

Ground-penetrating radar (GPR) is a widely used nondestructive detection tool. Full-waveform inversion (FWI) offers a reliable algorithm for accurately imaging GPR data. Despite its potential, FWI’s effectiveness is often compromised by unknown excitation sources, which can lead to computational failures, diminished imaging accuracy, or both. Addressing this challenge, this paper introduces a convolution-type objective function designed to mitigate the detrimental effects of these unknown excitation sources on FWI imaging. Critical to the success of this function is the precise selection of reference traces. Further refining the capability for signal separation, we have integrated squeeze-and-excitation (SE) modules into the traditional Wave-U-Net architecture, culminating in the development of the SE-Wave-U-Net framework. This framework is adept at dynamically extracting accurate reference traces from field GPR data, which are essential for accurate FWI imaging. The efficacy, accuracy, and practical applicability of our methods are validated through extensive analysis of numerical, laboratory experiments, and field GPR data. Our algorithm not only establishes a high-precision computational framework for FWI but also enhances the reliability of GPR imaging in complex environments.

A Planar Capacitive Sensor for Detecting and Measuring Thickness and Depth of Oil Under Ice

Abstract Detecting and characterizing oil spills in arctic environments is challenging due to the presence of sea ice. Methods based on Ground-Penetrating Radars and acoustic sensors were used in the literature to detect oil in ice-covered waters; however, their implementation is costly, and their capabilities are limited in terms of oil-thickness estimation. To address this problem, we propose a new low-cost sensing system based on a planar capacitive sensor that can detect the presence of oil under ice sheets and measure its thickness and depth. Our proposed sensor is based on movable dual electrodes mounted next to each other on the same sensing plane. The electric field created between the electrodes extends beneath the sensing plane and is affected by the electric properties of the sensed material (ice, oil, or water). Changes in the mutual capacitance of the electrodes are related to changes in the thickness and depth of the embedded oil phase. The capacitance of the sensor is measured at two different excitation frequencies while changing the horizontal separation distance between the electrodes. These measurements are collected to create a dataset for training machine-learning-based classification and regression models to detect the presence of oil and measure its thickness and depth. In comparison with the available sensing techniques, our proposed sensor has several advantages, such as being non-invasive or non-intrusive, simple to manufacture, safe to operate, and having low cost and low maintenance requirements. The experimental evaluation described in this paper demonstrates the effectiveness of our proposed system, which showed a very high detection accuracy of more than 90% and an accurate thickness and depth estimation capability with a Mean Absolute Error (MAE) of around 0.5 cm for thickness and depth estimations for oil thicknesses ranging between 0.5 cm and 8 cm and for oil depths ranging between 2 and 5 cm.

A state-of-the-art review on graph characterization and automated detection of road underground targets using ground-penetrating radar

A state-of-the-art review on graph characterization and automated detection of road underground targets using ground-penetrating radar

Mutual Interference Suppression and Signal Enhancement Method for Ground-Penetrating Radar Based on Deep Learning

Mutual Interference Suppression and Signal Enhancement Method for Ground-Penetrating Radar Based on Deep Learning

Integration of satellite and aerial images with multichannel GPR surveys in the archaeological area of Augusta Bagiennorum for an improved description of the urban setting

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

Utilizing Dual Polarized Array GPR System for Shallow Urban Road Pavement Foundation in Environmental Studies: A Case Study

