Ground Penetrating Radar B-scan Research Articles

Reinforced concrete mapping using full-waveform inversion of GPR data

Mapping the location and dimension of reinforcing bars in concrete can be critical for assessing the structure and state of reinforced concrete. Concrete structures, such as bridge pilings or cell phone tower foundations, are integral to modern life. Ground penetrating radar (GPR) is commonly used for mapping rebar grids, but traditional GPR data processing techniques fail to provide reliable information on the diameter of bars. Full-waveform inversion (FWI) of surface-coupled common-offset GPR B-scans (profiles) over reinforced concrete improves estimates of rebar diameters over more conventional ray-based methods. The method applies a sparse blind deconvolution (SBD) technique to obtain the optimized source wavelet and a sparse representation of the subsurface reflectivity series. A ray-based analysis is then performed on the estimated reflectivity model to define the initial geometry model to start the FWI. Applying this method to a synthetic data set and two real data cases with 1 and 2.6 GHz center frequency antennas results in errors in the rebar diameter estimates of less than 11% for rebars with concrete cover of 7.5 cm or less. These results compare favorably with those obtained from other methods that require cross-polarized antennas or ancillary equipment. The synthetic model demonstrates that the combination of SBD and FWI also improves ray-based estimates of the concrete permittivity and conductivity.

Dip Filter and Random Noise Suppression for GPR B-Scan Data Based on a Hybrid Method in f - x Domain

Ground-penetrating radar (GPR) is a close-range remote-sensing tool applied in a great many near-surface projects for engineering or environmental purposes. In GPR B-scans, there may exist a variety of reflections and diffractions that corresponds to different structures and targets in the subsurface media, and the noise is always embedded. To assist in the interpretation, GPR B-scans can be generally divided into two parts according to the dip attribute of the reflections, where the sub-horizontal layers and dipping structures are properly separated. In this work, we extend the f - x empirical mode decomposition (f - x EMD) to form a semi-adaptive dip filter for GPR data. In f - x domain, each frequency slice is decomposed by EMD and reconstructed to form a dipping profile and a horizontal profile respectively, where the reflections at different dips are separated adaptively. Then the noises mixed in the dipping profile are further separated by rank-deduction methods in f - x domain. The above two-step scheme constitutes the hybrid scheme, which can separate the dipping structures, sub-horizontal layers, and most of the random noise in GPR B-scans. We briefly review the basics of the f - x EMD, and then introduce the derived hybrid scheme in f - x domain. The proposed method is tested by the synthetic data, the forward simulation data, and the field data, respectively.

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.

Efficient Detection of Buried Plastic Pipes by Combining GPR and Electric Field Methods

In this paper, an efficient plastic pipe detecting model is proposed, which combines the ground penetrating radar (GPR) and the electric field method. The model consists of the electric field locating model (EFLM) and the GPR B-scan image interpreting (GBII) model. Synchronized electric field and GPR data are collected through a data acquisition device dedicatedly designed for the swift and accurate estimation of buried plastic pipes. The EFLM estimates the approximate locations of underground plastic pipes from the electric field data quickly, separates a GPR B-scan image into segments, keeps the segments that might contain hyperbolas, and discards the irrelevant ones. Then, the GBII model interprets the depth and radius of the buried pipe in the kept segments. Our numerical simulations and experiments prove that by utilizing the EFLM, the 1-D electric field data could be processed quickly and the GPR B-scan image could be segmented with part of irrelevant pixels discarded, while hyperbolas in the kept image segments could be automatically and accurately fitted. With our proposed model, the depth and radius of the buried pipes could be efficiently obtained.

A Machine Learning Based Approach for Automatic Rebar Detection and Quantification of Deterioration in Concrete Bridge Deck Ground Penetrating Radar B-scan Images

Ground penetrating radar (GPR) is a non-destructive method (NDT) for subsurface object identification. Interpretation of GPR data is often done manually by an engineer, which is a time-intensive task and requires moderate to significant level of training. The authors proposed a novel machine learning based processing for automatic interpretation and quantification of concrete bridge deck GPR B-scan images. The proposed method is based on combination of image processing, machine learning (ML) data classification, data filtering, and spatial pattern analysis for quantification of deterioration in concrete bridge decks. For the first time, the authors introduced a dataset of 4,000 B-scan images cropped from real bridge deck GPR field data, named DECKGPRH1.0. The proposed method is tested on bridge deck GPR data collected from three bridges with different NBI (National Bridge Inventory) ratings. The results presented indicate that by implementing a ML based classifier and a fine tuned filter, the proposed approach provides a robust solution for automatic quantification GPR field data.

Deep learning-based underground object detection for urban road pavement

ABSTRACT Ground penetrating radar (GPR) is a promising non-destructive evaluation technique for detecting buried underground objects in urban area. Deep learning technique is recently being applied into this field to automate the GPR data interpretation. However, there is no proper technique that can reflect the uniqueness of urban road pavements. In this study, an underground object detection technique suitable for urban road pavement is proposed by using a statistically determined threshold amplitude and a large amount of GPR B-scan image libraries. An automated thresholding technique is newly developed based on the statistical distribution of GPR data. Deep learning technique is then applied to the reconstructed GPR data to detect underground objects in urban area. The proposed method is experimentally validated by field data collected on urban roads in Seoul, South Korea. In addition, its application possibility is also tested with full-size GPR data. The proposed method successfully emphasises the feature of underground objects and classifies hyperbola, manhole cover, layer interface and subsoil background.

