Ground Penetrating Radar Interpretation Research Articles

Improving GPR interpretation by applying image-enhancing attributes and machine learning techniques: A case study over Green Hill Cemetery in Frankfort, Kentucky

Ground-penetrating radar (GPR) enables noninvasive imaging in structural mapping applications such as unmarked grave detection. However, burial signatures can be cryptic and require expert analysis. This study investigates attribute enhancement and machine learning to automate identification of variable-signature graves at Green Hill Cemetery in Frankfort, Kentucky. Nine complementary seismic attributes were computed from the GPR envelope reflectivity volume to boost discontinuities and patterns indicative of shafts. Coherent energy, pseudofrequency, and similarity transforms showed optimal visualization enhancement. These volumes were input into unsupervised k-means and self-organizing map (SOM) machine learning models to cluster potential burial sites. Both methods accurately characterized strong anomaly reflections associated with apparent grave boundaries. However, limitations emerged in classifying subtler signatures that are likely linked to deteriorated or deeper interments. SOM clustering provided finer segmentation between noise and targets. Collectively, attributes amplified burial edges for easier recognition, while machine learning clustering expedited identification of most vault structures and unmarked sites. However, edge case discrimination remains a challenge. Results suggest that hybrid human-machine learning analysis can enhance efficiency over purely manual interpretation. With further method refinement, automated attribute and machine learning workflows show strong potential for accelerating GPR-based cemetery and archaeological mapping.

The challenges of signal interpretation of burials in ground‐penetrating radar

AbstractThe identification of unmarked graves and burials is one of most common applications of ground‐penetrating radar (GPR) in archaeology. Despite a high frequency of use and a long history of experimentation, there appears to be considerable variability on what indicates a burial in GPR data—likely a consequence of heterogeneity in geological contexts, age and in burial practices. Although general statements about uncertainty in GPR interpretation may be acceptable in archaeological applications, the interpretative process becomes more complicated when GPR is used to locate unmarked graves in culturally, politically and legally contested locations such as at former Indian Residential Schools (IRSs) in Canada. In this paper, we review international applications of the technique and identify trends and traits between the authors' use of GPR to identify burials. By categorizing the studies based on the GPR reflection signatures identified, our review demonstrates that there is modest consensus across the 77 documents reviewed for what represents a burial. Interrogating these findings, we identify a range of potential contributors to signal heterogeneity and outline potential steps forward to a higher confidence or more statistically robust identification of unmarked graves using GPR.

Karangkates Railway Tunnel Risk Analysis Based on Ground Penetrating Radar Data

The Karangkates railway tunnel in Malang Regency is more than half a century old and has undergone land use changes into settlements and highways. This study aims to identify cavities between the ground surface and the roof of the Karangkates tunnel using Ground Penetrating Radar (GPR) data. Primary data were obtained through GPR measurements and interviews with residents. Qualitative methods are used to analyze GPR interpretation data readings and risk analysis. Risk analysis is carried out to determine the location and area of cavities and compile long-term risk management. From the results of the GPR analysis, 6 points were found with protruding cavities, with a cavity depth between 8 to 13.4 meters. The location of such cavities is in residential areas and highways. Risk management measures appropriate to the analysis and observation of the research site are carried out to reduce the impact of risk. The results showed that the risk value was low. By implication, appropriate corrective and maintenance actions must be taken to mitigate risks and maintain public safety.

Application of Ground-Penetrating Radar with the Logging Data Constraint in the Detection of Fractured Rock Mass in Dazu Rock Carvings, Chongqing, China

Geologic interpretation results from conventional ground-penetrating radar (GPR) detection methods tend to have a certain degree of uncertainty. In order to improve the reliability of ground-penetrating radars in the detection of rock mass fissures in grottoes, this study proposes a ground-penetrating radar detection method with the logging data constraint, which is applied to detect the fractured rock mass in the Baodingshan Scenic Area of Dazu Rock Carvings, Chongqing, China. First, conventional logging and borehole televiewer data were compared and verified, yielding detailed lithological and wellbore fissure information. Next, electromagnetic wave velocity was calibrated using GPR profile and the depth of the stratigraphic interface determined by borehole data. Utilizing this calibrated velocity, we are able to accurately calculate the depth values of anomalies in GPR interpretation profiles. Subsequently, we compared the preliminary GPR interpretation profile with the borehole televiewer images. After eliminating false anomalies caused by interference, we obtained more reliable location information for detection targets such as fissures, fracture zones, and weak interlayers. The results of fissure detection in the Dazu Rock Carvings indicate that the detection results of ground-penetrating radar are verified and supplemented under the constraints of stratigraphic and well-wall fissure information obtained by logging. This effectively mitigates the influence of multiplicity and false anomalies of GPR detection on interpretation results. GPR with the logging data constraint enhances the accuracy of the fissure detection results, providing novel technical means for the protection and restoration of grotto relics.

