3D Ground-penetrating Radar Data Research Articles

3D ground-penetrating radar data analysis and interpretation using attributes based on the gradient structure tensor

In near-surface geophysics, ground-penetrating radar (GPR) surveys are routinely employed in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. Thanks to recent technical developments in GPR instrumentation and antenna design, 3D surveys comprising several 100.000 traces can be performed daily. Especially in complex environments such as sedimentary systems, analyzing and interpreting the resulting GPR volumes is a time-consuming and laborious task that is still largely performed manually. In the last decades, several data attributes have been proposed to guide and improve such tasks and assure a higher degree of reproducibility in the resulting interpretations. Many of these attributes have been developed in image processing or computer vision and are routinely used, for example, in reflection seismic data interpretation. Especially in sedimentary systems, variations in the subsurface are accompanied by variations of GPR reflections in terms of amplitudes, continuity, and geometry in view of dip angle and direction. A promising tool to analyze such structural features is known as the gradient structure tensor (GST). Up to today, the application of the GST approach is limited to a few 2D GPR examples. Thus, we take up the basic idea of GST analysis and introduce and evaluate the corresponding attributes to analyze 3D GPR data. We apply the proposed GST approach to one synthetic and two field data sets imaging diverse sedimentary structures. Our results demonstrate that the proposed set of GST-based attributes can be efficiently computed in 3D and that these attributes represent versatile measures to address different typical interpretation tasks and, thus, help for an efficient, reproducible, and more objective interpretation of 3D GPR data.

Reflection characteristics of typical road defects in 3D GPR images for collapse mitigation

Road collapses pose dangerous threats to traffic safety in urban cities. Typically, these collapses result from under-road defects such as cavities, loose soil, and voids. With the development of multi-channel 3D ground penetrating radar (GPR) systems, GPR has become a routine method for inspecting the subsurface conditions of roads. However, the reflection characteristics of hidden defects in 3D GPR results have not been well studied due to the complexity of urban road structures. Consequently, accurately detecting all subsurface defects in GPR survey areas remains challenging. In this paper, numerical models of road structures with typical types of hidden defects are constructed, and their reflection characteristics in GPR B-scan profiles and C-scan depth slices are analyzed. Then, the field results obtained using a car-mounted 3D GPR system are presented to confirm the reflection characteristics of these hidden defects. Finally, the reflections of manhole covers and underground pipelines, which are the most prevalent forms of clutter in GPR images in urban environments, are compared with those of hidden defects. The findings of this paper will facilitate the accurate interpretation of field 3D GPR data for road inspections and the mitigation of road collapse accidents.

3D ground penetrating radar road underground target identification algorithm using time-frequency statistical features of data

3D ground penetrating radar is the main detection technology used to locate underground targets on urban roads, and the current underground target intelligent identification algorithms for this technology rely on two-dimensional images. Relying only on radar image texture as the identification feature, due to the limitations of single dimensionality and single feature utilization, leads to significant errors of these algorithms in identifying underground targets. For the above problems, an underground target depth time-frequency statistical feature identification algorithm based on 3D convolution using raw radar data is proposed. The detailed changes and overall trends of underground targets are extracted by multi-size 3D receptive fields. Meanwhile, the depth time-frequency statistical features of multiple position slices in the horizontal, vertical, crossed-vertical, left-right diagonal, and front-back diagonal directions of radar data are constructed at different layers. Multi-dimensional time-frequency statistical features are input to a fully connected classifier, and the final result is obtained by combining the classification results of the seven directions through the Bagging mechanism. This algorithm expands the feature dimension and direction of judging underground targets and strengthens the feature representation ability. It makes full use of the 3D ground penetrating radar data information and reflects the features of underground space from multiple angles, which improves the identification accuracy of underground targets.

Fast 3D ground penetrating radar simulations for glaciers

Modeling ground penetrating radar (GPR) reflection data on glaciers with methods that require the discretization of the full subsurface domain is extremely computationally expensive because of the combination of a large domain size and the comparatively short wavelength of the signal. To address this issue, we build on and extend a previously proposed approach based on the assumption of a homogeneous background medium (ice) in which various scattering objects (e.g., crevasses, channels, boulders) are embedded far from each other such that multiple scattering can be ignored. The glacier bed, below which no scatterers are assumed to exist, represents the lower limit of the modeling domain. With this method, the two-way propagation of the radar waves through the ice is simulated in a semi-analytical way, whereby scattering surfaces are represented with a set of planar elements of different electric and reflective properties, allowing a wide range of objects to be simulated. As we also take the antenna radiation pattern at the air-ice interface into account, this simple algorithm is able to produce realistic 3D GPR data in a fast and memory efficient way. In this study, we validate the presented algorithm with an analytical solution for a layered model, and we simulate radar data for a model of the Otemma glacier in Switzerland featuring a realistic topography of the glacier bed and a subglacial channel.

