3D Ground Penetrating Radar Research Articles

Precise estimation of subsurface moisture content based on laboratory measurement and 3D GPR field survey

The Sponge City construction currently undergoing in China aims to enhance the moisture capacity of urban pedosphere, and subsurface moisture content (SMC) is proposed to evaluate the effect of improvement. Different ground-penetrating radar (GPR) techniques have been used in previous studies to estimate moisture content, however, very few has assessed the potential of combing 2D common offset (CO) GPR with 3D multichannel GPR to resolve SMC estimates at the complex filed scale. In this paper, modified velocity analysis of in-situ 3D GPR common-midpoint (CMP) datasets was used together with 2D GPR laboratory calibration procedure to investigate spatial moisture variation and the impact of surface pavement and soil heterogeneity on subsurface water dynamics. Precise and reliable moisture information under two field sites (i.e., sponge pavement site and asphalt pavement site) was obtained, which further allowed us to quantitative characterize SMC distribution and its associated dynamics within the shallow vadose zone, and most importantly to investigate the coupling mechanism between subsurface water fluctuations with surface pavement conditions and subsoil moisture capacity in urban near surface environment. Additionally, through contrastive analysis, the superior performance of novel sponge material to detain and transit rainwater was highlighted. Data and results presented in this research provide pioneer reference for Sponge City construction and other engineering applications.

Seasonal Impact on 3D GPR Performance for Surveying Yedoma Ice Complex Deposits

Ground-penetrating radar (GPR) is a popular geophysical method for imaging subsurface structures with a resolution at decimeter scale, which is based on the emission, propagation, and reflection of electromagnetic waves. GPR surveys for imaging the cryosphere benefit from the typically highly resistive conditions in frozen ground, resulting in low electromagnetic attenuation and, thus, an increased penetration depth. In permafrost environments, seasonal changes might affect not only GPR performance in terms of vertical resolution, attenuation, and penetration depth, but also regarding the general complexity of data (e.g., due to multiple reflections at thaw boundaries). The experimental setup of our study comparing seasonal differences of summertime thawed and winter- and springtime frozen active layer conditions above ice-rich permafrost allows for estimating advantages and disadvantages of both scenarios. Our results demonstrate major differences in the data and the final GPR image and, thus, will help in future studies to decide about particular survey seasons based on the GPR potential for non-invasive and high-resolution investigations of permafrost properties.

In situ time-zero correction for a ground penetrating radar monitoring system with 3000 antennas

The time-zero correction is an essential step in the data pre-processing of ground penetrating radar (GPR) measurements to obtain an accurate signal propagation time between transmitting and receiving antennas. For a novel custom GPR monitoring system with about 3000 antennas and corresponding transceiver structures placed around a soil sample (lysimeter), an in situ approach for the time-zero correction is required. In particular, unknown material properties between any pair of transmitting and receiving antennas prevent a conventional time-zero correction. We present and compare two calibration approaches, namely a pairwise and a mesh calibration, both utilizing the ability of the monitoring system to conduct reciprocal measurements between any pair of antennas. The pairwise calibration enables an individual calibration for any antenna pair, whereas the mesh calibration reduces the influence of the soil between antenna pairs compared to the pairwise calibration. The developed approach is verified by utilizing a mathematical model. Experimental results from a simplified setup show that the lysimeter filling has a negligible impact onto the calibration approach based on adjacent measurements for the mesh calibration. In addition, it is shown that a state of the art time-zero calibration can be used to measure the signal delays within the analog circuit of the measurement system with an accuracy of ±4 ps. The simulation results indicate that by using the developed concept, no prior air calibration between every possible antenna combination is necessary. Thus, this work provides a crucial contribution towards an automated in situ time-zero correction for 3D GPR monitoring systems with many antennas.

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.