Maintaining the integrity of urban road pavements is vital for public safety, transportation efficiency, and economic stability. However, aging infrastructure and limited budgets make it challenging to detect subsurface defects that can lead to pavement collapses. Traditional inspection methods are often inadequate for identifying such underground anomalies. Ground Penetrating Radar (GPR), especially dual-polarized array systems, offers a non-destructive, high-resolution solution for subsurface inspection. Despite its potential, effectively detecting and analyzing areas at risk of collapse in urban pavements remains a challenge. This study employed a dual-polarized array GPR system to inspect road pavements in London. The research involved comprehensive field testing, including data acquisition, signal processing, calibration, background noise removal, and 3D migration for enhanced imaging. Additionally, Short-Fourier Transform Spectrum (SFTS) analysis was applied to detect moisture-related anomalies. The results show that dual-polarized GPR systems effectively detect subsurface issues like voids, cracks, and moisture-induced weaknesses. The ability to capture data in multiple polarizations improves resolution and depth, enabling the identification of collapse-prone areas, particularly in regions with moisture infiltration. This study demonstrates the practical value of dual-polarized GPR technology in urban pavement inspection, offering a reliable tool for early detection of subsurface defects and contributing to the longevity and safety of road infrastructure.

Application of Ground Penetrating Radar in the Assessment of Aged Roads: Focus On Complex Structures Under Different Weather Conditions

Application of Ground Penetrating Radar in the Assessment of Aged Roads: Focus On Complex Structures Under Different Weather Conditions

The Ground-Penetrating Radar Image Matching Method Based on Central Dense Structure Context Features

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively.

Evidence of brines interconnections and different flow patterns within the boulder clay glacier and its moraine (Victoria Land, East Antarctica)

Multi-technique integrated surveys were carried out to investigate brine characteristics, connectivity and flow patterns in the Boulder Clay Glacier area, Victoria Land, East Antarctica. Specifically, electromagnetic geophysical surveys focused mainly on Ground Penetrating Radar (GPR) and integrated by Frequency Domain induction, not only demonstrated the presence of brines in the subsurface, but also allowed to image several structures and glaciological elements. Chemical analyses suggested the origin and differentiation of the brines, providing evidence for interconnected pathways. To the best of our knowledge, this is the first time that brines interconnection and their chemical common footprint is demonstrated below a glacier and its moraine at shallow depth, combining boreholes, geophysical, geomorphological, and chemical data. This demonstrates that liquid flows can occur at very shallow depths, even in continental Antarctica, and that pressurised brines can flow beneath and through glaciers and moraines.

A Method to Detect Concealed Damage in Concrete Tunnels Using a Radar Feature Vector and Bayesian Analysis of Ground-Penetrating Radar Data

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

Georadar Survey and Simulation for Subsurface Investigation at Historical Mosque of Sorghatmesh, Cairo, Egypt

Sorghatmesh mosque is a historical structure that was constructed in Cairo, Egypt, by Prince Saif El-Din Sorghatmesh in 1356. A dual-frequency ground-penetrating radar (GPR) with 250–700 MHz was used to investigate the subsurface of the Sorghatmesh mosque for restoration purposes. A total of 37 lines were surveyed on the ground floor of the mosque. The subsurface utilities were detected, and the status of the concrete base and the medium of the ground floor were assessed. A set of subsurface anomalies were detected and interpreted within the ground floor area of the mosque. In order to validate the interpretation, a trial pit was drilled on the ground floor, allowing for the visual inspection of the subsurface, and a Georadar numerical simulation was carried out to study the responses of the subsurface materials and conditions. For a better comprehension of the results, the ground floor area was categorized into five zones where the GPR interpretations between survey lines are almost similar. This work not only demonstrates the effectiveness of GPR as a non-invasive investigation tool but also highlights the potential of integrating advanced technologies into cultural heritage preservation by offering refined methodologies and insights for future research and restoration efforts.

Polarisation Synthesis Applied to 3D Polarimetric Imaging for Enhanced Buried Object Detection and Identification

Detecting sub-surface objects poses significant challenges, partly due to attenuation of the ground medium and cluttered environments. The acquisition polarisation and antenna orientation can also yield significant variation of detection performance. These challenges can be mitigated by developing more versatile systems and algorithms to enhance detection and identification. In this study, a novel application of a 3D SAR inverse algorithm and polarisation synthesis was applied to ultra-wideband polarimetric data of buried objects. The principle of polarisation synthesis facilitates an adaptable technique which can be used to match the target’s polarisation characteristics, and the application of this revealed hidden structures, enhanced detection, and increased received power when compared to single polarisation results. This study emphasises the significance of polarimetry in ground-penetrating radar (GPR), particularly for target discrimination in high-lift-off applications. The findings offer valuable insights that could drive future research and enhance the performance of these sensing systems.

