scholarly journals A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements

2021 ◽  
Vol 13 (21) ◽  
pp. 4250
Author(s):  
Jordi Mahardika Puntu ◽  
Ping-Yu Chang ◽  
Ding-Jiun Lin ◽  
Haiyina Hasbia Amania ◽  
Yonatan Garkebo Doyoro
Keyword(s):  
Ground Penetrating Radar ◽  
Field Data ◽  
Comprehensive Evaluation ◽  
Random Noise ◽  
Wall Rock ◽  
Tunnel Lining ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Silhouette Index ◽  
Ground Penetrating

We aim to develop a comprehensive tunnel lining detection method and clustering technique for semi-automatic rebar identification in order to investigate the ten tunnels along the South-link Line Railway of Taiwan (SLRT). We used the Ground Penetrating Radar (GPR) instrument with a 1000 MHz antenna frequency, which was placed on a versatile antenna holder that is flexible to the tunnel’s condition. We called it a Vehicle-mounted Ground Penetrating Radar (VMGPR) system. We detected the tunnel lining boundary according to the Fresnel Reflection Coefficient (FRC) in both A-scan and B-scan data, then estimated the thinning lining of the tunnels. By applying the Hilbert Transform (HT), we extracted the envelope to see the overview of the energy distribution in our data. Once we obtained the filtered radargram, we used it to estimate the Two-dimensional Forward Modeling (TDFM) simulation parameters. Specifically, we produced the TDFM model with different random noise (0–30%) for the rebar model. The rebar model and the field data were identified with the Hierarchical Agglomerative Clustering (HAC) in machine learning and evaluated using the Silhouette Index (SI). Taken together, these results suggest three boundaries of the tunnel lining i.e., the air–second lining boundary, the second–first lining boundary, and the first–wall rock boundary. Among the tunnels that we scanned, the Fangye 1 tunnel is the only one in category B, with the highest percentage of the thinning lining, i.e., 13.39%, whereas the other tunnels are in category A, with a percentage of the thinning lining of 0–1.71%. Based on the clustered radargram, the TDFM model for rebar identification is consistent with the field data, where k = 2 is the best choice to represent our data set. It is interesting to observe in the clustered radargram that the TDFM model can mimic the field data. The most striking result is that the TDFM model with 30% random noise seems to describe our data well, where the rebar response is rough due to the high noise level on the radargram.

scholarly journals Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

2019 ◽  
Vol 11 (4) ◽  
pp. 405
Author(s):  
Xuan Feng ◽  
Haoqiu Zhou ◽  
Cai Liu ◽  
Yan Zhang ◽  
Wenjing Liang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Decomposition Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Target Classification ◽  
Data Set ◽  
Particle Center ◽  
Ground Penetrating ◽  
New Criterion

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

Helicopter-borne ground-penetrating radar investigations on temperate alpine glaciers: A comparison of different systems and their abilities for bedrock mapping

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA119-WA129 ◽  
Author(s):  
Anja Rutishauser ◽  
Hansruedi Maurer ◽  
Andreas Bauder
Keyword(s):  
Ground Penetrating Radar ◽  
Low Frequency ◽  
Large Data ◽  
Internal Reflection ◽  
Swiss Alps ◽  
Reflection Band ◽  
Data Set ◽  
Investigation Area ◽  
Attractive Option ◽  
Ground Penetrating

On the basis of a large data set, comprising approximately 1200 km of profile lines acquired with different helicopter-borne ground-penetrating radar (GPR) systems over temperate glaciers in the western Swiss Alps, we have analyzed the possibilities and limitations of using helicopter-borne GPR surveying to map the ice-bedrock interface. We have considered data from three different acquisition systems including (1) a low-frequency pulsed system hanging below the helicopter (BGR), (2) a stepped frequency system hanging below the helicopter (Radar Systemtechnik GmbH [RST]), and (3) a commercial system mounted directly on the helicopter skids (Geophysical Survey Systems Incorporated [GSSI]). The systems showed considerable differences in their performance. The best results were achieved with the BGR system. On average, the RST and GSSI systems yielded comparable results, but we observed significant site-specific differences. A comparison with ground-based GPR data found that the quality of helicopter-borne data is inferior, but the compelling advantages of airborne surveying still make helicopter-borne data acquisition an attractive option. Statistical analyses concerning the bedrock detectability revealed not only large differences between the different acquisition systems but also between different regions within our investigation area. The percentage of bedrock reflections identified (with respect to the overall profile length within a particular region) varied from 11.7% to 68.9%. Obvious factors for missing the bedrock reflections included large bedrock depths and steeply dipping bedrock interfaces, but we also observed that internal features within the ice body may obscure bedrock reflections. In particular, we identified a conspicuous “internal reflection band” in many profiles acquired with the GSSI system. We attribute this feature to abrupt changes of the water content within the ice, but more research is required for a better understanding of the nature of this internal reflection band.

