Ground Penetrating Radar Data Processing Research Articles

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task.

The S-transform is a fundamental time-frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0-3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.

Detection and delineation of cracks and voids in concrete structures using the ground penetrating radar technique

The accurate detection of early-age cracking and voids in concrete structures is vital for assessing their health and preventing deterioration, ultimately saving lives and resources. While Ground Penetrating Radar (GPR) offers a widely used method for concrete inspection and defect detection, its accuracy is subject to various limitations influenced by factors such as software, defect dimensions, moisture, and the analysis and interpretation of data. This study presents comprehensive techniques for detecting, delineating, and analyzing hidden cracks and voids in concrete using GPR, validated through laboratory testing and real-world applications. Introducing “DefectInspectoPy,” Python software, aims to address limitations in traditional GPR data processing software and defect detection methods. Results demonstrate the effectiveness of the proposed methods in accurately locating, analyzing, and delineating defects, aiding in determining coring locations for concrete structures. These findings have significant implications for enhancing safety, reducing maintenance costs, and preserving concrete infrastructure integrity.

Application of Variational Mode decomposition in GPR Detection of Oil Contaminated Soil

In this article, Ground penetrating radar (GPR) is used to investigate the contaminated soil by oil pollutants in oilfield operation area. Time frequency analysis method is applied to GPR data to reveal the changes of GPR signal frequency components with time variation, which is very important for data interpretation. We use variational mode decomposition (VMD) for time frequency analysis. It can decompose radar signal into several quasi-orthogonal intrinsic mode functions (IMFs) with limited bandwidth, and has strong robustness and local decomposition ability. We apply the VMD on field data acquired in the area with waste liquid contaminated soil in oilfield. The decomposed IMFs from GPR data shows that the signals from the contaminated soil is characterized by the strong energy and low frequency. Then the instantaneous attribute analysis of the IMF is carried out to delineate the scope and thickness of contaminated soil. The experimental results show that the limited bandwidth IMF with specific sparsity obtained by VMD method is more conducive to the processing and interpretation of GPR data.

A Comprehensive Review of Conventional and Deep Learning Approaches for Ground-Penetrating Radar Detection of Raw Data

Ground-penetrating radar (GPR) is a nondestructive testing technology that is widely applied in infrastructure maintenance, archaeological research, military operations, and other geological studies. A crucial step in GPR data processing is the detection and classification of underground structures and buried objects, including reinforcement bars, landmines, pipelines, bedrock, and underground cavities. With the development of machine learning algorithms, traditional methods such as SVM, K-NN, ANN, and HMM, as well as deep learning algorithms, have gradually been incorporated into A-scan, B-scan, and C-scan GPR image processing. This paper provides a summary of the typical machine learning and deep learning algorithms employed in the field of GPR and categorizes them based on the feature extraction method or classifier used. Additionally, this work discusses the sources and forms of data utilized in these studies. Finally, potential future development directions are presented.

Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases.

Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning methods. GPR data are complex, high-dimensional, and redundant, in particular with non-negligible noises, for which traditional machine learning methods are not effective when applied to GPR data processing and interpretation. To solve this problem, deep learning is more suitable to process large amounts of training data, as well as to perform better data interpretation. In this study, we proposed a novel deep learning method to process GPR data, the CRNN network, which combines convolutional neural networks (CNN) and recurrent neural networks (RNN). The CNN processes raw GPR waveform data from signal channels, and the RNN processes features from multiple channels. The results show that the CRNN network achieves a higher precision at 83.4%, with a recall of 77.3%. Compared to the traditional machine learning method, the CRNN is 5.2 times faster and has a smaller size of 2.6 MB (traditional machine learning method: 104.0 MB). Our research output has demonstrated that the developed deep learning method improves the efficiency and accuracy of railway subgrade condition evaluation.

