Synthetic Ground Penetrating Radar Data Research Articles

3D imaging and temporal evolution recognition of concrete internal defects based on GPR

Periodic inspections and recognition of temporal evolution for concrete internal defects are important for the long-term operation of civil engineering infrastructures. This study proposes a method integrating convolutional neural network (CNN) with maximum intensity projection (MIP) for 3D imaging and temporal evolution recognition of concrete internal defects. The proposed method utilizes MIP to process multiple ground penetrating radar (GPR) B-scans and generates 2D projected GPR B-scans containing spatial information. SegNet coupled with the Lovász softmax loss function is introduced to reconstruct 2D defects from projected GPR B-scans. Subsequently, 3D imaging result is reconstructed from 2D imaging results by 3D reconstruction module. Finally, the defect change extraction module realizes recognition of defect changes based on 3D imaging results at different time. The superiority of proposed method is validated based on both synthetic and real GPR data, which presents better recognition results and less time consumption than existing 3D CNN-based method.

Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study

Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-based inversion algorithms were used to reconstruct ground electrical properties (GEP) such as relative dielectric permittivity (RDP) from ground penetrating radar (GPR) data for contamination assessment, their inherent non-linear nature can introduce significant uncertainty and non-uniqueness into the reconstructed models. This is a challenge that traditional ML methods are not explicitly designed to address. In this study, a probabilistic hybrid technique has been introduced that combines the DeepLabv3+ architecture-based deep convolutional neural network (DCNN) with an ensemble prediction-based Monte Carlo (MC) dropout method. Different MC dropout rates (1%, 5%, and 10%) were initially evaluated using 1D and 2D synthetic GPR data for accurate and reliable RDP model prediction. The optimal rate was chosen based on minimal prediction uncertainty and the closest alignment of the mean or median model with the true RDP model. Notably, with the optimal MC dropout rate, prediction accuracy of over 95% for the 1D and 2D cases was achieved. Motivated by these results, the hybrid technique was applied to field GPR data collected over an AMD-impacted wetland near Silverton, Colorado. The field results underscored the hybrid technique's ability to predict an accurate subsurface RDP distribution for estimating the spatial extent of AMD-induced contamination. Notably, this technique not only provides a precise assessment of subsurface contamination but also ensures consistent interpretations of subsurface condition by different environmentalists examining the same GPR data. In conclusion, the hybrid technique presents a promising avenue for future environmental studies in regions affected by AMD or other contaminants that alter the natural distribution of GEP.

Adding realistic noise models to synthetic ground‐penetrating radar data

ABSTRACTCost‐effective computing capabilities have paved the road for the use of numerical modelling to develop advanced methods and applications of ground‐penetrating radar (GPR). Realistic synthetic data and the corresponding modelling techniques, respectively, should consider all subsurface and above‐ground aspects that influence GPR wave propagation and the characteristics of recorded signals. Critical aspects that can be realized in modern GPR modelling tools include heterogeneous and frequency‐dependent material properties, complex structures and interface geometries as well as three‐dimensional antenna models, including the interaction between the antenna and the subsurface. However, realistic noise related to the electronic components of a GPR system or ambient electromagnetic noise is often not considered, or simplified by assuming a white Gaussian noise model which is added to the modelled data. We present an approach to include realistic noise scenarios as typically observed in GPR field data into the flow of modelling synthetic GPR data. In our approach, we extract the noise from recorded GPR traces and add it to the modelled GPR data via a convolution‐based process. We illustrate our methodology using a modelling exercise, where we contaminate a synthetic two‐dimensional GPR dataset with frequency‐dependent noise recorded in an urban environment. Comparing our noise‐contaminated synthetic data with field data recorded in a similar environment illustrates that our method allows the generation of synthetic GPR with realistic noise characteristics and further highlights the limitations of assuming pure white Gaussian noise models.

