Multifrequency Ground-penetrating Radar Data Research Articles

Multi-Frequency GPR Data Fusion through a Joint Sliding Window and Wavelet Transform-Weighting Method for Top-Coal Structure Detection

Top-coal structure detection is an important basis for realizing effective mining in fully mechanized cave faces. However, the top-coal structure is very complex and often contains multi-layer gangues, which seriously influence the level of effective mining. For these reasons, this paper proposes a novel multi-frequency ground-penetrating radar (GPR) data-fusing method through a joint sliding window and wavelet transform weighting method to accurately detect the top-coal structure. It possesses the advantages of both high resolution and great detection depth, and it can also integrate multi-frequency GPR data into one composite profile to interpret the internal structure information of top coal in detail. The detection procedure is implemented following several steps: First of all, the multi-frequency GPR data are preprocessed and aligned through a band-pass filter and a zero offset elimination method to establish their spatial correspondences. Secondly, the proposed method is used to determine the time-varying weight values of each frequency GPR signal according to the wavelet energy proportion within the sliding window; also, the edge detection algorithm is introduced to improve the fusion efficiency of the wavelet transform so as to realize the effective fusion of the multi-frequency GPR data. Thirdly, a reflection intensity model of multi-frequency GPR signals traveling in the top-coal is established by using the stratified identification method, and then, the detailed top-coal structure can be inversely interpreted. Finally, the quantitative evaluation criteria, information entropy (IE), space–frequency (SF) and Laplacian gradient (LG), are used to evaluate the multi-frequency GPR data fusion’s effectiveness in laboratory and field environments. The experimental results show that, compared with the genetic, time-varying and wavelet transform fusion method, the fusion performance of the presented method possesses higher values in the IE, SF and LG evaluation criteria, and it also has both the merits of high resolution and great detection depth.

Deep learning-based multifrequency ground penetrating radar data merging

Ground-penetrating radar systems with a single central frequency suffer limitations due to the unavoidable trade-off between resolution and penetration depth that multifrequency equipments can overcome. A new semisupervised multifrequency merging algorithm was developed based on deep learning and specifically on bi-directional long-short term memory to automatically merge varying numbers of data sets collected at different frequencies. A new training strategy, based only on the data set of interest, without synthetic or real training data sets was implemented. The proposed methodology is tested on synthetic and field data, to evaluate its performance and robustness. The merging algorithm can manage the complementarity of information at different central frequencies, properly merging different types of data. Results indicate not only a smooth transition in time, but, even more important, a remarkable broadening of the bandwidth thus increasing the overall resolution. Our approach is not limited to specific frequency components or geologic settings but can be potentially exploited to merge any type of data set with different spectral components.

A Novel Multifrequency GPR Data Fusion Algorithm Based on Time-Varying Weighting Strategy

Ground-Penetrating Radar (GPR) technology plays an important role in near-surface geophysical survey. Usually, the dataset acquired by higher frequency antenna has higher resolution but limited penetration depth; while the dataset collected by lower one has deeper penetration depth but lower resolution. It is necessary to fuse several different frequency datasets into a composite displayed profile to get a trade-off between penetration depth and resolution. In such case, we propose a novel multi-frequency GPR data fusion method and test it on the data acquired from a limestone quarry in the Taihang Mountains of China. In our algorithm, time-varying weighting factors are calculated by virtues of a sliding time window, i.e. designed with segmented signals. According to this approach, we can achieve better local characteristics of the signal as well as the smoothness of the signal waveform. The results show that the spectrum bandwidth of the fused signal is broadened, and the fusion profile significantly improves the imaging of internal structures for limestone on different scales.

Multi-frequency GPR data fusion and its application in NDT

Ground-penetrating radar (GPR) is a non-destructive technique that utilizes high-frequency electromagnetic waves to detect and locate subsurface objects and interfaces. Resolution can be improved with an increase of the source frequency; however, this will lead to decreased investigation depth due to the stronger attenuation of the high frequency signal. Thus, there is a trade-off between the resolution and the investigation depth in the single central frequency-based GPR system. To obtain both high resolution and deep penetrating ability simultaneously, we applied multi-frequency GPR data fusion with three algorithms: time-domain fusion with weights/without, frequency-domain fusion with weights. The fusion effect is qualitatively and quantitatively evaluated and compared by the fused radar profile and Laplacian operator, which is a second derivative gradient operator and commonly used in image edge detection by detecting the zero-crossings of image intensity. The results of the numerical simulation and field data showed that the fused profile was able not only to retain the high resolution in the shallow area from the high-frequency antenna but also take advantage of the significant investigation depth of the low-frequency antenna by merging the multi-frequency data into a single profile. Thus, the fused GPR data has the ability to create a single profile with more information and detailed characteristics.

Full-Wave Removal of Internal Antenna Effects and Antenna–Medium Interactions for Improved Ground-Penetrating Radar Imaging

Antenna effects alter the detection of buried objects during ground-penetrating radar (GPR) surveys. In this paper, we propose a novel approach based on full-wave inversion to filter out antenna effects from GPR data. The approach, which is exact for locally planar layered media, resorts to a recently developed electromagnetic model that takes advantage of an intrinsic, closed-form solution of Maxwell’s equations to describe the antenna–medium system. As any multilayered medium can be reduced to a half-space medium with effective, frequency-dependent, global reflection coefficients, the method consists in inverting the radar data to retrieve a frequency-dependent, half-space complex conductivity. Converted into the time domain, this quantity represents the filtered radar image. We validated the approach through numerical simulations and laboratory experiments with pipes buried in a sandbox. The results demonstrated the validity of the concept and showed that the filtered radar images include only medium reflections, which means an easier interpretation in terms of medium structures. Antenna radiation pattern effects are, however, not removed. This physically based approach including the full-wave antenna model appears to be very promising for improved subsurface imaging and provides the basis for multifrequency GPR data fusion as the source is inherently normalized.

