Ground Penetrating Radar Research Articles

Using Ground-Penetrating Radar (GPR) to Investigate the Exceptionally Thick Deposits from the Storegga Tsunami in Northeastern Scotland

A submarine landslide on the edge of the Norwegian shelf that occurred around 8150 ± 30 cal. years BP triggered a major ocean-wide tsunami, the deposits of which are recorded around the North Atlantic, including Scotland. Ground-penetrating radar (GPR) was used here to investigate tsunami sediments within estuaries on the coast of northeastern Scotland where the tsunami waves were funnelled inland. Around the Dornoch Firth, the tsunami deposits are up to 1.6 m thickness, which is exceptionally thick for tsunami deposits and about twice the thickness of the 2004 IOT or 2011 Tohoku-oki tsunami deposits. The exceptional thickness is attributed to a high sediment supply within the Dornoch Firth. At Ardmore, the tsunami appears to have overtopped a beach ridge with a thick sand layer deposited inland at Dounie and partly infilled a valley. Later, fluvial activity eroded the tsunami sediments locally, removing the sand layer. At Creich, on the north side of the Dornoch Firth, the sand layer varies in thickness; mapping of the sand layer with GPR shows lateral thickness changes of over 1 m attributed to a combination of infilling an underlying topography, differential compaction, and later reworking by tidal inlets. Interpretation of the GPR profiles at Wick suggests that there has been a miscorrelation of Holocene stratigraphy based on boreholes. Changes in the stratigraphy of spits at Ardmore are attributed to the balance between sediment supply and sea-level change with washovers dominating a spit formed during the early Holocene transgression, while spits formed during the subsequent mid-Holocene high-stand are dominated by progradation.

An alternative approach for detecting cavities in reinforced concrete structures using GPR A-scan data

This paper introduces an alternative approach for detecting cavities in reinforced concrete walls using Ground Penetrating Radar (GPR) A-scan data. GPR, leveraging electromagnetic waves, is extensively applied for cavity detection within structures. The nature of electromagnetic waves, significantly influenced by reflective media and attenuating through them, requires specialized analysis methods for data interpretation. Traditional methods often involve identifying and eliminating overlapping reflection patterns or adjusting signal magnitude at specific depths to isolate peak signals from the target object's surface, which can be subjective and complex. To overcome these challenges, this study proposes quantitatively assessing the presence of cavities by analyzing the integral area of A-scan data within suspected ranges. Observations indicate a substantial difference in reflection patterns between areas with and without cavities, showcasing the potential of this approach for quantitative cavity detection. This approach offers a more objective and quantitative basis for identifying cavities in reinforced concrete structures.

Leakage detection applied to the Da Hedong Reservoir dam (China) based on the flow-field fitting method

Detecting leakage in concrete gravity dams presents a formidable challenge. The flow-field fitting (FFF) method is used to identify leaks in China’s Da Hedong Reservoir dam. We develop vertical- and horizontal-gradient approaches based on the transmitted signal electrical field. A plastic frame is developed to allow the probe to measure horizontal gradients. We determine the exact location of leaks at the reservoir’s bottom and upstream face, respectively, assuming that current leakages are located in hydraulic leakage zones. Numerical experiments and water pressure tests reveal that the conductive silt layer beneath the reservoir can lower the response of the potential values at the leakage inlet, and the effect of the silt layer should be considered and not ignored when interpreting measured data. In addition, with regard to the principle of superposition, the contribution associated with metal pipes in the dam body can be roughly determined by subtracting it from the original potential field data, thereby aiding in locating leaks around the metal pipes. In addition, the FFF measurements from the reservoir’s bottom also confirm the position of existing faults in the substratum at the reservoir site. Last, the results of the FFF method are tested against supporting evidence, such as ground penetrating radar and water pressure tests. In conclusion, these independent data confirm the effectiveness of the FFF method for leakage localization in the concrete gravity dam.