An Automatic GPR B-Scan Image Interpreting Model

Ground-penetrating radar (GPR) has been widely used as a nondestructive tool for the investigation of the subsurface, but it is challenging to automatically process the generated GPR B-scan images. In this paper, an automatic GPR B-scan image interpreting model is proposed to interpret GPR B-scan images and estimate buried pipes, which consists of the preprocessing method, the open-scan clustering algorithm (OSCA), the parabolic fitting-based judgment (PFJ) method, and the restricted algebraic-distance-based fitting (RADF) algorithm. First, a thresholding method based on the gradient information transforms the B-scan image to the binary image, and the opening and closing operations remove discrete noisy points. Then, OSCA scans the preprocessed binary image progressively to identify the point clusters <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> with downward-opening signatures, and PFJ further validates whether the point clusters with downward-opening signatures are hyperbolic. By utilizing OSCA and PFJ, point clusters with hyperbolic signatures could be classified and segmented from other regions even if there are some connections and intersections between them. Finally, the validated point clusters are fitted into the lower parts of hyperbolas by RADF that solves fitting problems with additional constraints related to the hyperbolic central axis. By integrating these methods, the proposed model is able to extract information from GPR B-scan images automatically and efficiently. The experiments on simulated and real-world data sets demonstrate the effectiveness of the proposed model. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> A point cluster is a collection of points with the same class identification.

SEA ICE THICKNESS MEASUREMENT BY GROUND PENETRATING RADAR FOR GROUND TRUTH OF MICROWAVE REMOTE SENSING DATA

Abstract. Observation of sea ice thickness is one of key issues to understand regional effect of global warming. One of approaches to monitor sea ice in large area is microwave remote sensing data analysis. However, ground truth must be necessary to discuss the effectivity of this kind of approach. The conventional method to acquire ground truth of ice thickness is drilling ice layer and directly measuring the thickness by a ruler. However, this method is destructive, time-consuming and limited spatial resolution. Although there are several methods to acquire ice thickness in non-destructive way, ground penetrating radar (GPR) can be effective solution because it can discriminate snow-ice and ice-sea water interface. In this paper, we carried out GPR measurement in Lake Saroma for relatively large area (200 m by 300 m, approximately) aiming to obtain grand truth for remote sensing data. GPR survey was conducted at 5 locations in the area. The direct measurement was also conducted simultaneously in order to calibrate GPR data for thickness estimation and to validate the result. Although GPR Bscan image obtained from 600MHz contains the reflection which may come from a structure under snow, the origin of the reflection is not obvious. Therefore, further analysis and interpretation of the GPR image, such as numerical simulation, additional signal processing and use of 200 MHz antenna, are required to move on thickness estimation.

Time-frequency analysis of enhanced GPR detection of RF tagged buried plastic pipes

Ground Penetrating Radar (GPR) is a well-received non-destructive technique for the detection of underground utilities, such as water/gas pipes, sewers, power cables, and telecommunication ducts. However, radar signatures of plastic pipes are generally weak when the surrounding soils are attenuative and/or the pipe and soils have similar electromagnetic characteristics. In order to increase the radar visibility of these pipes, attaching Radio-Frequency (RF) tags to them is a useful method. In this paper we designed two types of Bowtie-Omega shaped RF tags. The finite element method (FEM) simulation and measurement of the designed tags show strong resonances in the GPR spectrum, and by conducting GPR B-scans stronger radar signatures are observed when the RF tags are buried in our 2.0×1.5×0.75 m tank filled with dry sand. Furthermore, we implement a simple processing procedure based on Short-time Fourier Transform (STFT), by which strong time-frequency domain response of the tags is clearly seen at those designed resonant frequencies, and disappear when tags are not inserted. The resulting detection of the hollow plastic pipe in both time and frequency domains due to the co-located tag is evidently enhanced.

Determining the Optimal Frequency of Ground Penetrating Radar for Detecting Voids in Pavements

PURPOSES : The objective of this study is to determine the optimal frequency of ground penetrating radar (GPR) testing for detecting the voids under the pavement. METHODS : In order to determine the optimal frequency of GPR testing for void detection, a full-scale test section was constructed to simulate the actual size of voids under the pavement. Voids of various sizes were created by inserting styrofoam at varying depths under the pavement. Subsequently, 250-, 500-, and 800-MHz ground-coupled GPR testing was conducted in the test section and the resulting GPR signals were recorded. The change in the amplitude of these signals was evaluated by varying the GPR frequency, void size, and void depth. The optimum frequency was determined from the amplitude of the signals. RESULTS: The capacity of GPR to detect voids under the pavement was evaluated by using three different ground-coupled GPR frequencies. In the case of the B-scan GPR data, a parabolic shape occurred in the vicinity of the voids. The maximum GPR amplitude in the A-scan data was used to quantitatively determine the void-detection capacity. CONCLUSIONS: The 250-MHz GPR testing enabled the detection of 10 out of 12 simulated voids, whereas the 500-MHz testing allowed the detection of only five. Furthermore, the amplitude of GPR detection associated with 250-MHz testing is significantly higher than that of 500-MHz testing. This indicates that 250-MHz GPR testing is well-suited for the detection of voids located at depths ranging from 0.5~2.0 m. Testing at frequencies lower than 250 MHz is recommended for void detection at depths greater than 2 m.