Optimising Ground Penetrating Radar data interpretation: A hybrid approach with AI-assisted Kalman Filter and Wavelet Transform for detecting and locating buried utilities

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

Dynamic LIA advances hastened the demise of small valley glaciers in central Svalbard

Most small land-terminating glaciers in Svalbard have experienced large recession since the Little Ice Age (LIA) and today are thin, cold, and largely inactive. This likely contrasts to their LIA conditions, but the observational record from that time is sparse. We investigate the evolution of five small glaciers in central Nordenskiöld Land, Svalbard, from the LIA to 2019. Photogrammetric reconstructions and ground penetrating radar are used to reconstruct their geometric changes since 1936, and historical observation, photographs, and geomorphological mapping extend this history to before the 1900s. Our results show that from 1936 to 2019, the study glaciers on average lost 49.6% of their area and 77.4% ± 7.7% of their volume, with the greatest volume loss at Scott Turnerbreen of 91% ± 5%. Four out of these five glaciers strongly indicate a history of surge-like advances near the end of the LIA within one or two decades, and the rate of subsequent mass loss seems connected to their previous dynamics. This apparent switch to high activity during a period of rapid climatic change, could have implications for our understanding of past and future glacier evolution; climate change and highly dynamic glacier responses may be more connected than previously thought.

A frequency reconfigurable antireflection metasurface for GPR

Abstract The antireflection metasurface (AM) is employed in ground penetrating radar (GPR) to mitigate the strong reflection of electromagnetic waves at the air-ground interface due to impedance mismatch. However, due to constraints imposed by the relative bandwidth (RBW) and manufacturing processes, these layers tend to exhibit excessive thickness and bulky shape, narrow RBW, and fixed functionality in a passive configuration. This paper presents a novel, dual-band, independent wideband tuning, frequency reconfigurable AM based on varactor diodes with center frequencies of 1.35 GHz and 2.60 GHz. This metasurface possesses positive properties such as a single layer, the ultrathin thickness (0.03 & 0.06λ), the wide RBW (43.3% & 27.4%) and remarkable antireflection. The aforementioned metasurface achieves the described mechanisms and features through the destructive interference theory and the combine element technique. Numerical simulation results of surface currents and electric field energy power demonstrate the antireflection property. The equivalent electromagnetic parameter retrieval results also provide equivalent impedance conditions for non-perfect antireflection. The proposed AM samples demonstrate notable stepwise frequency reconfigurable properties in free-space experiments. The imaging effect after loading this AM is significantly improved in real-world GPR ballast roadbed anomaly detection experiments. This approach provides significant research value and promising prospects across various disciplines, including the stepped-frequency GPR, microwave imaging, and interdisciplinary fields.

Gjellestad introduction, background and historical framework

The spark that ignited this research project was the discovery of the Gjellestad ship in the autumn of 2018 by the Norwegian Institute for Cultural Heritage Research (NIKU). The surveying was initiated by Østfold County Council (ØCC) after a landowner planned to drain farmland (Figure 1). Archaeologists Lars Gustavsen and Erich Nau, both associated with NIKU, conducted the geophysical surveys that led to the uncovering of this amazing archaeological find. The discovery attracted massive international interest, partly because in this particular landscape in Østfold County no one had ever found any evidence to suggest that a ship could be hidden below the ground – this “ship” was considered something of an anomaly. In 2019 the Directorate for Cultural Heritage (Riksantikvaren) funded the verification project to investigate the degree of preservation of the ship anomaly using Ground Penetrating Radar (GPR), as well as to collect material for radiocarbon-dating of other features detected with the GPR. The Museum of Cultural History (MCH) at the University of Oslo led investigations of the ship anomaly, while ØCC led the remaining part of the surveying, mainly through trial trenching and sampling. MCH’s investigations showed that the ship’s state of preservation was poor, with the remaining wood both fragile and prone to attacks from soft rot bacteria, causing uncontrolled deterioration. On the advice of the cultural heritage authorities, the government decided to cover the full cost of the excavation of the ship burial. This excavation was conducted in 2020-21 under the leadership of archaeologist Christian Løchsen Rødsrud.

