Noniterative techniques for GPR imaging through a nonplanar air-ground interface

Anomaly detection of internal defects in underground engineering structures is critical. This paper proposes an unsupervised deep learning image-to-image translation method tailored for ground penetrating radar (GPR) images. The proposed model can translate real-world GPR images to simulated ones. In this manner, labeling real GPR images is not necessary, and only the target detection model trained on simulated GPR images is required to directly identify defects in real GPR images. The unsupervised deep learning network introduces geometry-consistency constraints into the CycleGAN, which largely prevents the problem of semantic distortion in translation. Validation of the proposed method was performed using GPR data collected in various scenarios using GPR of different center frequencies and manufacturers. Moreover, to verify its adaptability and feasibility for defect recognition, commonly used deep learning-based defect recognition methods, which were trained only on simulated GPR images, were used to detect the translated GPR images. The findings indicate that the type and location of internal defects in translated GPR images can be accurately identified using the proposed method.

This document analyses the performance of subspace signal processing techniques applied to ground penetrating radar (GPR) images in order to reduce the amount of clutter and noise in the measured GPR image. Two methods considered in this work are Principal Component Analysis (PCA) and Independent Component Analysis (ICA). An approach to combine those two techniques to improve their effectiveness when applied to GPR data is proposed in this paper. The experiments performed to gather GPR data and evaluate proposed algorithms are also described. The aim of undertaken experiments is to replicate conditions found in water reservoirs where cracks and holes in the reservoir foundations and joints cause excessive water leakages and losses to water companies and the UK economy in general. Performance of implemented algorithms is discussed and compared to the results achieved by a highly skilled human - GPR image analyst.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

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

Intensive interferences processing for GPR signal based on the wavelet transform and F-K filtering

The conventional method, i.e., the common middle point (CMP) method, has been used for many years for estimating the depth and permittivity of layered media from ground-penetrating radar (GPR) data. However, the CMP method results in noticeable errors in thickness and permittivity readings with the increase of antenna separation. To improve the measurement accuracy, a new mathematical model is presented, covering GPR measurement in one- and two-layer cases. In this model, we first check all the possible wave paths when the GPR signal propagates in the multilayer environment. We not only consider the effects from the air-ground interface but also introduce a ray-path-searching process in the GPR measurement using Fermat's shortest path law. The shortest path is then used in the process of GPR data inversion in order to calculate the depth and permittivity of each layer. Finally, we use the transmission-line matrix (TLM) method to simulate the propagation of a GPR signal in the multilayered formation. A time-sequence image that was produced by the finite-difference time-domain method has also been used to explain this presented model. By comparing the numerical simulation results with the measured results, it is found that the estimated layer thickness and permittivity by the new model agree well with the simulated results. It proves that the new model is more accurate and closer to the real measured situation.

This paper presents some aspects of the time propagation of underground water leakage in controlled laboratory conditions using a drilled polyvinyl chloride (PVC) pipe and interpreting ground penetrating radar (GPR) images. GPR pre-processed images are interpreted for easy identification and extraction of surfaces and volumes of water leakage. Finally, the temporal evolution of a water leak is shown using 3D models based on interpretation of GPR images. Water volumes obtained using this approach can be easily observed by personnel who lack highly specialized training in the analysis of raw data. The results of this study are promising and can help develop techniques to validate non-destructive models for the identification, distribution, and prediction of water leaks in water supply systems using GPR.

Recently, compressed sensing (CS) has been widely used in ground penetrating radar (GPR) imaging due to its low sampling time, high quality performance and high quality underground imaging results. There are always a large number of matrix operations involved in CS based GPR imaging process, which takes a long time. Traditionally, all scanning area was imaged by this CS imaging method. However, with the imaging scene becoming larger, the measurement matrix and the data dictionary in CS imaging model expand, which increases the time of CS-based imaging process. Thus, this paper proposes a fast CS based GPR imaging method to reduce the imaging time. Specifically, the geometric characteristics of the B-scan image can firstly be used to extract the region of interest (ROI) where the target may exist in a layered medium. Then the GPR imaging based on CS can be realized in ROI, which can reduce the imaging area and imaging time. Experimental results present that the method can effectively reduce the time of GPR imaging based on CS.

Hyperbolic detection is an important task in the Ground-Penetrating Radar (GPR) images due to the buried targets display the form of hyperbolic and has been researched all the time. The detection of hyperbolic is beneficial to the buried threat detection, classification and environment change detection of GPR images. In this letter, a robust hyperbola detection method for GPR images is presented. A model based on image phase and symmetry information is developed as a detector. The detector solves the problem of hyperbola extraction from GPR images with low physical resolution. As far as we know, the phase symmetry (PS) model is used in this task for the first time and the experimental results show that the effectiveness of the proposed method is better than other methods used in recent researches.

Analyses of GPR signals for characterization of ground conditions in urban areas

An automatic recognition algorithm for GPR images of RC structure voids

Images from ground penetrating radar (GPR) may be obscured by high clutter noise over the target signal, making target detection difficult. In this contribution, a decluttering technique is applied to GPR images. This clutter removal/reduction is achieved through wavelet decomposition and application of two methods using the third and fourth order statistics: skewness and kurtosis. These higher order statistics remove clutter but retain target signatures. Different scenarios are considered for real GPR images collected in our controlled lab environment set up and peak signal to noise ratio are compared for the two methods. Further features of targets and non-targets are extracted from de-noised images. These features are used in training a neural network classifier. This classifier is applied to various real GPR images with promising results for detection of targets.