3D ground penetrating radar cavity identification algorithm for urban roads using transfer learning

3D ground penetrating radar (GPR) is the main method for the detection of underground cavities in urban roads. The number of road cavity samples detected by 3D radar is small, whereas the intelligent identification model require a large number of learning samples for model training, resulting in inadequate model training. This causes the model to be less accurate in identifying cavities, resulting in many misses and misjudgments. Given the above problems, combined with the detection characteristics of the vertical, the horizontal, and the crossed slices obtained in one detection process of 3D GPR, a 3D GPR cavity intelligent recognition model based on model-based transfer learning is proposed. Firstly, a large amount of 3D GPR data of urban road models with cavities are obtained through forwarding simulation. And the intelligent recognition model was pre-trained on the cavity detection data on three types of slices respectively. Then, through model-based transfer learning, a small amount of real underground cavity data is used to speed up the convergence speed of model training and optimize the structural parameters. It breaks through the limitation of the insufficient number of cavity samples for 3D radar detection on the intelligent learning model training, reduces algorithm training costs, and improves identification accuracy.

Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection

AbstractThe echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.

Estimating porosity distribution of a heterogeneous alluvial aquifer by GPU-accelerated 3D conditional stochastic inversion of common-offset GPR reflection data

Three-dimensional (3D) high-resolution and accurate characterization of the heterogeneity in an alluvial aquifer is critical to predicting the groundwater flow and contaminant transport. In this study, we extend the previous 2D conditional stochastic inversion method with respect to common-offset ground-penetrating radar (GPR) data to 3D for the estimation of 3D porosity distribution in a heterogeneous alluvial aquifer. First, 3D stochastic realizations of subsurface properties can be obtained using the fast Fourier transform moving average (FFT-MA) method, and the realizations are conditioned to borehole porosity measurements available in the 3D survey area, as well as to the geostatistical parameters derived from the borehole logs and the processed 3D GPR data. Next, the realizations are constantly modified and regenerated via a localized simulated annealing optimization strategy, which ultimately makes their corresponding synthetic data offer an acceptable fit to the GPR data. To accelerate the computational speed of 3D inversion, we design and implement the multi-GPUs multi-node parallelization of the 3D conditional stochastic inversion algorithm. The proposed 3D inversion algorithm is positively verified through the application to field GPR data collected at the Boise Hydrogeophysical Research Site near Boise, Idaho, USA. Our results indicate that the proposed 3D inversion method has the potential to effectively recover the fine-scale porosity distribution of a heterogeneous alluvial aquifer.

Analysis of 2D and 3D GPR data interpretation using continuous wavelet transforms: Case study from an archaeological test site

Ground-penetrating radar (GPR) is one of the most important techniques for obtaining high-resolution data in archaeological research, and it is becoming increasingly important. The continuous wavelet transform (CWT), which is non-numerical technique, gives an overcomplete representation of a signal by continuously varying the wavelet’s translation and scale parameters in the time series dataset. This paper focuses on the novel technique of integrating CWT and the wavelet transform maxima (WTM) to extract information from an archaeological test site in south-eastern China. For the characterization of archaeological features, we assessed the importance of dense and accurate data collection as well as GPR signal processing. The mathematical formulation and applicability of GPR attributes, particularly amplitude-based attributes, to identify and characterize archaeological buried targets are also discussed. GPR data is acquired using co-polarized and cross-polarized configurations with transverse-electric (TE) and transverse-magnetic (TM) broadside frequency plates at 100 and 200 MHz. Next, CWT was applied using six different wavelet levels, followed by amplitude comparison. The archaeological targets were successfully interpreted using peak amplitude and CWT. The proposed methodology has significantly improved data visualization and interpretation of GPR data, and it also gave us good results in identifying archaeological anomalies.

Fast Segmentation and Dynamic Monitoring of Time-Lapse 3D GPR Data Based on U-Net

As the amount of ground-penetrating radar (GPR) data increases significantly with the high demands of nondestructive detection methods under urban roads, a method suitable for time-lapse data dynamic monitoring should be developed to quickly identify targets on GPR profiles and compare time-lapse datasets. This study conducted a field experiment aiming to monitor one backfill pit using three-dimensional GPR (3D GPR), and the time-lapse data collected over four months were used to train U-Net, a fast neural network based on convolutional neural networks (CNNs). Consequently, a trained network model that could effectively segment the backfill pit from inline profiles was obtained, whose Intersection over Union (IoU) was 0.83 on the test dataset. Moreover, segmentation masks were compared, demonstrating that a change in the southwest side of the backfill pit may exist. The results demonstrate the potential of machine learning algorithms in time-lapse 3D GPR data segmentation and dynamic monitoring.