Wave impedance inversion and interpretation of Ground Penetrating Radar (GPR) data for Amery Ice Shelf in East Antarctica

The polar ice sheet is vital for the global climate system, influencing the Antarctic ice sheet's mass balance, sea level rise, and Earth's surface energy. Accurate measurement of its thickness and internal structure is imperative for comprehending glacier evolution and climate change. Ground Penetrating Radar (GPR) is a high-resolution and non-invasive exploration instrument that is extensively used for glacier detection, but only limited geometric information can be identified in the GPR profile, making it difficult to capture the quantitative distribution of physical parameters. To image the fine dielectric parameters of GPR data acquired from the Amery Ice Shelf in East Antarctica in 2003, we propose a multi-trace GPR impedance inversion approach. This method utilizes the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm for GPR data, coupled with weighted L2-norm total-variation multiplicative regularization (MR) to quantitatively estimate the dielectric parameters in the interior of the ice sheet. This scheme adaptively adapts regularization parameters, enhances vertical resolution, and suppresses noise. Additionally, considering lateral correlation, we integrate directional differences to enhance lateral continuity. After validating with complex test data, we apply the method to Amery Ice Shelf GPR data, quantitatively imaging the ice sheet's geometric and dielectric parameters. The inversion results reveal ice thickness, internal features, and the interface between freshwater ice and refrozen sea ice. This allowed us to determine the ablation and refreezing zone boundary at the bottom of ice shelf, providing insights into melting/freezing mechanisms, ice shelf stability, and simulating ice shelf interior dynamics.

Applying Ground Penetrating Radar and Electrical Resistivity Tomography for the Detection of Archaeological Structures in a Pre‐Tarascan Classic‐Epiclassic Site, Tingambato, Michoacán, Mexico

ABSTRACTA survey was conducted to investigate buried archaeological remains at Tingambato, a pre‐Tarascan classic‐epiclassic archaeological site located in the north‐central part of the State of Michoacán in western Mexico, using ground‐penetrating radar (GPR) and electrical resistivity tomography (ERT). The aim of this study was to detect the foundations (ancient buried walls) and cavities (tombs), define the geometry of the foundations and correlate construction style and depth with relative chronological buried structures. The survey was carried out on two grids of 15 m × 37 m (Zona Verde) and 10.25 m × 36.5 m (Ballgame court), using a 200 MHz antenna for GPR and Schlumberger‐Wenner arrays for ERT. GPR 3D acquisition was carried out along parallel lines spaced 0.25 m apart in a single direction. In the first area, considering the geometric shape found at a depth of 1.35 m, we can assume the existence of a buried structure, probably wall remains. In the second area, a very diffractive zone coincides with a resistive anomaly (> 2000 ohm·m). In order to refine the GPR results, synthetic modelling and a comparison with real traces were carried out. The 1D GPR modelling allows us to precise the presence of a cavity with a rectangular cross section. Since the type of volcaniclastic avalanche deposits of the subsoil do not allow the formation of cavities of that size, we infer that it may be the remains of a tomb or an anthropogenic cavity.

High-precision imaging of small voids in tunnel lining based on reverse time migration

Abstract Diseases such as voids behind the initial support of the tunnel will lead to problems such as lining rupture and concrete damage in the tunnel structure, which seriously affects the driving safety in the tunnel. In tunnel construction, ground penetrating radar is frequently employed as a method for detecting hidden defects. However, when the cavity size is small, there will be a large error when the original radar data is directly interpreted, which cannot meet the needs of practical engineering. To enhance the precision in tunnel detection, the Finite-Difference Time-Domain method is used to simulate various small cavities of different shapes located behind the primary support lining of the tunnel, and the study involves examining the electromagnetic response characteristics of different kinds of holes. The migration techniques of KIRCHHOFF, F-K, and reverse time are employed to reconstruct the hole target. Furthermore, digital morphology is employed to enhance the clarity of the image. Finally, the edge detection technology is used to further extract the hole features. After that, small cavities of different shapes are buried in the established outdoor concrete model box and radar detection is carried out. The detection position of the reconstructed radar image and the actual position of the cavity in the model box are verified. The results show that compared with KIRCHHOFF migration and F-K migration, the image processed by reverse time migration is clearer and more intuitive, and can restore the contour of small holes well. The research findings can serve as a reference for the interpretation of radar data of the cavity behind the initial support of the tunnel.