Ground penetrating radar applied to subsurface culverts

ABSTRACT The failures of culverts can result in water and soil loss in the surrounding subsurface, leading to collapses. In densely constructed urban areas, buried culverts are inaccessible and invisible, making Ground Penetrating Radar (GPR) inspection a widely adopted nondestructive and efficient method. Previous research has employed artificial intelligence to automate GPR interpretation by identifying special radargram textures, but the irregular GPR responses of degraded subsurface soils present a challenge. Therefore, this study proposes a method for preliminary diagnosing large-scale culverts based on GPR wave attributes. The method comprises three parts: (1) evaluating soil compactness by estimating the tortuosity of GPR responses at the culvert top; (2) investigating soil moisture using the attenuation rate of GPR responses; and (3) overlaying the results of the incompact and moist soils to pinpoint potential degradations. Two long-distance culverts were inspected and diagnosed using the proposed method, and three abnormal sections were identified and confirmed by excavation evaluation. The case studies indicate that the proposed method can quickly provide reference for further in-depth investigation, thereby improving GPR survey efficiency and reducing workload by narrowing the investigation scope. Consequently, this study can facilitate large-scale GPR culvert surveys and further safeguard the water system.

Ground-penetrating Radar and Geotechnical Analyses to Investigate the Foundation Settlements of an Indiana House in NW Spain

ABSTRACT Settlements represent one of the most dangerous causes of degradation of the stability and preservation of buildings. This work presents a Ground-Penetrating Radar (GPR) survey aiming to investigate settlement problems that compromise the structural integrity of an Indiana-style house built in 1933–36 (Cabanas, northwest of Spain). From a simple visual inspection, it was possible to observe external damage like cracking, subsidence and bulging in the building façades, retaining walls and pavements. The GPR survey was conducted using a ProEx system with a 500 MHz antenna, which allowed for the production of an overall image of the subsoil until a depth of 3–4 m. The GPR data allowed for the detection of subsidence, voids and poor soil compaction, which made easier the identification of building settlements. The GPR interpretation was then validated with geotechnical prospection (sampling and dynamic probing superheavy (DPSH) tests), which showed good agreement between techniques. The better knowledge of this settlement phenomena was promising for the engineers and architects engaged in the design of conservation activities for such a singular architectural heritage.

GPR Mapping of buried monumental retaining walls at biblical Kiriath-Yearim near Jerusalem

ABSTRACT In 2019 a ground-penetrating radar (GPR) investigation was conducted at Kiriath-Yearim near Jerusalem, one of the largest Bronze and Iron Ages mounds in the highlands of the southern Levant. The main objective was to test an archaeological hypothesis regarding the existence of an Iron Age summit compound which was supported by monumental stone retaining walls. We used a wheeled GPR with simultaneous central frequencies of 200, 400, and 800 MHz. Despite attenuation in the topsoil, scattering by distributed rock fragments, and asphalt pavement, we were able to penetrate almost a meter and detect anomalies. Some of the GPR anomalies are consistent with the above-mentioned theory, meaning that their locations agree with data retrieved in nearby control trenches and with the plan of the site. The excavation findings, jointly with the GPR interpretation, indeed imply the existence of a rectangular monumental summit compound that dates to the 8th century BCE.

Integration of Geophysical Methods for Doline Hazard Assessment: A Case Study from Northern Oman

Subsurface formations with low compaction, often, due to the presence of underlying cavities, are potential sources of hazards. Thus, understanding the occurrence, properties, and extension of these weak zones poses a major concern in engineering geophysics. In this study, we examine the ability of geophysical methods to map weak areas over carbonates in Northern Oman. The weak zones are known to cause surface depression in many areas. The geophysical methods examined involve Ground-Penetrating Radar (GPR), Seismic Refraction Tomography (SRT), and Electrical Resistivity Tomography (ERT). This integrated geophysical survey was conducted near the Bimmah sinkhole, in the Quriya area, Northern Oman. The survey covers both an area with ground truth (low compaction sediments overlaying a cave) and a part with unknown subsurface properties. GPR velocity analysis using selected diffraction’s fitting helped to identify high-velocity anomalies that were attributed to the cavity. The GPR interpretation was calibrated with SRT and ERT. The former showed a clear drop in P-wave velocity and low ray coverage at the cavity zone, while the latter demonstrated high resistivity anomalies caused by the air filling the cavity. The scope was to examine the geophysical methods response, especially the GPR, and utilize the results of this preliminary approach for a wider exploration investigation in the area. The results from the study indicated that the GPR is capable to serve as a pioneer method in detecting the cavities. Hence, the GPR will cover large area in the site and the other two methods will be used as complementary for the final subsurface conditions’ evaluation.