Evaluation of starting model approaches and effective source wavelet variations for high-frequency ground-penetrating radar full-waveform inversion

High-frequency ground-penetrating radar (GPR) full-waveform inversion (FWI) can enhance the characterization of small-scale structures in the subsurface below the decimeter scale. We have investigated the potential and requirements to use FWI for higher-frequency data. Thereby, we focus on the two most important criteria to achieve reliable FWI results: adequate starting models that fulfill the half-wavelength criterion and the accuracy of the effective source wavelet. Therefore, we have defined a realistic reference model, generated synthetic GPR data sets (200, 450, and 700 MHz), and tested different standard ray-based starting model methods and frequency-hopping approaches to derive results close to our reference model. Although standard starting models provide good parameter reconstruction for lower-frequency data, a frequency-hopping approach is required for the 700 MHz data. In addition, we have seen that the reconstruction of the conductivity results is more sensitive to the presence of noise (25 dB) than the permittivity tomograms. The definition of the effective source wavelets is directly linked to the accuracy of the starting models; therefore, we investigate the effect on the FWI results for high-frequency data by varying the source wavelets in terms of starting time and/or amplitude. Considering the multiparameter nature of FWI, we observe that time shifts have a greater influence on the performance of the FWI than amplitude variations. Large time shifts of approximately 0.1 ns for the 700 MHz data may lead to the failure of the inversion, whereas amplitude variations (±5% of the maximum amplitude) affect the quantitative conductivity results only (no effect on permittivity) with an increased root-mean-square error of the data of up to 20%. Using a stochastically perturbed synthetic model, we determine an improved parameter reconstruction for higher frequencies. On the basis of our findings, we develop a workflow to obtain reliable results for high-frequency GPR FWI for future users.

An Automated GPR Signal Denoising Scheme Based on Mode Decomposition and Principal Component Analysis

Ground penetrating radar (GPR) signal processing has used mode decomposition methods such as empirical mode decomposition (EMD), variational mode decomposition, and their variations, but these methods are typically constrained by manual mode selection for the extraction of real signals and unwanted noises. In this study, we propose an automatic mode selection scheme for GPR signal denoising, which is demonstrated by the combination of the improved complete ensemble EMD (ICEEMD) and the multiscale principal component analysis (MSPCA). For GPR B-scan data, the intrinsic mode functions (IMFs) are automatically screened using our predefined criteria to remove noise after their spectra and the relationship between signals are first analyzed. Next, the nonlinear and nonstationary signals are adaptively decomposed into many time series by the proposed automated ICEEMD. Each A-scan dataset is then screened and subjected to MSPCA before being reassembled into the final denoised result. The synthetic and real GPR data prove our denoising scheme’s superiority over more established denoising techniques like MSPCA and median filtering.

A Multi-Path Encoder Network for GPR Data Inversion to Improve Defect Detection in Reinforced Concrete

Ground penetrating radar (GPR) has been extensively used in the routine inspection of reinforced concrete structures. However, the signatures in GPR images are reflected electromagnetic waves rather than their actual shapes. The interpretation of GPR data is a mandatory but time- and labor-consuming task. Furthermore, the rebars in the near-surface of concrete cause clutter in the GPR images, which hinders the interpretation of GPR data. This work presents a deep learning network to invert GPR B-scan images to permittivity maps of subsurface structures. The proposed network has a multi-path encoder which enables the network to leverage three kinds of GPR data: the original, migrated, and encoder–decoder-processed GPR data. Each type of processing method is designed to serve a different purpose: the original GPR images retain all the waveforms; the migration method intensifies the vertices of the subsurface anomalies; the encoder–decoder network suppresses rebar clutter and enhances the visibility of the defect echoes. The outputs of three processing methods are jointly used to interpret GPR B-scan images. We demonstrated the superiority of the proposed network by comparing it with a network with a single-path encoder. We also validated the proposed network with synthetic and experimental GPR data. The results indicate that the proposed network effectively reconstructs the defects in the reinforced concrete.