Simulation of GPR B-Scan Data Based on Dense Generative Adversarial Network

Urban subsurface infrastructures, e.g., pipelines and roads, are aging with the expansion of modern cities. Benefiting from the capability of nondestructive detection, ground penetrating radar (GPR) has been widely applied to underground objects or disasters detection, and GPR B-scan images are employed by manual interpretation. While, this way of high subjectivity and uncertainty inevitably results in failure of detection. Meanwhile, the shortage of labelled images greatly impedes the automatization and intelligentization of underground disaster detection based on GPR. Many data simulation techniques, e.g., forward modelling, were used to augment images for training; however, the generated forward images were not similar enough to the real B-scan data, which makes recognition a challenging task. To address this problem, we proposed a novel B-scan image simulation method based on generative adversarial network to generate synthetic images for training detection networks. Our network utilizes DenseNet as the backbone network of generator to extract image features, and a weighted total variation regularization term to regularize the loss function of the network. The comparison and ablation experiments verified that our network could generate simulation images with high similarity to real GPR B-scan images. We believe that this work contributes to the intelligent processing and analysis of GPR data, and improves the efficiency of underground disaster detection.

GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection

Ground penetrating radar (GPR) is a well-established technique used in archaeological prospection and it requires a number of specialized routines for signal and image processing to enhance the data acquired and lead towards a better interpretation of them. Computer-aided techniques have advanced the interpretation of GPR data, dealing with a wide range of operations aiming towards locating, imaging, and diagnosis/interpretation. This article will discuss the novel and recent applications of machine learning (ML) and deep learning (DL) techniques, under the artificial intelligence umbrella, for processing GPR measurements within archaeological contexts, and their potential, limitations, and possible future prospects.

Particle Swarm Optimization-Based Variational Mode Decomposition for Ground Penetrating Radar Data Denoising

Ground Penetrating Radar (GPR) has become a widely used technology in geophysical prospecting. The Variational Mode Decomposition (VMD) method is a fully non-recursive signal decomposition method with noise robustness for GPR data processing. The VMD algorithm determines the central frequency and bandwidth of each Intrinsic Mode Function (IMF) by iteratively searching for the optimal solution of the variational mode and is capable of adaptively and effectively dividing the signal in the frequency domain into the many IMFs. However, the penalty parameter α and the number of IMFs K in VMD processing are determined depending on manual experience, which are important parameters affecting the decomposition results. In this paper, we propose a method to automatically search the parameters α and K optimally by Particle Swarm Optimization (PSO) algorithm. Then the signal-to-noise ratio (SNR) and root-mean-square error (RMSE) are used to judge the best superposition of the IMFs for data reconstruction, and the process is data-driven without human subjective intervention. The proposed method is used to process the field data, and the reconstruction data show that this innovative VMD processing can effectively improve the SNR and highlight the target reflections, even some targets not found in pre-processing are also revealed.

Development of a workflow for processing ground-penetrating radar data from multiconcurrent receivers

Ground-penetrating radar (GPR) systems with multiconcurrent sampling receivers can rapidly acquire dense multioffset GPR data, which are not feasible using typical common-offset (CO) GPR systems with a single fixed offset transmitter-receiver pair. Multioffset GPR data from these new multiconcurrent receiver systems have the potential to be used to create detailed subsurface velocity models and enhanced reflection sections. These are important features that can improve qualitative and quantitative interpretation of GPR data. To realize these benefits and to deal with the large amount of multioffset data generated by these new systems, we have developed an automated and customized data processing workflow. There are three key algorithms that we have developed as part of our workflow, which is crucial for processing large volume, multioffset GPR data so as: first, to efficiently correct and manage time misalignments from multiconcurrent receivers; second, to carry out trace balancing of common-midpoint data for semblance analysis; and third, to automate the velocity analysis step. We showcase our processing workflow using two field data sets acquired using a multiconcurrent sampling receiver GPR system consisting of one transmitter and seven receivers. The field data were collected at two different locations: a site using a system with a 500 MHz center frequency and another site using a system with a 1000 MHz center frequency. We have determined, with both data sets, that our processing workflow could produce automated stacking velocity fields and enhanced zero-offset reflection cross sections. These benefits increase the information that can be used for interpretation (compared with conventional CO data) and can form the basis of further processing steps such as migration. As the cost of these multiconcurrent sampling receiver systems decreases over time, we anticipate their use, and the acquisition of dense multioffset GPR data, to become much more commonplace.