Research on Response Characteristics of GPR Method for Detecting Phreatic Lines in Embankments

The excessive elevation of the phreatic lines under continuous high-water levels is the cause of many dangerous situations in embankments. Therefore, accurately understanding the dynamic changes in the infiltration characteristics of embankments is of great significance for judging and evaluating the stability of engineering. This study conducted indoor model experiments and used ground penetrating radar to detect the phreatic lines of soil. By comparing the radar detection results with the measured position of the phreatic lines, it was determined that the phreatic lines appeared as a continuous strong reflection surface with similar shapes in the radargram, and the reflection below the reflection surface became weaker. Through time-domain analysis of the single waveform at different time points, it was found that the amplitude of the reflected wave at the saturation line was larger, and the energy attenuation below the saturation line was faster, resulting in a rapid decrease in amplitude; perform Fourier transform on a single channel signal to obtain a spectrogram, and perform frequency domain analysis on it. The main frequency is around 600MHz, and the second main frequency is around 900MHz. As the saturation line rises, the main frequency gradually shifts towards lower frequencies.

Synergy of remote sensing data collected with low-cost mobile mapping platform for detection and prediction of damages: a park alley case study

Data synergy involves acquiring and combining data from different sensors to achieve better problem analysis and research results. For more comprehensive data analysis, the sensors are not only mounted on one platform. Still, they should also be compatible with software and hardware, e.g. for the same timestamp registration by different sensors. The aim of this article is to propose the synergy between various remote sensing sensors including the ground penetrating radar (GPR), LiDAR (Light Detection and Ranging) sensor and three photogrammetric RGB cameras for damage detection in a pavement in a park alley. The data were acquired with a low-cost platform, in the Pole Mokotowskie Park in Warsaw, Poland. Three drives were made along the same path with the platform, so it was possible to assess the repeatability of the data. Based on the GPR data, orthophotomap, and digital terrain model (DTM) from images, an analysis of the cracks in the pavement was done. The paper proves additive value from the synergy of data collected for the alley also in the form of a common visualization of acquired data. Results presented in the article showed that using mobile mapping platform and technologies describing the situation above and below the ground level enable a more detailed analysis and inspection of the damages in the park alley.

Ice Thickness Measurement and Volume Modeling of Muztagh Ata Glacier No.16, Eastern Pamir

As a heavily glaciated region, the Eastern Pamir plays a crucial role in regional water supply. However, considerable ambiguity surrounds the distribution of glacier ice thickness and the details of ice volume. Accurate data at the local scale are largely insufficient. In this study, ground-penetrating radar (GPR) was applied to assess the ice thickness at Muztagh Glacier No.16 (MG16) in Muztagh Ata, Eastern Pamir, for the first time, detailing findings from four distinct profiles, bridging the gap in regional measurements. We utilized a total of five different methods based on basic shear stress, surface velocity, and mass conservation, aimed at accurately delineating the ice volume and distribution for MG16. Verification was conducted using measured data, and an aggregated model outcome provided a unified view of ice distribution. The different models showed good agreement with the measurements, but there were differences in the unmeasured areas. The composite findings indicated the maximum ice thickness of MG16 stands at 115.87 ± 4.55 m, with an ice volume calculated at 0.27 ± 0.04 km3. This result is relatively low compared to the findings of other studies, which lies in the fact that the GPR measurements somewhat constrain the model. However, the model parameters remain the primary source of uncertainty. The results from this study can be used to enhance water resource assessments for future glacier change models.

Estimation of soil moisture of a high Andean wetland ecosystem (Bofedal) with geo-radar data and In-Situ measurements, Ayacucho - Peru

