Dielectric constant determination using ground-penetrating radar reflection coefficients

2000 ◽  
Vol 43 (2-4) ◽  
pp. 189-197 ◽  
Author(s):  
Philip M. Reppert ◽  
F.Dale Morgan ◽  
M.Nafi Toksöz
Keyword(s):  
Dielectric Constant ◽  
Ground Penetrating Radar ◽  
Reflection Coefficients ◽  
Constant Determination ◽  
Radar Reflection ◽  
Ground Penetrating

Imaging the internal structure of trunks via multiscale phase inversion of ground-penetrating radar data

2021 ◽  
pp. 1-53
Author(s):  
Lei Fu ◽  
Lanbo Liu
Keyword(s):  
Dielectric Constant ◽  
Internal Structure ◽  
Ground Penetrating Radar ◽  
Phase Inversion ◽  
Multiscale Approach ◽  
White Oak ◽  
Near Surface ◽  
Oak Tree ◽  
Ground Penetrating ◽  
Constant Distribution

Ground-penetrating radar (GPR) is a geophysical technique widely used in near-surface non-invasive detecting. It has the ability to obtaining a high-resolution internal structure of living trunks. Full wave inversion (FWI) has been widely used to reconstruct the dielectric constant and conductivity distribution for cross-well application. However, in some cases, the amplitude information is not reliable due to the antenna coupling, radiation pattern and other effects. We present a multiscale phase inversion (MPI) method, which largely matches the phase information by normalizing the magnitude spectrum; in addition, a natural multiscale approach by integrating the input data with different times is implemented to partly mitigate the local minimal problem. Two synthetic GPR datasets generated from a healthy oak tree trunk and from a decayed trunk are tested by MPI and FWI. Field GPR dataset consisting of 30 common shot GPR data are acquired on a standing white oak tree (Quercus alba); the MPI and FWI methods are used to reconstruct the dielectric constant distribution of the tree cross-section. Results indicate that MPI has more tolerance to the starting model, noise level and source wavelet. It can provide a more accurate image of the dielectric constant distribution compared to the conventional FWI.

Kinetic Study of Portland Cement Hydration with Ground Penetrating Radar

2013 ◽  
Vol 539 ◽  
pp. 25-29
Author(s):  
Wei Chen ◽  
Pei Liang Shen ◽  
Jian Xin Lu ◽  
Wan Ru Zhang
Keyword(s):  
Dielectric Constant ◽  
Dielectric Properties ◽  
Ground Penetrating Radar ◽  
Cement Hydration ◽  
Relative Dielectric Constant ◽  
Gradual Decline ◽  
Hardening Process ◽  
Ground Penetrating ◽  
Kinetics Of ◽  
Portland Cement Hydration

The variations of dielectric constant and the amplitude of reflected EM wave of concrete during the first 3 days are measured with Ground Penetrating Radar (GPR) at 20 oC. The amplitude decreases sharply after mixing with water, and then increases till a stabilized stage, followed by a gradual decline. The relative dielectric constant decreases with increasing hydrating time. The results show that the dielectric properties of concrete can be used as an effective way of studying the kinetics of concrete setting and hardening process at early ages.

scholarly journals Soil water content estimation using ground penetrating radar data via group intelligence optimization algorithms: An application in the Northern Shaanxi Coal Mining Area

2020 ◽  
pp. 014459872097336
Author(s):  
Fan Cui ◽  
Jianyu Ni ◽  
Yunfei Du ◽  
Yuxuan Zhao ◽  
Yingqing Zhou
Keyword(s):  
Dielectric Constant ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Error Rate ◽  
Ground Penetrating Radar ◽  
Volumetric Water Content ◽  
Average Error ◽  
Average Error Rate ◽  
Ground Penetrating

The determination of quantitative relationship between soil dielectric constant and water content is an important basis for measuring soil water content based on ground penetrating radar (GPR) technology. The calculation of soil volumetric water content using GPR technology is usually based on the classic Topp formula. However, there are large errors between measured values and calculated values when using the formula, and it cannot be flexibly applied to different media. To solve these problems, first, a combination of GPR and shallow drilling is used to calibrate the wave velocity to obtain an accurate dielectric constant. Then, combined with experimental moisture content, the intelligent group algorithm is applied to accurately build mathematical models of the relative dielectric constant and volumetric water content, and the Topp formula is revised for sand and clay media. Compared with the classic Topp formula, the average error rate of sand is decreased by nearly 15.8%, the average error rate of clay is decreased by 31.75%. The calculation accuracy of the formula has been greatly improved. It proves that the revised model is accurate, and at the same time, it proves the rationality of the method of using GPR wave velocity calibration method to accurately calculate the volumetric water content.