3D ground-penetrating radar attribute classification: A case study from a paleokarst breccia pipe in the Billefjorden area on Spitsbergen, Svalbard

Ground-penetrating radar (GPR) is a method that can provide detailed information about the near subsurface in sedimentary and carbonate environments. The classical interpretation of GPR data (e.g., based on manual feature selection) often is labor-intensive and limited by the experience of the interpreter. Novel attribute-based classification approaches, typically used for seismic interpretation, can provide faster, more repeatable, and less biased interpretations. We have recorded a 3D GPD data set collected across a paleokarst breccia pipe in the Billefjorden area on Spitsbergen, Svalbard. After performing advanced processing, we compare the results of a classical GPR interpretation to the results of an attribute-based classification. Our attribute classification incorporates a selection of dip and textural attributes as the input for a k-means clustering approach. Similar to the results of the classical interpretation, the resulting classes differentiate between undisturbed strata and breccias or fault zones. The classes also reveal details inside the breccia pipe that are not discerned in the classical interpretation. Using nearby outcropping breccia pipes, we infer that the intrapipe GPR facies result from subtle differences, such as breccia lithology, clast size, or pore-space filling.

Interdisciplinary Investigations of the Neolithic Circular Ditch Enclosure of Velm (Lower Austria)

Middle Neolithic circular enclosures, known as Kreisgrabenanlage (KGA), are the oldest known monumental sites in Central Europe, dating roughly to 4850–4600/4500 BC. These large prehistoric monuments are mainly discovered by aerial archaeology and have been investigated by geophysical prospection and archaeological excavations since the 1960s. The site of Velm (Lower Austria) was discovered by aerial photographs in 2001. Due to its unusual location on a flat gravel plateau, the enclosure has become the object of intensive interdisciplinary research in recent years. In 2016, two motorized ground-penetrating radar (GPR) surveys were conducted, resulting in a detailed three-dimensional dataset visualizing the circular ditches, palisades and dwellings of an adjacent settlement. The high contrast between the gravel sediments and the humic earthen backfill of the ditches, palisades and individual postholes resulted in a highly detailed visualization of the Middle Neolithic monument. Based on this survey, selected structures were investigated by targeted archaeological excavations to evaluate the GPR results and to take samples for radiocarbon dating. This paper presents a synopsis of all the methods used. An integrated interpretation of aerial photo information, magnetometry and GPR is conducted, and it is shown to what extent these could be verified by the targeted archaeological excavation. By a detailed analysis of all applied archaeological prospection methods, it is now possible to interpret the monument in its entirety and confirm its dating to the Middle Neolithic Lengyel cultural complex.

Recognition of void defects in airport runways using ground-penetrating radar and shallow CNN

The interpretation of ground-penetrating radar (GPR) data usually depends on manual work, thus yielding unsatisfactory accuracy when processing complicated cases. To address this issue, the finite-difference time-domain method was adopted to explore the void-related features in GPR images, and guide the field survey to locate the void area. On this basis, a dataset including 811 void features was established from 10 airport runways using an 800 MHz ground-coupled GPR system. Data augmentation was used to enlarge the dataset and train four configurations of a mixed convolution neural network (CNN) model with feature extractors (ResNet18 and ResNet50) and object detectors (YOLOv2 and Faster R-CNN). To make the detection process automatic, an incremental random sampling (IRS) approach was used to generate images from GPR data and then fed into the trained model. The experimental results demonstrated that the shallow ResNet18-YOLOv2 with the IRS method is a promising strategy for void detection in airport runways.

Automatic modelling of urban subsurface with ground-penetrating radar using multi-agent classification method

ABSTRACT The subsurface of urban cities is becoming increasingly congested. In-time records of subsurface structures are of vital importance for the maintenance and management of urban infrastructure beneath or above the ground. Ground-penetrating radar (GPR) is a nondestructive testing method that can survey and image the subsurface without excavation. However, the interpretation of GPR relies on the operator’s experience. An automatic workflow was proposed for recognizing and classifying subsurface structures with GPR using computer vision and machine learning techniques. The workflow comprises three stages: first, full-cover GPR measurements are processed to form the C-scans; second, the abnormal areas are extracted from the full-cover C-scans with coefficient of variation-active contour model (CV-ACM); finally, the extracted segments are recognized and classified from the corresponding B-scans with aggregate channel feature (ACF) to produce a semantic map. The selected computer vision methods were validated by a controlled test in the laboratory, and the entire workflow was evaluated with a real, on-site case study. The results of the controlled and on-site case were both promising. This study establishes the necessity of a full-cover 3D GPR survey, illustrating the feasibility of integrating advanced computer vision techniques to analyze a large amount of 3D GPR survey data, and paves the way for automating subsurface modeling with GPR.