Exploring the Japan Cave in Taman Hutan Raya Djuanda, Bandung Using GPR

2018 ◽  
Vol 23 (3) ◽  
pp. 377-381
Author(s):  
Widodo Widodo ◽  
Azizatun Azimmah ◽  
Djoko Santoso
Keyword(s):  
Dielectric Permittivity ◽  
Ground Penetrating Radar ◽  
Field Data ◽  
Geophysical Methods ◽  
Forward Modeling ◽  
Additional Line ◽  
Reflection Amplitude ◽  
Underground Cavities ◽  
Ground Penetrating ◽  
Synthetic Models

Investigating underground cavities is vital due to their potential for subsidence and total collapse. One of the proven geophysical methods for locating underground cavities at a shallow depth is ground penetrating radar (GPR). GPR uses contrasting dielectric permittivity, resistivity, and magnetic permeability to map the subsurface. The aim of this research is to prove that GPR can be applied to detect underground cavities in the Japan Cave of Taman Hutan Raya Djuanda, in Bandung, Indonesia. Forward modeling was performed first using three representative synthetic models before field data were acquired. The data acquisition was then conducted using a 100 MHz GPR shielded antenna with three lines of 80 m and one additional line 10 m long. The result showed a region of different reflection amplitude, which was proven to be the air-filled cavities.

Diffraction imaging of ground-penetrating radar data

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. H1-H12 ◽  
Author(s):  
Hemin Yuan ◽  
Mahboubeh Montazeri ◽  
Majken C. Looms ◽  
Lars Nielsen
Keyword(s):  
Ground Penetrating Radar ◽  
Field Data ◽  
Velocity Structure ◽  
Velocity Estimation ◽  
Small Scale ◽  
Imaging Method ◽  
Surface Reflection ◽  
Diffraction Imaging ◽  
Saturated Media ◽  
Ground Penetrating

Diffractions caused by, e.g., faults, fractures, and small-scale heterogeneity localized near the surface are often used in ground-penetrating radar (GPR) reflection studies to constrain the subsurface velocity distribution using simple hyperbola fitting. Interference with reflected energy makes the identification of diffractions difficult. We have tailored and applied a diffraction imaging method to improve imaging for surface reflection GPR data. Based on a plane-wave destruction algorithm, the method can separate reflections from diffractions. Thereby, a better identification of diffractions facilitates an improved determination of GPR wave velocities and an optimized migration result. We determined the potential of this approach using synthetic and field data, and, for the field study, we also compare the estimated velocity structure with crosshole GPR results. For the field data example, we find that the velocity structure estimated using the diffraction-based process correlates well with results from crosshole GPR velocity estimation. Such improved velocity estimation may have important implications for using surface reflection GPR to map, e.g., porosity for fully saturated media or soil moisture changes in partially saturated media because these physical properties depend on the dielectric permittivity and thereby also the GPR wave velocity.

On the use of dispersive APVO GPR curves for thin-bed properties estimation: Theory and application to fracture characterization

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. J1-J12 ◽  
Author(s):  
Jacques Deparis ◽  
Stéphane Garambois
Keyword(s):  
Ground Penetrating Radar ◽  
Inverse Method ◽  
Phase Variation ◽  
Fractured Media ◽  
Data Set ◽  
Fracture Characterization ◽  
Propagation Effects ◽  
Phase Liquid ◽  
Ground Penetrating ◽  
Homogeneous Formation

The presence of a thin layer embedded in any formation creates complex reflection patterns caused by interferences within the thin bed. The generated reflectivity amplitude variations with offset have been increasingly used in seismic interpretation and more recently tested on ground-penetrating radar (GPR) data to characterize nonaqueous-phase liquid contaminants. Phase and frequency sensitivities of the reflected signals are generally not used, although they contain useful information. The present study aims to evaluate the potential of these combined properties to characterize a thin bed using GPR data acquired along a common-midpoint (CMP) survey, carried out to assess velocity variations in the ground. It has been restricted to the simple case of a thin bed embedded within a homogeneous formation, a situation often encountered in fractured media. Dispersive properties ofthe dielectric permittivity of investigated materials (homogeneous formation, thin bed) are described using a Jonscher parameterization, which permitted study of the dependency of amplitude and phase variation with offset (APVO) curves on frequency and thin-bed properties (filling nature, aperture). In the second part, we discuss and illustrate the validity of the thin-bed approximation as well as simplify assumptions and make necessary careful corrections to convert raw CMP data into dispersive APVO curves. Two different strategies are discussed to correct the data from propagation effects: a classical normal-moveout approach and an inverse method. Finally, the proposed methodology is applied to a CMP GPR data set acquired along a vertical cliff. It allowed us to extract the characteristics of a subvertical fracture with satisfying resolution and confidence. The study motivates interest to use dispersion dependency of the reflection coefficient variations for thin-bed characterization.