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.

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.

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.

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.

Frequency–Wavenumber Analysis of Deep Learning-based Super Resolution 3D GPR Images

This paper proposes a frequency–wavenumber (f–k) analysis technique through deep learning-based super resolution (SR) ground penetrating radar (GPR) image enhancement. GPR is one of the most popular underground investigation tools owing to its nondestructive and high-speed survey capabilities. However, arbitrary underground medium inhomogeneity and undesired measurement noises often disturb GPR data interpretation. Although the f–k analysis can be a promising technique for GPR data interpretation, the lack of GPR image resolution caused by the fast or coarse spatial scanning mechanism in reality often leads to analysis distortion. To address the technical issue, we propose the f–k analysis technique by a deep learning network in this study. The proposed f–k analysis technique incorporated with the SR GPR images generated by a deep learning network makes it possible to significantly reduce the arbitrary underground medium inhomogeneity and undesired measurement noises. Moreover, the GPR-induced electromagnetic wavefields can be decomposed for directivity analysis of wave propagation that is reflected from a certain underground object. The effectiveness of the proposed technique is numerically validated through 3D GPR simulation and experimentally demonstrated using in-situ 3D GPR data collected from urban roads in Seoul, Korea.

Beyond Amplitudes: Multi-Trace Coherence Analysis for Ground-Penetrating Radar Data Imaging

Under suitable conditions, ground-penetrating radar (GPR) measurements harbour great potential for the non-invasive mapping and three-dimensional investigation of buried archaeological remains. Current GPR data visualisations almost exclusively focus on the imaging of GPR reflection amplitudes. Ideally, the resulting amplitude maps show subsurface structures of archaeological interest in plan view. However, there exist situations in which, despite the presence of buried archaeological remains, hardly any corresponding anomalies can be observed in the GPR time- or depth-slice amplitude images. Following the promising examples set by seismic attribute analysis in the field of exploration seismology, it should be possible to exploit other attributes than merely amplitude values for the enhanced imaging of subsurface structures expressed in GPR data. Coherence is the seismic attribute that is a measure for the discontinuity between adjacent traces in post-stack seismic data volumes. Seismic coherence analysis is directly transferable to common high-resolution 3D GPR data sets. We demonstrate, how under the right circumstances, trace discontinuity analysis can substantially enhance the imaging of structural information contained in GPR data. In certain cases, considerably improved data visualisations are achievable, facilitating subsequent data interpretation. We present GPR trace coherence imaging examples taken from extensive, high-resolution archaeological prospection GPR data sets.

3D modeling of ground-penetrating radar data across a realistic sedimentary model

Ground-penetrating radar (GPR) is an established geophysical tool to explore a wide range of near-surface environments. Today, the use of synthetic GPR data is largely limited to 2D because 3D modeling is computationally more expensive. In fact, only recent developments of modeling tools and powerful hardware allow for a time-efficient computation of extensive 3D data sets. Thus, 3D subsurface models and resulting GPR data sets, which are of great interest to develop and evaluate novel approaches in data analysis and interpretation, have not been made publicly available up to now.We use a published hydrofacies data set of an aquifer-analog study within fluvio-glacial deposits to infer a realistic 3D porosity model showing heterogeneities at multiple spatial scales. Assuming fresh-water saturated sediments, we generate synthetic 3D GPR data across this model using novel GPU-acceleration included in the open-source software gprMax. We present a numerical approach to examine 3D wave-propagation effects in modeled GPR data. Using the results of this examination study, we conduct a spatial model decomposition to enable a computationally efficient 3D simulation of a typical GPR reflection data set across the entire model surface. We process the resulting GPR data set using a standard 3D structural imaging sequence and compare the results to selected input data to demonstrate the feasibility and potential of the presented modeling studies. We conclude on conceivable applications of our 3D GPR reflection data set and the underlying porosity model, which are both publicly available and, thus, can support future methodological developments in GPR and other near-surface geophysical techniques.