High Andean ecosystems within microbasins serve as crucial areas for water recharge, containing both surface and subsurface moisture. However, these ecosystems are currently under threat due to overgrazing, degradation, and the impacts of climate change. The objective is to validate the subsoil moisture of bofedal estimated using ground-penetrating radar (GPR) data in comparison to in-situ measurements obtained with a soil moisture meter (SMM) in the Apacheta microbasin of the Ayacucho region. The validation method involves comparing soil moisture values obtained with the SMM, with the estimated dielectric permittivity (DP) values from GPR surveys along four transects (T) in a bofedal. Reflected wave amplitude data are converted to DP values to identify water pockets (70<DP<81) and saturated soil moisture (10<DP<40). An analysis of the determination coefficient R2 and the Kappa index (κ) was conducted between both groups of bofedal subsoil moisture data along the four surveyed transects at depths ranging from 0 to 24 cm that contain water and saturated moisture. T1 contains a volume of 1,16m3 (47.85 %), T2 has 0.98m3 (46.6 %), T3 lacks water (40.8 %), and T4 holds 0.63m3 (31.45 %). The correlation of DP data with SMM for T1 (R2=0.801), T2 (R2=0.949), T3 (R2=0.837) y T4 (R2=0.842) implies that the SMM measurements significantly explain the estimated DP. Moreover, the kappa test demonstrated good agreement reliability between both observations made with GPR and SMM, with κ=0.763;[95%CI:0.471−1.055], indicating that the GPR method for measuring subsoil moisture is acceptable with an 87.5% confidence level.

A Study on Drone Based Mine Detection and Diffusion System: Present Scenario and Potential Prospects

Abstract: Decades-long issues are caused by explosive remnants of war (ERW) and landmines that remain subterranean during times of conflict. Therefore, it is critical to promptly identify concealed explosive devices and secure the former zones of conflict. The sensor is affixed to the drone via a stabilization robot that possesses a bouncing motion that enables the detector to precisely survey the ground.. After knowing location of mines, its getting diffuser destroy by using drones .Due to drones mines detection is getting easy than manually or mobile vehicle tracking. Here we are focusing on GPR (Ground Penetrating Radar) and IR(Infrared)sensor for detecting land mines below the ground also we using a camera to know the precise location of mine. For defusing mine dead-weight is used. Dead-weight is thrown from drone to destroy mines. By using drones it is convenient to cover more area in less time. This research aims to produce a technical reference that increases awareness and understanding of unmanned aerial vehicle (UAV) monitoring in Massachusetts, in addition to evaluating its environmental, mining, and reclamation applications.

Detection of Buried Nonlinear Targets Using DORT

Ground-penetrating radars (GPR) based on a variety of techniques have been proposed to improve the performance of buried target (e.g., landmines, threat devices) detection. However, the small radar cross section (RCS) of small electronic devices poses difficulties for target detection, especially when they are buried in lossy and inhomogeneous media. This paper presents a novel buried nonlinear target detection method based on the decomposition of the time-reversal operator (DORT) that uses a multistatic system to overcome the limitations of conventional GPR. Using harmonic radar, which detects the harmonic responses scattered from electronic devices, and DORT processing, which enables focusing/imaging of the detected target, the detection performance is verified by conducting simulation and measurements. The overall results demonstrate that the proposed method achieves accurate detection of buried targets with small RCS.

Topography and buildings of an early Islamic Andalusi city: evidence for Madīnat Ilbīra from excavations and ground penetrating radar

ABSTRACT Madīnat Ilbīra was the capital of one of the territorial organization units of the Umayyad state. The city was founded in the second half of the ninth century, and then abandoned in the eleventh century as a result of the collapse of the Caliphate of Córdoba and the transfer of the centre of the region to Granada. Its remains were located only in the nineteenth century. Modern excavations and non-invasive geophysical prospections carried out in this century allow the reconstruction of the spatial system of the city in the era of its greatest prosperity, the location of the citadel and the main mosque, and the reconstruction of the city’s buildings.

Climate Change and Cultural Heritage: A Global Mapping of the UNESCO Thematic Indicators in Conjunction with Advanced Technologies for Cultural Sustainability