Proposing New Methods to Enhance the Low-Resolution Simulated GPR Responses in the Frequency and Wavelet Domains

To date, a number of numerical methods, including the popular Finite-Difference Time Domain (FDTD) technique, have been proposed to simulate Ground-Penetrating Radar (GPR) responses. Despite having a number of advantages, the finite-difference method also has pitfalls such as being very time consuming in simulating the most common case of media with high dielectric permittivity, causing the forward modelling process to be very long lasting, even with modern high-speed computers. In the present study the well-known hyperbolic pattern response of horizontal cylinders, usually found in GPR B-Scan images, is used as a basic model to examine the possibility of reducing the forward modelling execution time. In general, the simulated GPR traces of common reflected objects are time shifted, as with the Normal Moveout (NMO) traces encountered in seismic reflection responses. This suggests the application of Fourier transform to the GPR traces, employing the time-shifting property of the transformation to interpolate the traces between the adjusted traces in the frequency domain (FD). Therefore, in the present study two post-processing algorithms have been adopted to increase the speed of forward modelling while maintaining the required precision. The first approach is based on linear interpolation in the Fourier domain, resulting in increasing lateral trace-to-trace interval of appropriate sampling frequency of the signal, preventing any aliasing. In the second approach, a super-resolution algorithm based on 2D-wavelet transform is developed to increase both vertical and horizontal resolution of the GPR B-Scan images through preserving scale and shape of hidden hyperbola features. Through comparing outputs from both methods with the corresponding actual high-resolution forward response, it is shown that both approaches can perform satisfactorily, although the wavelet-based approach outperforms the frequency-domain approach noticeably, both in amplitude and shape of the outputted hyperbola response.

Ground penetrating radar imaging of water leaks from buried pipes based on back-projection method

In this paper, the application of ground penetrating radar (GPR) imaging for detecting the water leaks from buried pipes is examined. Experimental water leakage conditions for a shallowly buried plastic pipe are realized within the laboratory sand and outdoor soil mediums. The successive B-scan GPR measurements of the mediums are performed at various time instants while the water is leaking out of the pipe. The corresponding time-series B-scan images are reconstructed using the back-projection algorithm that we have specifically formulated for the subsurface GPR imaging applications. The signatures of the leak regions are then assessed by both direct interpretation of the resultant images and application of change detection procedures. The obtained results demonstrate the capability of GPR for locating the sources of the leaks accurately.

PRACTICAL ALGORITHMS TO FOCUS B-SCAN GPR IMAGES: THEORY AND APPLICATION TO REAL DATA

It is well known in B-scan ground penetrating radar (GPR) imagery that the underground scatterers generally exhibit defocused, hyperbolic characteristics. This is mainly due to the data collection scheme and the finite beam width of the main lobe of the GPR antenna. To invert this undesirable effect and obtain focused images, various migration or focusing algorithms have been developed. In this paper, we survey the performance of our recent focusing algorithms, namely; hyperbolic summation (HS) and frequency-wavenumber (w-k) based synthetic aperture radar (SAR) focusing. The practical usage of these focusing methods were tested and examined on both simulated and measured GPR data of various buried targets. The simulation data set is obtained by a physical optics shooting and bouncing ray (PO-SBR) technique code. Measurements were taken by a stepped frequency continuous wave (SFCW) radar set-up. Scattered C-band field data were measured from a laboratory sand box and from outdoor soil environment. The proposed focusing methods were then applied to the B-scan GPR images to enhance the resolution quality within these images. The resultant GPR images obtained with the proposed algorithms demonstrate enhanced lateral resolutions.

Accurate Determination of Underground GPR Wavefront and B-Scan Shape From Above-Ground Point Sources

While the determination of the wavefront shape radiated by ground-penetrating radar (GPR) in contact with the ground is trivial, the refraction of rays at the ground surface from a point source in the air makes accurate determination of the transmitted wavefront challenging. Impulse GPRs usually have their emitters in air, transmitting waves through a quasi-planar interface into the soil. The waves radiated initially in air can be approximated as circular, but once they enter the soil half-space, they propagate with a wavefront shape that resembles a hyperbola. The exact shape is derived and shown to be well approximated by a hyperbola with specific parameters depending on the relative dielectric constant of the half-space epsiv' ( = n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), the height of the source h, and the propagation time from source to wavefront multiplied by the wave velocity in the half-space p. A correction formula is provided to reduce the error between the approximate hyperbola and the exact shape. In addition, an equation for the shape of the B-scan contour of a point scatterer is derived.