Ground Penetrating Radar in Coastal Hazard Mitigation Studies Using Deep Convolutional Neural Networks

There is a long history of coastal erosion caused by frequent storm surges in the coastal regions of Australia, which imposes great threats to communities and infrastructures alongside the beach. Old Bar Beach, New South Wales, Australia, is one such hotspot famous for its extreme coastal erosion. To apply remedial measures such as beach nourishment effectively and economically, estimating/reconstructing the subsurface hydrogeology over the coastal areas is essential. A geophysical tool such as a ground-penetrating radar (GPR) which works on the principle of reflecting electromagnetic (EM) waves, can be conveniently deployed to delineate the soil and rock profiling, water-table depth, bedrock depth, and the subsurface structural features. Here, DeepLabv3+ architecture based newly developed deep convolutional neural networks (DCNNs) were used to establish an inherent non-linear relationship between the GPR data and the EM wave velocity. The presented DCNNs have a lesser number of layers, a lesser number of trainable (learnable) parameters, a high convergence rate and, at the same time, achieve prediction accuracy comparable to that of well-established DeepLabv3+ networks, having high trainable parameters and a relatively low convergence rate. Here, firstly the DCNNs were trained and validated on small 1D datasets. Each dataset contains a 1D GPR trace and a corresponding EM velocity model. The DCNNs turned out to be quite promising in the 1D case, with training, validation, and testing accuracy of approximately 95%, 94%, and 95%, respectively. Secondly, 1D trained weights were applied to 2D synthetic GPR data for EM velocity prediction, and the accuracy of prediction achieved was approximately 95%. Seeing the excellent performance of the DCNNs in the 2D prediction case using 1D trained weights, a large amount of 1D synthetic datasets (approximately 1.2 million) were generated and gaussian noise was added to it to replicate the real field scenario. Thirdly, topographically corrected GPR data acquired over the Old Bar Beach were inverted using the DCNNs trained on 1.2 million 1D synthetic datasets to obtain the subsurface high-resolution, high-precision EM velocity, and εr distribution information to understand the hydrogeology over the beach. The findings presented in this paper agree well with the previous hydrogeological studies carried out using GPR. Our findings show that DCNNs, along with GPR, can be successfully used in coastal environments for the quick and accurate hydrogeological investigation required for the implementation of coastal erosion mitigation methods such as beach nourishment.

Combining ground-penetrating radar sections with different antenna frequencies including time-frequency domain noise suppression filters

Ground-penetrating radar (GPR) is one of the most useful geophysical methods that rapidly and efficiently provide high-resolution subsurface images depending on antenna frequencies and target depth. Recently, to enrich the interpretation of GPR sections, multifrequency sections have been combined at deeper and shallower levels in a profile. In this context, the techniques of combination and image quality improving processes of GPR data also have gained importance. For this, simple summation (SS) and time-shifted balanced summation (TSBS) techniques, frequency-domain local mean notch filter (FDLMNF), frequency-space ( f- x) filter, and automatic gain control (AGC) processes are sequentially applied within a workflow. Although the FDLMNF is used to effectively remove single-frequency spike-like noises without distortion around the notch frequency, the f- x filter is applied to suppress the random noise and improve the lateral traceability of the section, and AGC provides a statistically based gain recovery. In the proposed workflow, after generating synthetic and field GPR data with 100 and 250 MHz frequencies, data were combined using SS and TSBS techniques. Finally, FDLMNF, f- x, and AGC were applied to increase the image quality of this combined section. The result sections were evaluated and compared using the Fourier average amplitude and f- k spectra. According to the spectra, the TSBS technique provides good spectral balancing and offers wider spectral content. At the same time, it was observed that the image quality of the combined GPR sections increased significantly after applying FDLMNF, f- x, and AGC processes. As a result, when the sections resulting from SS and TSBS techniques are examined, the image enhancement processes that protect and strengthen the spectral bandwidth of the data are successful. Thus, the temporal resolution and spatial continuity of GPR sections also will be increased, and a better quality interpretation will be provided.