Ballast fouling inspection and quantification with ground penetrating radar (GPR)

ABSTRACT Ground penetrating radar (GPR) has been applied for ballast layer inspection for two decades, mainly for the analysis of ballast layer fouling levels. However, some issues that affect the inspection quality remain unsolved, such as issues involving the GPR equipment quality (antenna) and the correlation between the GPR indicator and fouling index. With the aim of solving these two issues, in this paper, we investigated the difference between the results of two different antennas, the GPR data processing technique, indicators for the fouling level (by GPR signal processing) and the correlation between the indicators and fouling index (obtained by sieving). The results show that the antenna quality determines the inspection quality. The indicators can reflect the ballast layer fouling level, and they correlate the best with the fouling index (obtained by the percentage of particles passing through a 5 mm sieve size). This study is helpful for the future modification of railway ballast maintenance standards.

Uses of Green's function for enhancing the image resolution of Ground Penetrating Radar (GPR) data

Ground Penetrating Radar (GPR) data plays a vital role in the interpretation of shallow geological features. GPR produces a high-resolution image that is capable of capturing the subtle changes in the sub-surface geology. The work was conducted in the Baliapur region of Dhanbad district of Jharkhand state of India. The area is a part of the Chhota Nagpur plateau with a granitic gneiss complex. Baliapur area consists of multiple dykes formed mainly by igneous rocks and exposes to the surface. The geological ages of the dykes are from the Lower Jurassic to the Cretaceous-Eocene age. The knowledge on the evolvement of these dykes is highly significant for controlling the geology of the famous Damodar River Valley basin. The conducted research work has represented the detail of the internal structure of one such dyke in the area based on the high-resolution image of GPR data. The enhancement of image resolution was initiated based on the migration process such as FK and Kirchhoff after all necessary initial processing sequences of GPR data. However, the migration process has shown certain limitations to capture the internal structure of the igneous dyke. Because of such limitations, the Green's Function was introduced during the processing of GPR data to enhance image resolution through wave equation solution. The study shows the prominent features of the internal structure of the dyke after the introduction of Green's Function.

Nondestructive Inspection of Tree Trunks Using a Dual-Polarized Ground-Penetrating Radar System

In recent years, trees in European countries have been increasingly endangered by emerging infectious diseases (EIDs). In the United Kingdom, this has been observed to affect whole woodlands and forests, threatening the existence of some types of trees. Although quarantine measures have been taken to limit the spreading of such diseases, this has not yet been effectively controlled leading to millions of trees affected by EIDs. Ground-penetrating radar (GPR) has proven effective in identifying critical features on diseased trees for detection of EIDs spread. However, the irregular shape of tree trunks and their complex internal structure represent real challenges for conventional GPR measurements and data processing methodologies. In this research, a dual-polarised GPR system is used to detect internal decay in tree trunks using novel signal processing methodologies. A polarimetric correlation filter based on Bragg Scattering on a 3D Pauli feature vector and an arc-shaped Kirchhoff migration are discussed in detail. The proposed polarimetric correlation filter is utilised to enhance the signal-to-noise ratio (SNR) of B-scans due to bark and tree trunk high-loss properties of tree trunks. Meanwhile, an arc-shaped Kirchhoff migration algorithm is performed to counteract the influence of the bark irregularity. The proposed data processing framework is successfully validated with measurements on a real tree trunk, where cross-sections were subsequently cut for comparison purposes. Outcomes from the proposed methodology demonstrate a high consistency with the features observed on the tree trunk cross-sections, indicating the reliability of the proposed detection scheme for assessing tree-decay associated with EIDs.

GPRI2Net: A Deep-Neural-Network-Based Ground Penetrating Radar Data Inversion and Object Identification Framework for Consecutive and Long Survey Lines