Research on Airborne Ground-Penetrating Radar Imaging Technology in Complex Terrain

The integration of ground-penetrating radar (GPR) with unmanned aerial vehicles (UAVs) enables efficient non-contact detection, performing exceptionally well in complex terrains and extreme environments. However, challenges in data processing and interpretation remain significant obstacles to fully utilizing this technology. To mitigate the effects of numerical dispersion, this paper develops a high-order finite-difference time-domain (FDTD(2,4)) three-dimensional code suitable for airborne GPR numerical simulations. The simulation results are compared with traditional FDTD methods, validating the accuracy of the proposed approach. Additionally, a Kirchhoff migration algorithm that considers the influence of the air layer is developed for airborne GPR. Different processing strategies are applied to flat and undulating terrain models, significantly improving the identification of shallowly buried targets. Particularly under undulating terrain conditions, the energy ratio method is introduced, effectively suppressing the interference of surface reflections caused by terrain variations. This innovative approach offers a new technical pathway for efficient GPR data processing in complex terrains. The study provides new insights and methods for the practical application of airborne GPR.

Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing.

Automatic detection of water pipeline leakage via unsupervised learning applied on ground penetrating radar data

ABSTRACT Aging urban water supply pipelines are facing significant leakage issues. Ground penetrating radar (GPR) is an effective non-destructive method for leakage localization, but its image analysis mainly relies on interpreter experience, leading to uncertainty and inefficiency. To address this issue, an unsupervised learning method based on GPR data attributes is proposed for automatic leakage detection. First, velocity and energy attributes are extracted from B-Scan profiles to differentiate wet areas caused by leakage from normal areas. Then, the density-based spatial clustering of applications with noise (DBSCAN) clustering method is applied to automatically classify these two types of areas. The proposed method was evaluated on an experimental platform with constant water pressure and pre-drilled leakage points. Experiments show that the velocity and energy attributes decrease by 25.2 and 26.6% after leakage. Using velocity attributes yields better results, and suggestions for DBSCAN hyperparameters are provided. Feature importance analysis indicates that two-way-travel-time with a score of 46.1 and energy attribution index with a score of 32.7 significantly influence leakage identification. This method can also estimate affected leakage areas with an error not exceeding the spacing of three measurement lines, and is available across various underground conditions without needing well-labeled training data.

Robotic destructive and nondestructive testing of concrete structures

Structural elements may develop defects due to severe environmental conditions. Nondestructive testing is a common method for structural evaluations; however, these structures may be inaccessible or located in unsafe areas. In this paper, a Sawyer robot was employed to carry nondestructive testing (NDT) sensors such as Ground Penetration Radar (GPR) and Impact Echo (IE). The experiment comprised twelve reinforced concrete beams divided into four groups: Control (without defects), Void, Corrosion, and Debonding. The beams underwent three-point bending tests, with loads distributed across three levels: 50 %, 75 %, and 100 % of the failure load. Robotic and handheld IE and GPR tests were conducted, with IE performed before the actual test and GPR conducted before, during, and after the tests. Load-displacement diagrams were subsequently generated. The comparison between robotic and handheld tests demonstrated the high accuracy of the robotic NDT and indicated that the defects led to reduced load capacity and increased crack propagation.