Fusion of Multifrequency GPR Data Freed From Antenna Effects

Several data fusion approaches have been developed to optimize both resolution and characterization depth for multifrequency ground-penetrating radar (GPR). In this study, we propose a novel physically based method to merge radar data coming from antennas operating in different frequency ranges. The strategy relies on the removal of the source and antenna effects from GPR data and the subsequent fusion of the resulting signals, which are now normalized, in the frequency domain. The approach used to filter out antenna effects resorts to an intrinsic, closed-form solution of Maxwell's equations to describe the radar-antenna-medium system. We validated the multifrequency GPR data fusion approach through laboratory experiments with measurements performed in far- and near-field conditions above a copper plane and pipes buried at different depths in a sandbox. The results demonstrated the benefit of filtering the frequency-dependent antennas effects before data fusion. Enhanced radargrams were subsequently obtained as a result of the broadening of the spectral bandwidth. This physically based fusion approach appears to be very promising to improve subsurface imaging.

Advanced multi-frequency GPR data processing for non-linear deterministic imaging

In this paper, the quantitative imaging of the dielectric characteristics of unknown targets buried in a lossy half-space is performed by suitably processing wide-band ground penetrating radar (GPR) measurements. An innovative multi-frequency (MF) fully non-linear inverse scattering (IS) technique exploiting the integration of a conjugate-gradient (CG) solver within the iterative multi-scaling approach (IMSA) is proposed. Representative results from numerical test cases are presented to provide the interested readers with some indications on the effectiveness, as well as the current limitations, of the proposed approach when directly compared to a state-of-the-art frequency-hopping (FH) based method formulated in the same framework. Such a validation points out that if, on the one hand, the proposed MF strategy is computationally more efficient than the FH one, on the other hand, it turns out to be less reliable and accurate in several situations.

Permafrost Subgrade Condition Assessment Using Extrapolation by Deterministic Deconvolution on Multifrequency GPR Data Acquired Along the Qinghai-Tibet Railway

The Qinghai-Tibet railway (QTR) stretches 1956 km on Tibetan Plateau, the “roof of the world” from Xining City, Qinghai to the City of Lhasa, Tibet, China. About one half ( ${\sim}960\;\text{km}$ ) of the total length of this railway is on permafrost subgrades. Frozen earth hazard and other subgrade problems lead to subgrade instability and affect the normal operation of railway transportation. It is critical to develop a rapid and efficient technique for railway foundation defect detection. Multifrequency ground-penetrating radar (GPR) provides a good tradeoff between imaging depth and resolution for railway subgrades inspection. We have developed a signal fusion method to extrapolate the higher frequency, higher resolution GPR signal into a greater depth based on the lower frequency, greater penetration signal using the extrapolation with deterministic deconvolution (EDD) algorithm. This paper first introduces the principles of EDD and demonstrates the procedure by applying it to a synthetic data set. Next, data preprocessing and filtering are discussed to prepare the field GPR data acquired on QTR to be suitable for carrying out EDD. Finally, the multifrequency radar signal is fused based on EDD to reach a higher resolution and signal-to-noise ratio for the fused radargram profile. Examples of application of this proposed approach at three sections provide a demonstration for characterizing the status of subgrade permafrost and recognizing subgrade problem in different depths beneath the QTR. Our field tests of EDD to multifrequency GPR data demonstrate that it is a promising technique for signal enhancement.

GPR Prospecting Through an Inverse-Scattering Frequency-Hopping Multifocusing Approach

An innovative information-acquisition approach to 2-D ground-penetrating radar (GPR) prospecting is presented. A microwave inverse-scattering nested scheme combining a frequency hopping (FH) procedure and a multifocusing (MF) technique is proposed. On the one hand, the FH scheme effectively handles multifrequency GPR data, whereas on the other hand, MF techniques have been proven to be effective tools in reducing the occurrence of multilocal minima affecting GPR investigations. This allows the use of a local search technique based on the conjugate gradient method to iteratively solve the inverse problem at hand. Selected results are reported and analyzed to give some insights to the interested readers on the advantages and limitations of such an approach when handling synthetically generated and experimental GPR data.

Ray‐based synthesis of bistatic ground‐penetrating radar profiles

An algorithm has been developed to numerically synthesize 2-D bistatic (common‐offset), ground‐penetrating radar (GPR) profiles using the principles of geometrical ray theory. By assuming nondispersive propagation, kinematic properties of electromagnetic waves are simulated by ray tracing. Dynamic properties are simulated by computing transmitter and receiver directivities, reflection and transmission coefficients, geometrical spreading, and attenuation coefficients. The main limitations are that wave effects, such as diffractions, and offline (3-D) effects are not included. The algorithm is applied to iterative modeling of multioffset, multifrequency GPR data acquired over an outcrop of fractured Austin Chalk in Dallas County in northeast Texas. Modeling is able to simulate realistically the main time and amplitude behaviors observed in GPR reflections at 50, 100, and 200 MHz at each of 1, 3, and 5 meter antenna separations from a single model. Detailed modeling produces quantitative estimates of the spatial distributions of electrical properties that are consistent with the geologic environment.