Research on Void Signal Recognition Algorithm of 3D Ground-Penetrating Radar Based on the Digital Image

The three-dimensional ground-penetrating radar system is an effective method to detect road void disease. Ground penetrating radar image interpretation has the characteristics of multi-solution, long interpretation period, and high professional requirements of processors. In recent years, researchers have put forward solutions for automatic interpretation of ground-penetrating radar images, including automatic detection algorithm for subgrade diseases based on support vector machines, etc., but there are still some shortcomings such as training models with a large amount of data or setting parameters. In this article, a three-dimensional ground-penetrating radar void signal recognition algorithm based on the digital image is proposed, and the algorithm uses digital images to characterize radar signals. With the help of digital image processing methods, the images are processed by binarization, corrosion, expansion, connected area inspection, fine length index inspection, and three-dimensional matching inspection, so as to identify and determine the void signals and extract the void area volume index. The algorithm has been verified by laboratory tests and engineering projects, and the results show that the void identification algorithm can accurately identify the void area position; the error level between the measured values and the measured values of length, width, buried depth, and area is between 2.2 and 17.3%, and the error is generally within the engineering acceptance range. The volume index calculated by the algorithm has a certain engineering application value; compared with the support vector machine, the regressive convolution neural network, and other recognition methods, it has the advantage of not needing a large amount of data to train or modify parameters.

Research on the Dynamic Monitoring Technology of Road Subgrades with Time-Lapse Full-Coverage 3D Ground Penetrating Radar (GPR)

Road safety is important for the rapid development of the economy and society. Thus, it is of great significance to monitor the dynamic changing processes of road diseases, such as cavities, to provide a basis for the daily maintenance of roads and prevent any possible car accidents. The ground penetrating radar (GPR) technology is widely used in road disease detection due to its advantages of nondestructiveness, rapidness, and high resolution. Traditionally, one-time 2D GPR detection cannot obtain the 3D spatial changes of subgrades. Thus, we developed a road subgrade monitoring method based on the time-lapse full-coverage (TLFC) 3D GPR technique by focusing on solving the key problems of time and spatial position mismatches in experimental data. Moreover, we used the time zero consistency correction, 3D data combination, and spatial position matching methods, as they greatly improve the 3D imaging quality of underground spaces. Finally, the time-lapse attribute analysis method was used in the TLFC 3D GPR data to obtain detailed characteristics and an overall rule of the dynamic subgrade change. Overall, this research proves that TLFC 3D GPR is an optimal choice for road subgrade monitoring.

Using Geometry Data from Ground Penetrating Radar to Improve Pavement Layers Auscultation Using Seismic Surface Waves Inversion Method

In order to reduce the unknown parameters during the inversion of the dispersion curve in the aim to obtain more precise Vs and VP profiles, the Multi-Channel Analysis of Surface Waves (MASW) method was combined with the Ground Penetrating Radar (GPR) method. First, a 2D GPR profile was made on the pavement, then 1D MASW profiles spaced by 0.2 m apart in the perpendicular direction and centered on the intersection with the Radar profile. The GPR measurements, made with a 1600 Hz antenna, allowed to calculate the dielectric permittivities then velocities in order to convert the times profiles to depth. Thus, the thicknesses of the layers are directly read on the radar profile. MASW measurements were performed by simulating the Multi-Channel Simulation with One Receiver (MSOR) method in Land Streamer mode. Six 4.5 Hz pointless geophones are connected to the seismograph consisting of an Arduino Due microcontroller and a nano-computer type Raspberry Pi 4. The results of the dispersion analysis showed a fundamental mode located between approximately 70 Hz and 200 Hz and an inverted dispersion, characteristic of pavements with higher frequencies propagating at higher velocities. The results show that the integration of the number of layers and the thicknesses obtained from the GPR measurements in the inversion parameters makes it possible to obtain a more precise Vs and VP velocity profile. All profiles, seismic and radar, have shown that velocities decrease with depth. The detected heterogeneities appear to be related to differences in water content inside the pavement.

