Advanced Inversion Techniques for Ground Penetrating Radar

Fedeli, Alessandro; Pastorino, Matteo; Randazzo, Andrea

doi:10.26636/jtit.2017.119717

Cited by 6 publications

(3 citation statements)

References 72 publications

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“…Recent advances in the development of inverse scattering procedures for GPR imaging are revised and discussed in the literature. This enhances imaging capabilities of GPR method [42,43].…”

Section: Suitability Of Gpr Application For Soil Pipe Detectionmentioning

confidence: 88%

Detection of Soil Pipes Using Ground Penetrating Radar

Bernatek‐Jakiel

Kondracka

2019

Remote Sensing

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Soil piping leads to land degradation in almost all morphoclimatic regions. However, the detection of soil pipes is still a methodological challenge. Therefore, this study aims at testing ground penetrating radar (GPR) to identify soil pipes and to present the complexity of soil pipe networks. The GPR surveys were conducted at three sites in the Bieszczady Mountains (SE Poland), where pipes develop in Cambisols. In total, 36 GPR profiles longitudinal and transverse to piping systems were made and used to provide spatial visualization of pipe networks. Soil pipes were identified as reflection hyperbolas on radargrams, which were verified with the surface indicators of piping, i.e., sagging of the ground and the occurrence of pipe roof collapses. Antennas of 500 MHz and 800 MHz were tested, which made possible the penetration of the subsurface up to 3.2 m and 2 m, respectively. Concerning ground properties, antenna frequencies and processing techniques, there was a potential possibility to detect pipes with a minimum diameter of 3.5 cm (using the antenna of lower frequency), and 2.2 cm (with the antenna of higher frequency). The results have proved that soil pipes meander horizontally and vertically and their networks become more complicated and extensive down the slope. GPR is a useful method to detect soil pipes, although it requires field verification and the proper selection of antenna frequency.

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“…Recent advances in the development of inverse scattering procedures for GPR imaging are revised and discussed in the literature. This enhances imaging capabilities of GPR method [42,43].…”

Section: Suitability Of Gpr Application For Soil Pipe Detectionmentioning

confidence: 88%

Detection of Soil Pipes Using Ground Penetrating Radar

Bernatek‐Jakiel

Kondracka

2019

Remote Sensing

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“…Simplified GPR sources have long been used in the past for a diverse number of applications, e.g. inverse scattering problems [21], [22]. Early works [23]- [26] have focused on implementing simple antenna types and/or parts of antenna models which do not yet include all antenna components.…”

Section: Introductionmentioning

confidence: 99%

Developing Realistic FDTD GPR Antenna Surrogates by Means of Particle Swarm Optimization

Stadler

Igel

2022

IEEE Trans. Antennas Propagat.

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The antenna is the most important part of a groundpenetrating radar (GPR) system and defines the employed electromagnetic pulse and how it is transferred to the ground. It is crucial to account for these coupling effects in numerical simulations and to implement realistic antenna models, e.g., for fullwaveform inversion (FWI). We present a method of developing and adapting 3D Finite-Difference Time-Domain (FDTD) models of GPR antennas, complete with electric components, dielectric material properties and feed pulse details. We exemplify this with a commercially available, shielded 400 MHz GPR antenna, a model of which was set up by fitting synthetic data to an experimental signal of the antenna reflected at a metal plate in air. For this FWI we used a particle-swarm optimization (PSO) algorithm because the fitted parameters show complex individual effects on the GPR waveform. The resulting antenna model is then validated against data measured in air, water, and with a metal plate in the near-field of the antenna. Overall, the synthetic data reproduces the validation data very accurately. Signals of objects placed in the near-field of the antenna and the change of the shape and frequency content of the radiated wavelet with varying subsurface properties are emulated correctly.

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“…The translation of the electromagnetic information stored in the B-Scan into inner-defect related information, such as locations, shapes, and dielectric properties, is of great importance for tunnel lining defect inspection. There are a number of existing methods for GPR inversion which aim to map the dielectric distribution of the structure to be detected based on the recorded GPR data [12]. These methods mainly include common-midpoint velocity analysis [13], ray-based methods [14], reverse-time migration (RTM) [15], tomography approaches [16], and full-waveform inversion (FWI) methods [17]- [18].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Ren

Liu

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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A DNN architecture called GPRInvNet is proposed to tackle the challenge of mapping Ground Penetrating Radar (GPR) B-Scan data to complex permittivity maps of subsurface structure. GPRInvNet consists of a trace-to-trace encoder and a decoder. It is specially designed to take account of the characteristics of GPR inversion when faced with complex GPR B-Scan data as well as addressing the spatial alignment issue between time-series B-Scan data and spatial permittivity maps. It fuses features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. The sensitive zone on the permittivity map spatially aligned to the enhanced trace is reconstructed accurately. GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel lining containing complex defects has been reconstructed using GPRInvNet, and results demonstrate that GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrate the superiority of the GPRInvNet. To generalize GPRInvNet to real GPR data, we integrated background noise patches recorded form a practical model testing into synthetic GPR data to train GPRInvNet. The model testing has been conducted for validation, and experimental results show that GPRInvNet achieves satisfactory results on real data.

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Advanced Inversion Techniques for Ground Penetrating Radar

Cited by 6 publications

References 72 publications

Detection of Soil Pipes Using Ground Penetrating Radar

Detection of Soil Pipes Using Ground Penetrating Radar

Developing Realistic FDTD GPR Antenna Surrogates by Means of Particle Swarm Optimization

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

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