Imaging the internal structure of trunks via multiscale phase inversion of ground-penetrating radar data

Ground-penetrating radar (GPR) is a geophysical technique widely used in near-surface noninvasive detecting. It has the ability to obtain a high-resolution internal structure of living tree trunks. Full-waveform inversion (FWI) has been widely used to reconstruct the dielectric constant and conductivity distribution for crosswell applications. However, in some cases, the amplitude information is not reliable due to the antenna coupling, radiation pattern, and other effects. We have developed a multiscale phase inversion (MPI) method, which largely matches the phase information by normalizing the magnitude spectrum; in addition, a natural multiscale approach by integrating the input data with different times is implemented to partly mitigate the local minimal problem. Two synthetic GPR data sets generated from a healthy oak tree trunk and from a decayed trunk are tested by MPI and FWI. A field GPR data set consisting of 30 common-shot GPR data is acquired on a standing white oak tree ( Quercus alba); the MPI and FWI methods are used to reconstruct the dielectric constant distribution of the tree cross section. Results indicate that MPI has more tolerance to the starting model, noise level, and source wavelet. It can provide a more accurate image of the dielectric constant distribution compared to the conventional FWI.

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.

GPRInvNet: Deep Learning-Based Ground-Penetrating Radar Data Inversion for Tunnel Linings

A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data.

A statistics-based descriptor for automatic classification of scatterers in seismic sections

Discontinuities and small structures induce diffractions on seismic or ground-penetrating radar (GPR) acquisitions. Therefore, diffraction images can be used as a tool to access valuable information concerning subsurface scattering features, such as pinch outs, fractures, and edges. Usually, diffraction-imaging methods operate on diffraction events previously detected. Pattern-recognition methods are efficient to detect, image, and characterize diffractions. The use of this kind of approach, though, requires a numerical description of image points on a seismic section or radargram. We have investigated a new descriptor for seismic/GPR data that distinguishes diffractions from reflections. The descriptor consists of a set of statistical measures from diffraction operators sensitive to kinematic and dynamic aspects of an event. We develop experiments in which the proposed descriptor was incorporated into a pattern-recognition routine for diffraction imaging. The obtained method is useful for performing the automatic classification of image points using supervised and unsupervised algorithms, as a complementary step to Kirchhoff imaging. We also develop a new type of filtering, designed to address anomalies on the diffraction operators caused by interfering events. We evaluate the method using synthetic seismic data and real GPR data. Our results indicate that the descriptor correctly discriminates diffractions and shows promising results for low signal-to-noise-ratio situations.

On the usage of diffractions in ground-penetrating radar reflection data: Implications for time-lapse gas migration monitoring

Time-lapse ground-penetrating radar (GPR) measurements are used to image/monitor, for example, water infiltration, water table changes, and fluid/gas flow patterns. Although crosshole GPR is often preferred over surface-reflection GPR in such studies, its application is limited by the selected borehole geometry, which may be difficult to define in an optimal way, especially in experiments in which flow pathways are difficult to predict. Surface-reflection GPR data sets are generally faster to collect over relatively large areas and are therefore more efficient for covering the volume when a fast-moving tracer (e.g., gas) may infiltrate a heterogeneous subsurface medium. We have used a diffraction imaging approach on time-lapse surface-reflection GPR data to detect changes in radar wave velocity associated with gas (CO2) injected into a heterogeneous chalk succession. We initially test and evaluate the diffraction imaging approach on synthetic GPR data. Afterward, we apply the methodology to time-lapse GPR field data, and we evaluate the robustness of using information from diffractions in light of the obtained data quality. The synthetic tests indicate that diffractions provide essential information for delimiting the area affected by gas in the heterogeneous chalk section studied. Our field experiment shows that using the diffraction information alone allows for detection of gas-affected zones and, therefore, potential flow characteristics of gas movement. We infer that the CO2 flow patterns in our study most likely are determined by small-scale fractures rather than the porosity/permeability of the rock matrix. Moreover, the approach used may serve as an initial study for future more targeted experiments or for further detail-retrieving full-waveform inversion.