3D Visualization Techniques for Analysis and Archaeological Interpretation of GPR Data

The non-invasive detection and digital documentation of buried archaeological heritage by means of geophysical prospection is increasingly gaining importance in modern field archaeology and archaeological heritage management. It frequently provides the detailed information required for heritage protection or targeted further archaeological research. High-resolution magnetometry and ground-penetrating radar (GPR) became invaluable tools for the efficient and comprehensive non-invasive exploration of complete archaeological sites and archaeological landscapes. The analysis and detailed archaeological interpretation of the resulting large 2D and 3D datasets, and related data from aerial archaeology or airborne remote sensing, etc., is a time-consuming and complex process, which requires the integration of all data at hand, respective three-dimensional imagination, and a broad understanding of the archaeological problem; therefore, informative 3D visualizations supporting the exploration of complex 3D datasets and supporting the interpretative process are in great demand. This paper presents a novel integrated 3D GPR interpretation approach, centered around the flexible 3D visualization of heterogeneous data, which supports conjoint visualization of scenes composed of GPR volumes, 2D prospection imagery, and 3D interpretative models. We found that the flexible visual combination of the original 3D GPR datasets and images derived from the data applying post-processing techniques inspired by medical image analysis and seismic data processing contribute to the perceptibility of archaeologically relevant features and their respective context within a stratified volume. Moreover, such visualizations support the interpreting archaeologists in their development of a deeper understanding of the complex datasets as a starting point for and throughout the implemented interactive interpretative process.

Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.

Ground penetrating radar for buried utilities detection and mapping: a review

This paper presents a review on Ground Penetrating Radar (GPR) detection and mapping of buried utilities which have been widely used as non-destructive investigation and efficiently in terms of usage. The reviews cover on experimental design in GPR data collection and survey, pre-processing, extracting hyperbolic feature using image processing and machine learning techniques. Some of the issues and challenges facing by the GPR interpretation particularly in extracting the hyperbolas pattern of underground utilities have also been highlighted.

Waveform and Frequency Spectrum Property of Ground-Penetrating Radar for Coarse-Grained Weak Sulfuric Acid Saline Soil Subgrade

To accurately obtain the waveform and spectral characteristics of saline soil subgrade, on the basis of the provincial highway widening project of Ruoqiang-Weili county in Sinkiang, the field detection of coarse-grained weak sulfuric acid saline soil subgrade was carried out by using the type LTD-2100 ground-penetrating radar (GPR) to obtain the original measured files under different water content (ω) levels. IDSP analysis program was used to process the measured file and obtain the initial characteristics of waveform and frequency spectrum. To verify the initial characteristics, a rectangular model box was created in the laboratory with the field packing (the size was 1×0.8×0.8 m), and the water content of the model packing and temperature were manually adjusted. The model property of the waveform and frequency spectrum was obtained by detecting the model. A comparison of the initial property with the model property shows that under a positive temperature, the increase in ω will enhance the electromagnetic reflection, and when 8% 32%, the electromagnetic loss is significantly intensified as the line mapping shows typical snowflake-like characteristics, and the point mapping shows typical linear characteristics, the spectrum energy is concentrated, the main frequency distribution range is 20-65 MHz, and the low frequency characteristics are evident. The results can provide a relevant reference for GPR detection and interpretation of coarse-grained weak sulfuric acid saline soil subgrade.