This study investigates the relationship between cultural heritage and climate change, assessing the global implementation of advanced technologies in line with UNESCO’s Thematic Indicators for Cultural Sustainability. Few studies have been conducted on this topic; hence, theoretical background examines the keywords related to cultural heritage preservation, conservation, restoration, climate change mitigation, and adaptation, as well as the intersection of culture and climate change. It also analyses the definitions provided by leading global organizations and explores the use of advanced technologies in protecting cultural heritage. The research methodology is based on an analytical method consisting of a bibliometric assessment and a scientometric assessment. The bibliometric and scientometric analyses map occurrences, frequencies, and intercorrelations of these keywords with UNESCO Thematic Indicators and advanced technology utilization. The findings reveal a predominance of conservation-related Thematic Indicators, suggesting a conservative approach to cultural sustainability, particularly for environmental resilience, wealth, and livelihoods. In terms of advanced technologies, laser scanning and photogrammetry are used for both conservation and restoration purposes, while chromatography and virtual tours are mainly applied to conservation and preservation practices. Otherwise, infrared thermography, X-ray imaging, and online platforms are used, respectively, for heritage preservation, restoration, and conservation. On the other hand, ground-penetrating radar and remote sensing exhibit fewer connections to heritage protection. The mapping of culture and climate change also highlights the importance of conservation in responding to changing climate conditions. Climate adaptation is closely linked to both conservation and preservation efforts, highlighting the critical role of cultural heritage in fostering climate resilience.

Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement

Moisture damage is one of the major defects in asphalt pavement, and will evolve into potholes in a short time which will affect traffic safety. Ground Penetrating Radar (GPR) is an effective non-destructive testing (NDT) method for detecting moisture damage but its data explanation replies on human experience and subjects to labor intensive. To address this issue, an automatic detection method based on extreme gradient boosting (XGBoost) combined with Bayesian hyper-parameter optimization (BHPO) was proposed to detect moisture damage area from GPR traces. High frequency GPR antenna with 2.3 GHz was used to detect the moisture damage area from simulation, laboratory and field tests, and moisture damage dataset with 7960 traces was collected. Thirty time-frequency parameters were extracted from each GPR trace, normalized to unify the three data source, and then optimized into 12 sensitive parameters by feature importance method. These 12 parameters were used to build the recognition model with XGBoost, and the model tuning parameters were optimized by BHPO. To obtain optimization model, random forest (RF) and artificial neural network (ANN) were also trained with BHPO, and compared with XGBoost model. The results indicate that performance of XGBoost model with BHPO achieves the highest accuracy and lowest time cost both in moisture damage and normal trace classification, the accuracies for moisture damage are XGBoost (96.9%) > ANN (95.6%) > RF (95.4%), respectively, and normal are XGBoost (96.5%) > RF (96.1%) and ANN (96.0%), respectively. On this basis, field tests were conducted by core samples, which verified the correct result of XGBoost model. Our method provides a swift and accurate method to locate subsurface targets directly from GPR signals.

Integrated Building Modelling Using Geomatics and GPR Techniques for Cultural Heritage Preservation: A Case Study of the Charles V Pavilion in Seville (Spain).

This paper highlights the fundamental role of integrating different geomatics and geophysical imaging technologies in understanding and preserving cultural heritage, with a focus on the Pavilion of Charles V in Seville (Spain). Using a terrestrial laser scanner, global navigation satellite system, and ground-penetrating radar, we constructed a building information modelling (BIM) system to derive comprehensive decision-making models to preserve this historical asset. These models enable the generation of virtual reconstructions, encompassing not only the building but also its subsurface, distributable as augmented reality or virtual reality online. By leveraging these technologies, the research investigates complex details of the pavilion, capturing its current structure and revealing insights into past soil compositions and potential subsurface structures. This detailed analysis empowers stakeholders to make informed decisions about conservation and management. Furthermore, transparent data sharing fosters collaboration, advancing collective understanding and practices in heritage preservation.

GPR Mapping of Cavities in Complex Scenarios with a Combined Time-Depth Conversion.

The paper deals with a combined time-depth conversion strategy able to improve the reconstruction of voids embedded in an opaque medium, such as cavities, caves, empty hypogeal rooms, and similar targets. The combined time-depth conversion accounts for the propagation velocity of the electromagnetic waves both in free space and in the embedding medium, and it allows better imaging and interpretation of the underground scenario. To assess the strategy's effectiveness, ground penetrating radar (GPR) data referred to as an experimental test in controlled conditions are accounted for and processed by two different approaches to achieve focused images of the scenario under test. The first approach is based on a classical migration algorithm, while the second one faces the imaging as a linear inverse scattering approach. The results corroborate that the combined time-depth conversion improves the imaging in both cases.