Interpolating wide-aperture ground-penetrating radar beyond aliasing

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. H13-H22 ◽  
Author(s):  
Saulo S. Martins ◽  
Jandyr M. Travassos
Keyword(s):  
Ground Penetrating Radar ◽  
Real Data ◽  
Velocity Model ◽  
Data Sets ◽  
Inversion Problem ◽  
Data Set ◽  
Isolated Points ◽  
Antarctic Continent ◽  
Ground Penetrating ◽  
The Antarctic

Most of the data acquisition in ground-penetrating radar is done along fixed-offset profiles, in which velocity is known only at isolated points in the survey area, at the locations of variable offset gathers such as a common midpoint. We have constructed sparse, heavily aliased, variable offset gathers from several fixed-offset, collinear, profiles. We interpolated those gathers to produce properly sampled counterparts, thus pushing data beyond aliasing. The interpolation methodology estimated nonstationary, adaptive, filter coefficients at all trace locations, including at the missing traces’ corresponding positions, filled with zeroed traces. This is followed by an inversion problem that uses the previously estimated filter coefficients to insert the new, interpolated, traces between the original ones. We extended this two-step strategy to data interpolation by employing a device in which we used filter coefficients from a denser variable offset gather to interpolate the missing traces on a few independently constructed gathers. We applied the methodology on synthetic and real data sets, the latter acquired in the interior of the Antarctic continent. The variable-offset interpolated data opened the door to prestack processing, making feasible the production of a prestack time migrated section and a 2D velocity model for the entire profile. Notwithstanding, we have used a data set obtained in Antarctica; there is no reason the same methodology could not be used somewhere else.

Continuous and simultaneous measurement of reflector depth and average soil-water content with multichannel ground-penetrating radar

Geophysics ◽  
2008 ◽  
Vol 73 (4) ◽  
pp. J15-J23 ◽  
Author(s):  
Holger Gerhards ◽  
Ute Wollschläger ◽  
Qihao Yu ◽  
Philip Schiwek ◽  
Xicai Pan ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Ground Penetrating Radar ◽  
Synthetic Data ◽  
Evaluation Procedure ◽  
Noninvasive Technique ◽  
Data Set ◽  
Large Variability ◽  
Ground Penetrating

Ground-penetrating radar is a fast noninvasive technique that can monitor subsurface structure and water-content distribution. To interpret traveltime information from single common-offset measurements, additional assumptions, such as constant permittivity, usually are required. We present a fast ground-penetrating-radar measurement technique using a multiple transmitter-and-receiver setup to measure simultaneously the reflector depth and average soil-water content. It can be considered a moving minicommon-midpoint measurement. For a simple analysis, we use a straightforward evaluation procedure that includes two traveltimes to the same reflector, obtained from different antenna separations. For a more accurate approach, an inverse evaluation procedure is added, using traveltimes obtained from all antenna separations at one position and its neighboring measurement locations. The evaluation of a synthetic data set with a lateral variability in reflector depth and an experimental example with a large variability in soil-water content are introduced to demonstrate the applicability for field-scale measurements. The crucial point for this application is the access to absolute traveltimes, which are difficult to determine accurately from common-offset measurements.

Application of the Ground Penetrating Radar in the Quality Detection of Tunnel Lining

2011 ◽  
Vol 243-249 ◽  
pp. 5381-5385 ◽  
Author(s):  
Ji Shun Pan ◽  
Lei Yang ◽  
Yuan Bao Leng ◽  
Zhi Quan Lv
Keyword(s):  
Data Processing ◽  
Ground Penetrating Radar ◽  
Tunnel Lining ◽  
Quality Detection ◽  
Steel Bar ◽  
Data Processing Method ◽  
Work Mechanism ◽  
Important Means ◽  
Working Principle ◽  
Ground Penetrating

Based on the ground penetrating radar's work mechanism, this article briefly introduces the working principle and the data processing method of ground penetrating radar detecting the tunnel lining. In view of the lining quality detection's characteristics, it summarizes a series of atlas reflection characteristic of the examination target such as the lining thickness, the backfill quality, the steel bar reinforcement situation, the adjacent formation structural feature and so on, and analyses and comments on them with project examples. The research believes that under appropriate working condition, as an important means to guarantee the construction security and maintain the tunnel health, ground penetrating radar technology can examine the lining quality fast and effectively, and meet the needs of the tunnel lining quality detection with suitable equipment, working method and data processing plan.

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