In ground-penetrating radar (GPR) detection, the impedance mismatch between air and the material under tested (MUT) creates a strong reflection at the air-ground interface. It substantially restricts the GPR’s detecting depth by reducing the electromagnetic energy fed into the ground. This paper describes a dual-band antireflection metasurface for an air-ground impedance matching layer (IML) with central frequencies of 1.0 GHz and 2.5 GHz. Its dual-band feature relies on two distinct inner and outer closed resonant ring topologies. The destructive interference theory instructs the design and optimization of the proposed IML. Through simulation and experiment, the dual-band IML can adequately eliminate reflection at the air-ground interface and enhance transmission from the air to the MUT for GPR applications. Real-world GPR experiments demonstrate that a hazy basement interface is clearer when IML is positioned at the ground’s surface. Owing to the passive and electrically thin properties of IML, the proposed approach offers a feasible impedance matching solution for GPR and through-wall radar detection. Moreover, it also has application potential in multi-band GPR and can be extended to microwave and millimeter-wave imaging systems.

Ground penetrating radar (GPR) measurements have been conducted to retrace the natural course of the Obra River and changes of spatial extent of Obrzanskie Lake (western Poland). The Obra River valley was subjected to intensive anthropogenic transformation. Three artificial canals were constructed here in the beginning of the nineteenth century. The GPR surveys were done using a georadar MALA ProEx equipped with 250 MHz antenna. The measurement was verified by coring and analyses of historical maps, aerial photographs, and satellite images. The research has shown that there are at least three elongated depressions preserved in the modern land surface near the village of Solec, situated in the middle course of the valley. Several structures suggesting functioning of a multi-channel pattern in the past have been detected inside and in the vicinity of the depressions. Filling the channels with silts and peats might have been the effect of flow deceleration caused by the construction of the Obra Canals. Moreover, situation of two of the retraced depressions suggests that the landforms might be the remains of a river bed disappearing in wetlands. Further studies including groundwater flux measurements in the area between the depressions are needed to confirm such hypothesis. Furthermore, the study allowed retracing the spatial extent of Obrzanskie Lake. The sediment structures detected by the GPR measurements and calibrated with coring were interpreted to be the traces of the lake shoreline. The interpretation was supported by the findings inferred from aerial and satellite images. It was shown that Obrzanskie Lake had had at least three islands and had been directly connected with a network of braided channels before the anthropogenic intervention. The spatial extent of the lake was significantly altered by the construction of the North Obra Canal. Disconnecting the lake from the Canal resulted in more than 50 % decrease of the lake surface.

We compare the capability of ground penetrating radar (GPR) and time domain reflectometry (TDR) to assess the temporal development of spatial variation of surface volumetric water content. In the case of GPR, we measured surface water content with the ground wave, which is a direct wave between the sender and receiver through the upper centimeters of the soil. Spatial water content variation was measured on 18 d with GPR and TDR during a 30-d monitoring period. To ensure large fluctuations in the spatial water content variation, we created a heterogeneous pattern of water content by irrigation on 2 d. The temporal development of the spatial variation was studied by means of the variogram and interpolated water content maps. To compare GPR and TDR variograms, we estimated confidence intervals of the experimental variograms and the variogram model parameters with a jackknife approach and a first-order approximation of model parameter uncertainty. The results showed that the 95% confidence intervals of the GPR experimental variogram were one to two orders of magnitude smaller than the 95% confidence intervals of the TDR experimental variogram because of the larger number of GPR measurements. Consequently, the uncertainty in the variogram model parameters was also much lower for GPR, which meant that the temporal development of the fitted GPR variogram model parameters was easier to interpret. Furthermore, the larger GPR measurement volume resulted in a low spatial nugget variance of 1 × 10−6 to 1 × 10−9 (m3 m−3)2 because short distance variation was averaged. This meant that GPR accurately measured spatial correlation lengths, even in the case of low water content variation. Interpolated maps showing the increase of water content due to irrigation and the subsequent gradual drying of the soil were more accurate and reproducible for GPR. It was concluded that the noninvasive GPR measurements provide the means to accurately and consistently monitor the development of spatial water content variation in time.

This paper combines such powerful non-destructive technique as ground penetrating radar (GPR) with intelligent data analysis in order to acquire new knowledge on mapping/monitoring/verification systems aimed at initially facilitating the evaluation of the health of the buried assets of water distribution system (WDS) infrastructures in urban areas. Preprocessing techniques based on a multi-agent approach coupled with a suitable analysis of the properties of the obtained groups of objects found, as well as a classification supported by machine learning techniques, are presented in this work. The work is based on GPR image studies conducted under controlled laboratory conditions using a commercial antenna. The buried objects correspond to pipes of various materials commonly used in WDSs. Furthermore, GPR images of various pipes either empty or full of water, as well as leaking water, are also included in the dataset of the study. The dataset is divided into two subsets that pursue: 1) the development of the methodology; i.e. capturing objects in GPR images to favour the feature extraction process, and 2) the evaluation of the feasibility of implementation of the proposed classification, as well as the response of the methodology to various environmental interactions recorded in the images. The results of this work are promising in the sense of promoting the inclusion of powerful tools such as GPR towards the provision of smarter tools that adequately support technical management in WDSs, and, eventually, that of other surrounding infrastructure(s).