Recognition method of typical anomalies during karst tunnel construction using GPR attributes and Gaussian processes

Ground penetrating radar (GPR) is a geophysical exploration technique used in a wide range of applications such as polar exploration, hydrogeological surveys, and archeological prospection. In recent years, it has been applied to tunnel construction for forecasting geological anomalies in front of the tunnel face. However, the application of GPR, forecasting geological anomalies in karst tunnels, has always been restricted by the problem of ambiguity. In the paper, based on the GPR investigation of highway tunnels in the karst areas in Guangxi, China, several GPR data of typical karst geological anomalies were selected as the analysis objective. The purpose of this study is twofold: to analyze and summarize the GPR attribute characteristics of typical geological anomalies by studies of laboratory tests and to introduce a new method for identifying different types of karst geological anomalies automatically in karst tunnel construction. Firstly, the attribute analysis technology of GPR is introduced, and the propagation law of electromagnetic waves in karst geological anomalies is studied from three perspectives: the time domain, frequency domain, and time-frequency domain by using physical laboratory simulations. Then, an intelligent identification model for typical karst geological anomalies is established in accordance with a Gaussian process (GP). Results from the Gaussian classification of field cases indicate that the type of typical karst geological anomalies can be effectively identified via comparing model prediction probability. The findings of this research allow for quantitative description of the GPR interpretation of typical karst geological anomalies in tunnel advanced prediction.

Interpretation of GPR (Ground Penetrating Radar) and soil profiles at selected veredas of the Formoso River Basin, Buritizeiro, Minas Gerais, Brazil

This work shows a correlation between the GPR (Ground Penetrating Radar) data and the geochemical distribution of the selected soil profiles in the Formoso river basin, to show the correlation between the vertical distribution of the soil units and the GPR profile. This interpretation is important to easily demonstrate the evolution of these important geomorphological units with the increase in human activities in the cerrado. Thus, it is extremely important to implement the necessary mechanisms to protect these environments as water sources in the region. This research can contribute to the creation of protected areas of sustainable use, specifically areas of ecological interest (ARIE) for the protection of paths considered strategic for the conservation of soil and water.

Mapping the response of volumetric soil water content to an intense rainfall event at the field scale using GPR

Ground-penetrating radar (GPR) is a convenient tool for volumetric soil water content (VSWC) estimation in hydrological and agricultural studies. Although case studies have been widely carried out, little attention has been paid to subsoil moisture estimates. In this research, we investigated three-dimensional soil moisture variation down to a depth of 1 m and the effect of rainfall events on spatial soil moisture dynamics. GPR surveying lines were conducted both before and after a heavy rainfall event to map the VSWC. Soil sampling and time domain reflectometry (TDR) probe data at different depths (20, 40, 60, 80, and 100 cm) were acquired. Our results demonstrated that there was a significant correlation between the dielectric constants and VSWCs at all depths. The established relationships for the different depth ranges had a low VSWC discrepancy when the dielectric constants ranged from 10 to 15. The effective range of each variogram was larger than 20 m, except for that of the 0–100 cm VSWC map after rainfall. In addition, the validation diagrams using corrected TDR values demonstrated relatively reliable VSWC maps. Approximately 89% of the variation in VSWC could be explained by the dielectric constants in the depth range of 0–40 cm, and VSWC predictions at this soil depth outperformed those at other depth ranges, with an overall RMSE of 0.027 m3 m−3 and R2 of 0.725. Furthermore, we also monitored the effect of precipitation on the accuracy of the VSWC prediction on shallow surfaces. Our study shows that three-dimensional soil moisture dynamics can be accurately estimated at the field scale by integrating GPR interpretation and spatial extrapolation methods.

The Assessment of Relative Permittivity on Diesel Vapour in the Moisture Content of Terap Red Soil by Ground Penetrating Radar

In a common agriculture resource, soil contamination monitoring is a prominent area of study. Nowadays, it is crucial to provide a database for the interpretation of ground penetrating radar (GPR) field data in monitoring soil contamination, such as diesel scatter migration. This study aims to assess the association between permittivity properties and soil water content (θw) for diesel contamination in Terap Red soil, which is classified as lateritic soil. Terap Red soil is an agro potential soil and available in more than 40% of distribution areas in Northern Malaysia (Agro-based State). In this research, 800 MHz shielded antenna GPR was applied for 24 hour measurement in a concrete simulation field tank, which was filled with Terap Red soil (1.5 m x 2.6 m x 1.5 m) located at Universiti Teknologi MARA (UiTM) Perlis, Malaysia. Embedded moisture content probe was simultaneously measured to monitor the response of volumetric water content in the contaminated soil. The GPR data were pre-processed and filtered by Reflexw 7.5. The calibrated Agilent Technologies Automated Vector Analyser (VNA) was used to verify the independent relative permittivity value from GPR. As a result, the evaluation of velocities and reflection of GPR data were influenced by the presence of diesel and contaminated vapour. A positive and significant correlation was obtained between relative permittivity and moisture content in the diesel-contaminated soil. In addition, a positive and strong linear regression analysis was also found between relative permittivity and moisture content. This analysis included an accurate total difference of root mean square error (RMSE) difference, which amounted to 0.04, with calibrated dielectric permittivity.