A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects

The forward full-wave modeling of ground-penetrating radar (GPR) facilitates the understanding and interpretation of GPR data. Traditional forward solvers require excessive computational resources, especially when their repetitive executions are needed in signal processing and/or machine learning algorithms for GPR data inversion. To alleviate the computational burden, a deep learning-based 2D GPR forward solver is proposed to predict the GPR B-scans of subsurface objects buried in the heterogeneous soil. The proposed solver is constructed as a bimodal encoder-decoder neural network. Two encoders followed by an adaptive feature fusion module are designed to extract informative features from the subsurface permittivity and conductivity maps. The decoder subsequently constructs the B-scans from the fused feature representations. To enhance the network's generalization capability, transfer learning is employed to fine-tune the network for new scenarios vastly different from those in training set. Numerical results show that the proposed solver achieves a mean relative error of 1.28%. For predicting the B-scan of one subsurface object, the proposed solver requires 12 milliseconds, which is 22,500x less than the time required by a classical physics-based solver.

3D modelling of asphalt concrete overlay based on GPR data

ABSTRACT For proper rehabilitation planning and appropriate technology selection for airport pavements, information on the layer thicknesses is important. Pavement layer thickness can be determined by ground penetrating radar (GPR). GPR data is usually displayed in distance vs thickness diagrams. These two-dimensional displays do not utilise the full potential of the three-dimensional (3D) information of GPR data. To appropriately represent the data, the display should be 3D, as well. The aim of this study is to present a process for creating a 3D model and spatial representation of asphalt concrete (AC) overlay thickness. The 3D model of the AC overlay surface is generated based on the Delaunay triangulation of interpreted GPR data. The 3D model is presented on contour and band maps, whose accuracy is evaluated by comparing the thicknesses displayed by the contour maps with those measured in core samples. The calculated mean relative error of the contour map is 7.2%. The band map is used to identify sections of equal thickness or to analyse predefined sections, to select the appropriate rehabilitation technology. The combination of the interpreted GPR data and Delaunay triangulation successfully compensates for the lack of 3D GPR, in terms of the spatial representation of layer thickness.

Integrating electrical resistivity tomography and ground‐penetrating radar methods to map archaeological walls near northern Ishtar gate, ancient Babylon city, Iraq

AbstractElectrical resistivity tomography (ERT) and ground‐penetrating radar (GPR) were collected on the eastern side of the northern Ishtar gate in ancient Babylon, Iraq, to locate the palace wall and other surrounding walls. Due to the presence of a low resistivity (highly conductive) top layer associated with brick rubble and other debris, the GPR reflection profiles show a high‐energy attenuation, but a series of processing and filtering steps produced coherent reflections of about 2 m depth. Profile analysis shows the geometry and layering of the walls and the surrounding matrix. With the ERT, the surface conductive zone produces various distortions in ERT inverse models, making identifying the features' lower boundaries difficult. Here, we suggest that instead of analysing these two data sets independently, the integration of both reveals not just the walls but their composition and defines material in the surrounding units. This integration shows how the interpretation of the shallow features on the 3D ERT maps is improved by comparison and interpretation in conjunction with the reflections visible on both reflection profiles and the 3D GPR amplitude slices. The orientation of these features and reflections emphasizes the existence of one series of buried walls at a depth of 90–150 cm. The thickness of these walls varies between 0.25 and 1 m. Another wall‐like feature is visible only on 3D ERT maps and not with the 3D GPR slices at 2 m depth, which indicates a thickness of 11 m. It is interpreted as the palace wall, which is consistent with earlier archaeological excavations. An analysis of the geometry and composition of both wall components, perhaps of different ages, and constructed for different reasons, indicates that some shallower walls may be the remains of rooms built as residences for soldiers, or they may belong to the other ruins of northern Ishtar gate.

3D ground-penetrating radar attributes to generate classified facies models: A case study from a dune island