A Case Study of the Integration of Ground-Based and Drone-Based Ground-Penetrating Radar (GPR) for an Archaeological Survey in Hulata (Israel): Advancements, Challenges, and Applications

This study delves into the fusion of ground-based and drone-based ground-penetrating radar (GPR) technologies in archaeological exploration. Set against the backdrop of the Hulata solar panel construction site in Israel, the research confronts daunting obstacles such as clayey soil, accurate detection of small objects, and the imperative of timely reporting crucial for construction management. The drone-based GPR, a testament to technological innovation, showcases remarkable adaptability to challenging terrains, dispelling doubts about electromagnetic wave decay in clayey soil. Methodologically, the study employs detailed orthophoto mapping and grid-type surveys. The correlation of the results significantly bolsters the reliability of archaeological discoveries, uncovering scattered artifacts buried approximately 1–1.5 m below the surface. Meticulous excavations validate the geophysical surveys, affirming the presence of structures constructed from boulders. The application at the Hulata site validates the adaptability of drone-based GPR in challenging terrains. It provides a swift, cost-effective, and minimally invasive alternative to traditional excavation techniques, thereby transforming the field of archaeology.

Acceleration of Abramov Glacier (Pamir‐Alay) retreat since the Little Ice Age

The Koksu River valley is located in the Pamir‐Alay mountain range and contains 25 glaciers larger than 1 km2 and numerous smaller glaciers. The largest glacier in the catchment is Abramov Glacier with a current surface area of 22.55 km2 (in 2022), which was extensively monitored between 1965 and 1999, and resumed in 2011. The long and detailed mass balance time series provide, among other information, benchmark climate variables for the Pamir‐Alay range. We report 10 new cosmogenic 10Be exposure dates of glacial moraines directly deposited by Abramov Glacier to extend the glacial history of the valley. Six boulders indicate that the Local Last Glacial Maximum occurred at 17.1±1.0 ka. Four boulders suggest a Little Ice Age (LIA) glacial advance around AD 1750. Secular glacier mass balance reconstructions suggest a progressively negative mass balance since the LIA advance. The decrease in mass balance accelerated in the last quarter of the 20th century. Results from repeated ground penetrating radar (GPR) measurements suggest that Abramov Glacier lost about 403 million m3 of ice volume between 1986 and 2018. Based on the reconstruction of the glacier surface, the corresponding equilibrium line altitude, which is closely correlated with the mass balance, increased by about 70 to 80 m during this period. Our results also suggest that Abramov Glacier has become increasingly out of equilibrium with the climate over the last two decades. This is supported by repeated GPR measurements of the tongue area, which indicate a dramatic decrease in glacier area and ice volume over the period 1986–2018.

Field Validation of Deep-Learning-Based Ground Penetrating Radar Image Analysis for Advancing Subsurface Distress Detection

This research introduces an innovative method for detecting subsurface cracks within pavements by leveraging ground penetrating radar (GPR) technology in conjunction with advanced deep learning techniques. Its primary aim is to significantly improve the accuracy and efficiency of pavement assessment, particularly for operational and maintenance purposes. The proposed model, GPR-YOLOR (You Only Learn One Representation), extends the YOLOR framework and incorporates a region of interest within the top pavement layer to detect subsurface cracks. While the model can be trained with annotated data, the main challenge lies in validating results in the field because of the inability to visually inspect subsurface conditions and the high cost associated with direct coring. To overcome this challenge, we propose an alternative approach that utilizes the co-occurrence of surface cracks as pseudo labels, allowing for easy verification. To ensure that surface cracks correspond to subsurface cracks, the focus is exclusively on transverse cracks that develop in a bottom-up manner, such as fatigue and reflective cracks. Through this methodology, our GPR-YOLOR model achieves an F1 score of 0.72, with a precision of 0.76 and a recall of 0.68. The results from field validation underscore the effectiveness of the GPR-YOLOR model in accurately identifying subsurface cracks, highlighting its practical significance in conducting field condition assessments.