Ground penetrating radar (GPR) enables infrastructure inspection using consecutive and long survey lines. However, the existing GPR data processing methods may lead to distortions or dislocations in the reconstructed shapes of the detected objects or even inconsistencies of the inverted dielectric values when performing inversion or object identification directly using GPR data obtained from the consecutive and long survey lines. To overcome these issues, this study proposed a novel deep neural network (DNN) architecture named GPRI2Net to simultaneously reconstruct the permittivity maps and categorize the object class labels from the GPR data of consecutive and long survey lines. GPRI2Net combined a convolutional neural network (CNN) based on DenseUnet and a recurrent neural network (RNN) based on bidirectional convolutional long short-term memory (Bi-ConvLSTM) to exploit the contextual information in and between the B-Scan segments extracted from the GPR data of a consecutive and long survey line. In addition, GPRI2Net performed inversion and object identification simultaneously using one network, which highly shared features between the two tasks and greatly reduced the computational complexity. Validation experiments were performed at two levels: first, using synthetic data based on the tunnel liner defects model and then using a sandbox model test in a realistic scenario. The results demonstrated that GPRI2Net can reconstruct consecutive permittivity maps and categorize the object class labels from GPR data with different dominant frequencies and survey line lengths and achieved a superior performance using the synthetic data compared to several other methods. Moreover, GPRI2Net also achieved satisfactory results using real-world GPR data.

Impact and Removal of Ground-Penetrating Radar Vibration on Continuous Asphalt Concrete Pavement Density Prediction

Ground-penetrating radar (GPR) could be implemented for continuous asphalt concrete (AC) pavement density predictions during compaction process. However, vibrations during survey and the complexity of GPR data processing may impede its application. GPR antenna vibration affects the collected data, which could be mitigated through profile smoothing using decomposition approaches. A signal-processing technique is proposed for filtering vibration noise to allow for continuous AC density prediction from GPR data. During constant speed survey, low-pass filter cutoff frequency and length may be used during signal processing for profile smoothing. Nonstationary profile caused by inconsistent speed survey could be smoothed using wavelet transform. Empirical mode decomposition is suggested for utilization when sudden braking/accelerating occurs. Field tests using roller-mounted antennas were used to validate the process.

Improving GPR Imaging of the Buried Water Utility Infrastructure by Integrating the Multidimensional Nonlinear Data Decomposition Technique into the Edge Detection

Although ground-penetrating radar (GPR) is effective to detect shallow-buried objects, it still needs more effort for the application to investigate a buried water utility infrastructure. Edge detection is a well-known image processing technique that may improve the resolution of GPR images. In this study, we briefly review the theory of edge detection and discuss several popular edge detectors as examples, and then apply an enhanced edge detecting method to GPR data processing. This method integrates the multidimensional ensemble empirical mode decomposition (MDEEMD) algorithm into standard edge detecting filters. MDEEMD is implemented mainly for data reconstruction to increase the signal-to-noise ratio before edge detecting. A quantitative marginal spectrum analysis is employed to support the data reconstruction and facilitate the final data interpretation. The results of the numerical model study followed by a field example suggest that the MDEEMD edge detector is a competent method for processing and interpreting GPR data of a buried hot spring well, which cannot be efficiently handled by conventional techniques. Moreover, the proposed method should be readily considered a vital tool for processing other kinds of buried water utility infrastructures.

An Improved GPR Method Based on BP and RPCA for Tunnel Lining Defects Detection and Its Application in Qiyue Mountain Tunnel, China

Tunnel lining defects are one of the most common problems that tunnels experience during operation, and they can pose severe safety risks. The most popular nondestructive testing method for detecting tunnel lining defects is ground penetrating radar (GPR), one of the basic geophysical applications. However, detection responses might differ significantly from the real shape of tunnel lining defects, making it challenging to identify and interpret. When data quality is poor, interpretation and identification become more challenging, resulting in a high cost of tunnel repairs. The improved back projection (BP) imaging and robust principal component analysis (RPCA) are used in this work to offer a GPR data processing method. Even in the event of poor data quality, our method could recover GPR responses, allowing the shapes and locations of tunnel lining flaws to be clearly depicted. With BP imaging, this approach recovers the tunnel defects’ responses to better forms and positions, and with RPCA, it further isolates the target imaging from clutters. Several synthetic data demonstrate that the approach presented in this work may successfully repair and extract the positions and forms of lining defects, making them easier to identify and comprehend. Furthermore, our technique was used to GPR data gathered from the Qiyue Mountain Tunnel in China, yielding more accurate findings than the traditional method, which was validated by the actual scenario to illustrate the efficiency of our method on real data.