Ground-penetrating radar (GPR) is a standard geophysical technique used to image near-surface structures in sedimentary environments. In such environments, GPR data acquisition and processing are increasingly following 3D strategies. However, the processed GPR data volumes are typically still interpreted using selected 2D slices and manual concepts such as GPR facies analyses. In seismic volume interpretation, the application of (semi-)automated and reproducible approaches such as 3D attribute analyses as well as the production of attribute-based facies models are common practices today. In contrast, the field of 3D GPR attribute analyses and corresponding facies models is largely untapped. We have developed and applied a workflow to produce 3D attribute-based GPR facies models comprising the dominant sedimentary reflection patterns in a GPR volume, which images complex sandy structures on the dune island of Spiekeroog (Northern Germany). After presenting our field site and details regarding our data acquisition and processing, we calculate and filter 3D texture attributes to generate a database comprising the dominant texture features of our GPR data. Then, we perform a dimensionality reduction of this database to obtain meta texture attributes, which we analyze and integrate using composite imaging and (also considering additional geometric information) fuzzy c-means cluster analysis resulting in a classified GPR facies model. Considering our facies model and a corresponding GPR facies chart, we interpret our GPR data set in terms of near-surface sedimentary units, the corresponding depositional environments, and the recent formation history at our field site. Thus, we demonstrate the potential of our workflow, which represents a novel and clear strategy to perform a more objective and consistent interpretation of 3D GPR data collected across different sedimentary environments.

Integrating Airborne Laser Scanning and 3D Ground-Penetrating Radar for the Investigation of Protohistoric Structures in Croatian Istria

We present the investigation of two rather ephemeral archaeological sites located in the municipality of Oprtalj/Portole (Croatian Istria) by means of integrated archaeological, geophysical and remote sensing techniques. The results obtained confirm the first interpretation of these contexts; a protohistoric burial mound and a small hillfort, respectively. We further obtained detailed information about both deposits through 2D and 3D remote sensing and geophysical studies that produced maps, volumes, profiles and cross-sections. At the first site, the volume reconstruction of both the inner stone core and the superimposed earth of the putative stone mound also allowed us to estimate the labour necessary to erect the structure. In conclusion, our study demonstrates that the integrated approach can be valuable not only to acquire novel data about the archaeological deposits but also to calibrate future investigations and to plan effective measures for heritage management, monitoring and valorization.

Comprehensive Geophysical Study at Wabar Crater, Rub Al‐Khali Desert, Saudi Arabia

AbstractInterest in impact craters on the earth's surface has increased worldwide and is being investigated by using remote sensing, geological, boreholes, geophysical, and laboratory measurements. These measurements are used to build dynamic models to study crater formation. In this work, the near‐crater sediments at the young Wabar crater field in Saudi Arabia have been investigated using magnetic, transient electromagnetic (TEM), seismic, and ground‐penetrating radar (GPR) methods. The main objectives of this research were to (a) explore the possibility of any remnant major pieces of the meteorite, (b) investigate the meteoroid direction, and (c) map the deformational structures associated with the meteorite impact. Our results show five different magnetic anomaly types and three layers in the subsurface. The maximum depth of deformation due to the impact of the meteorite is about 25 m as shown by the seismic travel time tomogram, the quasi‐2D TEM, and the 3D GPR model. TEM survey confirmed the geometrical characteristics of the major crater and located another small crater (known as Philby‐A). The magnetic survey shows no evidence of any remnant major pieces of the meteorite; however, it was used to trace ejecta material containing highly dilute magnetic material. The magnetic carrier is most likely spheres of metal incorporated in the black/green glasses. During the expedition, many small pieces of the meteoroid were found and collected for further geochemical analysis. Based on the geophysical findings, the meteorite direction was found to be from north to south.

Ground-penetrating radar imaging reveals glacier's drainage network in 3D

Abstract. Hydrological systems of glaciers have a direct impact on the glacier dynamics. Since the 1950s, geophysical studies have provided insights into these hydrological systems. Unfortunately, such studies were predominantly conducted using 2D acquisitions along a few profiles, thus failing to provide spatially unaliased 3D images of englacial and subglacial water pathways. The latter has likely resulted in flawed constraints for the hydrological modelling of glacier drainage networks. Here, we present 3D ground-penetrating radar (GPR) results that provide high-resolution 3D images of an alpine glacier's drainage network. Our results confirm a long-standing englacial hydrology theory stating that englacial conduits flow around glacial overdeepenings rather than directly over the overdeepening. Furthermore, these results also show exciting new opportunities for high-resolution 3D GPR studies of glaciers.