Mapping lateritic bauxite at Az Zabirah, Saudi Arabia, using ground-penetrating radar exploration method

This paper presents the three-dimensional (3D) data interpretation of a ground-penetrating radar (GPR) survey, in conjunction with the existing boreholes, covering the area of the Az Zabirah bauxite deposits, northern Kingdom of Saudi Arabia. The purpose of this study was to measure and map the volume of the bauxite deposits in the selected survey area, as well as to prove the success of the ground-penetrating radar exploration method in detecting and mapping the bauxite layer. The 3D GPR data interpretation detected the bauxite layer at different depths and of variable thickness. The interpretation of the GPR profiles show three distinct layers, namely sandstone, upper clay, and bauxite of variable thicknesses. Each zone has a different reflection pattern or radar facies, which aided in their differentiation. The calculated total volume of bauxite in the study area from the GPR survey and borehole data was 34,696 m3 and 34,101 m3, respectively. The minor difference in the calculated volumes between the two datasets was approximately 1%.

Searching for prehistoric small-sized graves in complex geoarchaeological conditions: Ayios Vasilios North Cemetery (Peloponnese, Greece)

Prehistoric cemeteries are important and unique sources of funerary and social practices from the early stages of civilization, but also a highly challenging environment for geophysical investigation. This study evaluates the results of a Ground Penetrating Radar (GPR) survey for the detection and mapping of simple, small-sized, box-shaped graves, placed at small depths at the Late Bronze Age Ayios Vasilios North Cemetery (Laconia, Peloponnese, Greece). Interpreted grave anomalies allowed post-survey excavations to verify and not miss existent graves, but also led to the exploration of several anomalies that were not graves. We highlight and analyze in detail anomalies possibly related to graves in plan- and profile-view, compare them to real structures from pre- and post-survey excavation findings, and search for solutions for an efficient identification of GPR anomalies with graves and increase of the effectiveness and dependability of GPR for grave searching in complex site conditions, which include strong variations between geological and anthropogenic structure. We propose a procedure for refining grave interpretation that utilizes the results of both initial and advanced processing of 2D and 3D GPR data. The results of our study will benefit the use of GPR as a non-invasive method to search for small-sized graves placed at small depths in complex subsurface conditions and extend GPR use to investigate the organization and function of prehistoric cemetery sites in an intensively used space. Our work enhances the literature relative to geophysical surveying of prehistoric cemeteries, while the methodology discussed in this case study provides a useful tool for cemetery site management.

Detection of Buried Roman Wall Remains in Ground‐penetrating Radar Data using Template Matching

AbstractWhereas in the last decades the acquisition and processing of archaeological ground‐penetrating radar (GPR) data have become mature, the interpretation is still challenging. Manual delineation in three dimensions is time consuming, and often the determination of an isosurface value is not straightforward. This paper presents a method designed specifically for the extraction of buried linear features such as wall foundations, based on template matching. First, the three‐dimensional (3D) GPR data cube is synthesized into a two‐dimensional (2D) slice. To achieve this, an energy slice based on a sufficiently large time window may often be appropriate, although in this study a combination with other attributes, for example based on phase symmetry, made weak anomalies more distinct. In the next step, we compute the 2D normalized cross‐correlation of the composite 2D slice and a number of templates with dimensions similar to the walls in the data set. Of the resulting correlation matrices, the highest correlation coefficient is kept for each pixel, if it exceeds a certain threshold. In this way, wall foundations are successfully mapped, but also many false detections are produced. The latter are greatly reduced in number by using a size threshold and discarding isolated features. The remaining regions are enclosed in bounding boxes, which after vertical extrusion can be used as a simplified 3D representation of the wall structures, and for the creation of a filtered isosurface. For the evaluation of our results, a manual interpretation was used. In the 2D case (i.e. when comparing the total area of the automatically mapped structures versus the manually delineated ones), both the detection rate and the correctness were ~77%. Slightly lower rates (~71%) were obtained in the 3D case (i.e. comparing volumes). Our method was applied to the GPR survey of a Roman villa in Kent, UK. Copyright © 2016 John Wiley & Sons, Ltd.

Multichannel 3D Ground Penetrating Radar - Advances in Civil Infrastructure Scanning

The scope of this paper is to highlight improvements in Ground Penetrating Radar (GPR) as an infrastructure condition assessment tool, in particular through the use of multichannel 3D GPR. Multichannel 3D GPR is a relatively new and alternative infrastructure scanning tool which can assist geophysicists and engineers in providing 100% sub-surface coverage of an investigation area, where site access is possible. Advantages of an increased level of subsurface coverage using multichannel 3D GPR includes providing the user with improved accuracy in highlighting and quantifying regions that may require further invasive testing for future maintenance programs and also possible long-term monitoring. This paper briefly discusses current applications of standard GPR for infrastructure condition assessments and how multichannel 3D GPR can improve knowledge of the sub-surface in these application areas. Visualisations of multichannel 3D GPR data outputs with interpretations have been presented to illustrate the improved subsurface information made available from this method. The example presented is an approximately 4 m long section of multichannel 3D GPR data acquired along the surface of a reinforced concrete-lined tunnel.