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.

A Noise Suppression Method of Ground Penetrating Radar Based on EEMD and Permutation Entropy

Ensemble empirical mode decomposition (EEMD) is a noise-assisted analysis method, which can overcome the mode mixing problem in empirical mode decomposition (EMD). However, the choice of signal intrinsic mode functions (IMFs) still depends on subjective experience in most cases. In order to solve the problem, a noise suppression method based on EEMD and permutation entropy (PE) is proposed according to the characteristics of ground-penetrating radar (GPR) signal. In the proposed method, first EEMD is used to decompose the GPR signal into a series of IMFs, and the PE of each IMF is calculated; then, the global threshold obtained by the second-order difference of PE of all IMFs is used to distinguish between noise IMFs and target IMFs; finally, the signal is reconstructed with target IMFs to remove the noise. The experimental results for synthetic and practical GPR data show that the proposed method can effectively remove the noise in the GPR signal and improve the resolution of the target.

Automatic hyperbola detection and fitting in GPR B-scan image

Detecting buried objects from ground penetrating radar (GPR) profiles often requires manual interaction and plenty of time. This paper presents an automatic scheme for buried objects detection and localization. First, a trained deep learning framework — Faster R-CNN with data augmentation strategy is applied to identify hyperbolic signatures from a gray GPR B-scan image, which is capable of not only recognizing whether a B-scan profile contains traces of buried object, but also detecting candidate hyperbola region. Then, the detected rectangle region is extracted and transformed to a binary image, a novel double cluster seeking estimate (DCSE) algorithm is proposed to separate object point cluster from each other and enable the identification of hyperbolic signatures. Subsequently, a column-based transverse filter points (CTFP) method is utilized to extract hyperbola fitting points automatically from the validated point cluster. Downward opening hyperbola fitting is carried out and their respective peaks are obtained finally. The proposed scheme is able to extract information from GPR B-scan images automatically and efficiently; it is validated significant performance in the analysis of synthetic and on-site GPR data sets. • Faster R-CNN with data augmentation strategy is applied to pick hyperbola region from the gray GPR B-scan image. • A novel double cluster seeking estimate (DCSE) algorithm is proposed to separate object point cluster from each other. • A column-based transverse filter points (CTFP) method is utilized to extract hyperbola fitting points automatically. • The experiments show significant performance in the analysis of synthetic and on-site GPR data sets.

Characterization of Subsurface Cavities using Gravity and Ground Penetrating Radar

Subsurface cavities occur naturally by dissolution of carbonates and evaporites or by human action, such as the construction of tunnels and tombs. They can be filled with air, water, sediments, or a combination. Gravity and ground penetrating radar (GPR) methods have been used widely to determine the location and size of subsurface cavities. The objective of this study is to present a quantitative approach to estimate the porosity and water saturation of cavity-filling materials from GPR and gravity measurements. The approach uses appropriate rock-physics models of the dielectric permittivity and density of a shallow cavity and estimates the porosity and water saturation inside the cavity by solving the two model equations simultaneously for these two variables. We test the proposed method using synthetic GPR and gravity data sets corresponding to three spherical-cavity models: air-filled, water-filled, and a partially-saturated sand filling. Results show that the method is accurate in retrieving the correct porosity within 0.76% error and water saturation within 2.4% error. We also apply the method on three published case studies over air-filled rectangular cavities. We found that the proposed method estimated the correct porosity and water saturation in one study but failed with the other studies. However, when the procedure was repeated with gravity values calculated from parameters reported in these studies, the proposed method estimated the correct porosity and water saturation accurately.