Masonry texture reconstruction for building seismic assessment: Practical evaluation and potentials of Ground Penetrating Radar methodology

Within the context of masonry texture identification and geometrical reconstruction, Ground Penetrating Radar (GPR) could emerge as an effective survey method, due to its high-resolution, scalable and non-destructive approach. This study has methodologically assessed operational advantages and weaknesses of the methodology when implemented for a seismic assessment purpose. In particular, the scope was to examine acquisition and post-processing strategies that could reliably highlight the presence or absence of specific construction features on which to develop a quantitative indication of the quality of the masonry. For this purpose, a high frequency 3D GPR acquisition on a plastered masonry wall incorporating different brick geometries, has been carried out. The acquisition was made along two different survey directions and analysing unfocussed and migrated radar slices. Thanks to a favourable antenna pattern alignment and achieved resolution enhancement, the focussed GPR data collected along the vertical mortar joints orientation have proven to be able to properly reconstruct the wall texture, providing therefore detailed and crucial information on its geometrical appearance.

On the moisture migration of concrete subject to high temperature with different heating rates

A better understanding of moisture migration in concrete at high temperature can play an important role to improve the fire-resistance and radiation-shielding capability of concrete structures. In this regard, within the numerical framework of Multi-physics Lattice Discrete Particle Model, the moisture clog in concrete during heating is defined as the moisture-saturated region between the so-called “water front” and “water back” (isosurfaces of saturation = 1). The moisture migration in concrete subject to slow and fast heating has been investigated by simulating experimental tests in which moisture has been monitored via different techniques, namely 3D Neutron Tomography and Ground-Penetrating Radar. An overall consistency between experimental and numerical results has been observed, indirectly proving the effectiveness of numerical modelling and experimental monitoring of moisture migration.

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

GPR Monitoring of Artificial Debonded Pavement Structures throughout Its Life Cycle during Accelerated Pavement Testing

The paper gives an overview of a ground penetrating radar (GPR) experiment to survey debonding areas within pavement structure during accelerated pavement tests (APT) conducted on the university Gustave Eiffel’s fatigue carrousel. Thirteen artificial defect sections composed of three types of defects (Tack-free, Geotextile, and Sand-based) were embedded during the construction phase between the top and the base layers. The data were collected in two stages covering the entire life cycle of the pavement structure using four GPR systems: An air-coupled ultra-wideband GPR (SF-GPR), two wideband 2D ground coupled GPRs (a SIR-4000 with a 1.5 GHz antenna and a 2.6 GHz-StructureScan from GSSI manufacturer), and a wideband 3D GPR (from 3D-radar manufacturer). The first stage of the experiments took place in 2012–2013 and lasted up to 300 K loadings. During this stage, the pavement structure presented no clear degradation. The second stage of experiments was conducted in 2019 and continued until the pavement surface demonstrated a strong degradation, which was observed at 800 K loadings. At the end of the GPR experiments, several trenches were cut at various sections to get the ground truth of the pavement structure. Finally, the GPR data are processed using the conventional amplitude ratio test to study the evolution of the echoes coming from the debonded areas.

Detection of road cavities in urban cities by 3D ground-penetrating radar

Cavities under urban roads have increasingly become a great threat to traffic safety in many cities. As a quick, effective, and high-resolution geophysical method, ground-penetrating radar (GPR) has been widely used to detect and image near-surface objects. However, the interpretation of field GPR data remains challenging. For example, it is hard to distinguish reflections caused by road cavities or other urban utilities with a conventional 2D GPR survey. The superiority of 3D GPR in data interpretation is demonstrated by a laboratory experiment. Two pipes and a glass-made cavity buried in a sandpit show similar hyperbolic reflections in the 2D GPR profiles; thus, they are difficult to discriminate. In contrast, their geometric shapes and dimensions are readily identified in the 3D image reconstructed from the synthetic 3D GPR data set. Based on these ideas, we have developed a car-mounted 3D GPR system with two antenna arrays oriented in different polarization directions and have detected more than 100 cavities in three Chinese cities over the past year. The field data of two such cavities are presented. As a result, the cavity depth, horizontal size, and height can be accurately estimated from the 3D GPR data set. Laboratory and field experimental results indicate that 3D GPR possesses potential in the detection and recognition of road cavities and utilities in the complex urban environment.