Integrating Ground Penetrating Radar, Induced Polarization and Aerial Photograph to Analyze Land Subsidence in Borehole Mining Operation Area: A Case Study from South Bangka Island

The remaining alluvial tin reserves around large ex-mining area can be an opportunity to optimize the potential reserves using a small-scale mining system. One of the latest breakthroughs in the mining system for the remaining alluvial tin reserves on land is the borehole mining (BHM) method. However, mining operations using BHM have several environmental impacts especially related to its former holes. This study aims to identify the changes in geometry of the former holes both on the surface and below the surface through direct field observations and measurements using geophysical methods. The study site is in the large ex-mining area of Air Nudur Block in South Bangka Regency, Bangka Belitung Islands Province. The geophysical methods used to identify the changes in the geometry of the BHM holes are GPR (ground penetrating radar) and IP (induced polarization). The GPR method has high resolution subsurface visual capabilities at shallow depths and this method can distinguish between stable and unstable layers. The IP method is used to determine the overburden layer and the condition of the alluvial layer where some of it has become a hole. These two methods will mutually reinforce in interpreting the change in the geometry of the BHM holes. Changes in hole diameter and depth were measured directly in the field within 2 (two) months from the completion of mining operations using the BHM method followed by the periodic topographic measurements using UAV (unmanned aerial vehicle). The results of observations on changes in the elevation of the ground surface around the former BHM hole indicated that the land level subsided by about 1 m. Meanwhile, the results of GPR data processing showed changes in the hole geometry with the hummocky radargram pattern into chaotic. Interpretation of IP data processing shows resistivity values of 250 - 1700 ohm.m and chargeability of 15 - 25 ms in the disturbed alluvial layer around the former BHM holes.

Subsoil Recognition for Road Investment Supported by the Integration of Geodetic and GPR Data in the Form of a Point Cloud

During road construction investments, the key issue affecting the structure’s safety is accurate subsoil recognition. Identifying subsoil variability zones or natural voids can be performed using geophysical methods, and ground-penetrating radar (GPR) is recommended for this task as it identifies the location and spatial range karst formations. This paper describes the methodology of acquisition and processing of GPR data for ground recognition for road investment. Additional subsoil research was performed after karst phenomena were identified in the investment area, formations not revealed by geological recognition from earlier studies during the pre-design stage. Mala Ramac CU II radar with a 250 MHz antenna and a Leica DS2000 with 250 and 700 MHz antennas with real-time geopositioning were used to obtain the data. Regarding GPR data postprocessing, we present a method of converting spatial visualization into a point cloud that allows for GPR and geodetic data integration and confrontation. This approach enabled us to determine the locations of control trenches, the results of which were used for material validation, which is necessary to improve the reliability of subsoil recognition. The results showed a high correlation between the recorded GPR signals and the subsoil structure. Additionally, differences in the quality of results for measurements conducted before laying supporting layers with slag and on the completed road structure surface are illustrated.

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.

Resolution enhancement of deconvolved ground penetrating radar images using singular value decomposition

Sharpening ground-penetrating radar (GPR) images remains a persistent challenge. Two approaches in particular—singular value decomposition (SVD) and sparse blind deconvolution (SBD)—have been shown to effectively denoise GPR images and resolve a reflectivity model, respectively. To our knowledge, these approaches have not been used to process GPR data from archaeological sites. This paper highlights the treatment of a sample GPR profile acquired over two graves in sandy soil. SVD can remove user-specified components of the GPR image such as the direct wave, horizontal banding, and ringing from surface features. These are removed via a low order eigenimage filter in which the decomposed GPR data is reconstructing without the eigenimages containing them. This type of filter significantly alters the frequency content and total amplitude, which may or may not negatively influence subsequent filtering techniques or data analysis. Thus, we use SBD to investigate the influence of eigenimage filtering. The eigenimage-filtered GPR image returns a more physically meaningful reflectivity model compared to SBD of the original GPR profile. We additionally test the common approach ‘denoising’ GPR data by also omitting higher-order eigenimages from the reconstructed GPR image. The resulting reflectivity model was less sparse indicating that higher-order eigenimages contain signal information critical to deconvolution. To test the effectiveness of other filtering steps with SVD filtering, we compare the migration of the original GPR profile against that of an eigenimage-filtered image. The migrated filtered image, particularly if higher-order eigenimages are preserved, provides more spatially constrained point reflectors regardless of the migration algorithms used compared to the migrated original profile.