Scale and rotation statistic-based self-adaptive function for ground penetrating radar denoising in curvelet domain

Nonlinear and non-stationary ground penetrating radar (GPR) data for geophysics exploration are often mixed with various complex noise sources, such as random and coherent noise. Those complex noise sources are introduced by the acquisition system and other sources of measurement uncertainty. The data sets which are contaminated by the above noises have a serious influence on the accurate extraction of weak reflected signals and the effective identification of diffracted wave hyperbolic coaxial characteristics. The ignorance of the influence of noise will cause great errors in the interpretation of GPR data and the subsequent migration imaging with full waveform. The curvelet transform not only is related to position and frequency as compared with the wavelet transform, but also is controlled by the translation angle. With such a unique advantage, curvelets are used for ground roll whitening, coherent noise denoising and separation of overlapping events. The traditional curvelet transform denoising with a hard threshold function needs to give a reasonable threshold function control coefficient according to the noise level of GPR data. As a result, an appropriate choice of a hard threshold is a basic requirement, and this presents a challenging task in curvelet denoising. To overcome these shortcomings, an self-adaptive threshold function for GPR data denoising with curve transform is proposed in this paper. For detailing the reasonable control coefficient of the threshold function, the complex block threshold function algorithm is used to analyze the distribution of peak-signal-to-noise ratio value of the noisy synthetic GPR data contaminated with random and coherent noise by using the traditional threshold function curvelet transform. Based on the high order statistical theory, the accuracy distribution of the curvelet coefficient for useful signals in scale and rotation direction are correlatively stacked and determined by using the statistical self-adaptive function. And then the threshold range of noise removal components is given by the statistical self-adaptive function. The effectiveness of the proposed denoising algorithm for the noisy synthetic contaminated with different types of noise (i.e., Gaussian random and coherent), and field GPR data is demonstrated by comparing the denoising results via curvelet transform with those from traditional thresholding function. The presented denoising results by the statistical self-adaptive function is helpful and instructive for the accurate inference and interpretation of complex GPR data.

MCA-Based Clutter Reduction From Migrated GPR Data of Shallowly Buried Point Target

Reducing clutter is an important process in terms of improving the detection of targets when using ground penetrating radar (GPR), especially in the case of overlapping target responses and clutter. We present an alternative scheme to remove clutter from monostatic GPR data by combining migration and morphology component analysis (MCA). A simple phase shift migration is first performed on received B-scan data. In the migrated B-scan, clutter, and useful target responses exhibit different morphologies. The clutter appears to be horizontal stripes and the hyperbolic reflection from the target is refocused into an enhanced point-like shape. Subsequently, an overcomplete dictionary is constructed to discriminatively and sparsely represent each morphology based on the simulated GPR data. In particular, the 2-D undecimated discrete wavelet transform is used for the target component, and the 2-D curvelet atoms are used for the clutter. Then, the MCA technique is exploited to sparsely separate the two different components, which leads to the clutter reduction. The sparse representation of 2-D finite-difference-time-domain-based synthetic GPR data illustrates the validity of the overcomplete dictionary. In this paper, the proposed clutter removal method is tested on a synthetic model of point-like targets and real data from buried landmines. The migration concentrates the refracted signals from landmines to their origins, and the shapes of the landmines can be clearly defined. The results of the clutter reduction suggest that the proposed method is more effective than the method implemented directly on the B-scan data and the traditional mean subtraction.

Attenuation of random noise in GPR data by image processing

Random noise in ground penetrating radar (GPR) data affects the signal-to-noise ratio, blurs the details, and complicates reconnaissance of the useful information. Many methods with different advantages and disadvantages have been proposed to eliminate or weaken the random noise. We have reviewed basic principles of various signal processing techniques including the curvelet transform (CT), non-local mean (NLM), median, and mean filters to remove the random noise and compared their performances using synthetic and actual GPR data. The performances of the four filters were analyzed on synthetic GPR data both in time and frequency domains. On noisy synthetic data, results indicate that the CT filter performs better than NLM, mean, and median filters at attenuating random noise and improving S/N of the GPR data. On the real data, the performance of only the NLM and CT filters was investigated. Comparing the results clearly shows the CT filter robustness for the random noise attenuation and simultaneously its signal preservation.