Ü-Net: Deep-Learning Schemes for Ground Penetrating Radar Data Inversion

2020 ◽  
Vol 25 (2) ◽  
pp. 287-292
Author(s):  
Longhao Xie ◽  
Qing Zhao ◽  
Chunguang Ma ◽  
Binbin Liao ◽  
Jianjian Huo
Keyword(s):  
Neural Network ◽  
Dielectric Constant ◽  
Data Compression ◽  
Ground Penetrating Radar ◽  
Quantitative Imaging ◽  
Inversion Method ◽  
Output Unit ◽  
Data Inversion ◽  
Ground Penetrating ◽  
Constant Distribution

Electromagnetic (EM) inversion is a quantitative imaging technique that can describe the dielectric constant distribution of a target based on the EM signals scattered from it. In this paper, a novel deep neural network (DNN) based methodology for ground penetrating radar (GPR) data inversion, known as the Ü-net is introduced. The proposed Ü-net consists of three parts: a data compression unit, U-net, and an output unit. The novel inversion approach, based on supervised learning, uses a neural network to generate the dielectric constant distribution from GPR data. The GPR data can be compressed and reshaped the size using data compression unit. The U-net maps the object features to the dielectric constant distribution. The output unit meshes the dielectric constant distribution more finely. A novel feature of the proposed methodology is the application of instance normalization (IN) to the DNN EM inversion method and a comparison of its performance to batch normalization (BN). The validity of this technique is confirmed by numerical simulations. The Mean-Square Error of the test data sets is 0.087. These simulations prove that the instance normalization is suitable for GPR data inversion. The proposed approach is promising for achieving quality dielectric constant images in real-time.

Ground‐penetratinng radar reflection profiling of groundwater and bedrock in an area of discontinuous permafrost

Geophysics ◽  
1998 ◽  
Vol 63 (5) ◽  
pp. 1573-1584 ◽  
Author(s):  
Steven A. Arcone ◽  
Daniel E. Lawson ◽  
Allan J. Delaney ◽  
Jeffrey C. Strasser ◽  
Jodie D. Strasser
Keyword(s):  
Ground Penetrating Radar ◽  
Relative Dielectric Permittivity ◽  
Well Log ◽  
Surface Conditions ◽  
Continuous Contact ◽  
Interior Alaska ◽  
Discontinuous Permafrost ◽  
Radar Reflection ◽  
Ground Penetrating ◽  
Scattering Losses

We have used ground‐penetrating radar to profile the depth of permafrost, to groundwater beneath permafrost, and to bedrock within permafrost in alluvial sediments of interior Alaska. We used well log data to aid the interpretations and to calculate dielectric permittivities for frozen and unfrozen materials. Interfaces between unfrozen and frozen sediments above permafrost were best resolved with wavelet bandwidths centered at and above 100 MHz. The resolution also required consideration of antenna configuration, season, and surface conditions. Depths to subpermafrost groundwater were profiled where it was in continuous contact with the bottom of the permafrost, except near transitions to unfrozen zones, where the contact appeared to dip steeply. The complexity of the responses to intrapermafrost bedrock, detected at a maximum depth of 47 m, appears to distinguish these events from those of subpermafrost saturated sediments. The relative dielectric permittivity ranged between 4.4 and 8.3 for the permafrost, and between 12 and 45 for partially to fully saturated, unfrozen silts and sands. Scattering losses are evident from intrapermafrost diffractions and from the improved penetration achieved by lowering the midband radar frequency from 100 to 50 MHz.

Ground Penetrating Radar for Water Content and Compaction Evaluation: A Laboratory Test on Construction Material

2020 ◽  
Vol 25 (2) ◽  
pp. 169-179
Author(s):  
Hashem Ranjy Roodposhti ◽  
Mohammad Kazem Hafizi ◽  
Mohammad Reza Soleymani Kermani
Keyword(s):  
Dielectric Constant ◽  
Water Content ◽  
Ground Penetrating Radar ◽  
Large Scale ◽  
Construction Material ◽  
Construction Materials ◽  
Dielectric Constants ◽  
Base Layer ◽  
Subsurface Water ◽  
Ground Penetrating

With the aid of ground penetrating radar (GPR), it is possible to evaluate physical properties of a constructed base layer in engineered structures (pavement, land consolidation projects, etc.) non-destructively, quickly, and accurately. High spatial variations of subsurface water content and deficient compaction can lead to unexpected damage and structural instability. In this research, we established a relationship between the dielectric constant, water content, and compaction, whereby, an interactive relationship between these parameters is presented. To achieve this, large-scale laboratory experiments were carried out on construction materials to simulate field conditions. According to USCS, the tested soil type was GW-GM (type E base layer according to Iran's highway specifications code). Furthermore, water content and compaction were changed between 4% -12.9% and 84.7% -94.9%, respectively. The travel-times in each test, including three profiles with more than 210 traces, are measured automatically. Additionally, the calculated dielectric constants were compared with the Topp and Roth equations. R-square and RMS error of the final interactive equation between dielectric constant and water content-compaction were 0.95 and 0.41, respectively. Moreover, the sensitivity analysis of the proposed interactive equation shows that changes in water content of soil have greater impact on dielectric constant than soil compaction changes. The data also indicate the importance of considering the compaction changes of soil to reduce the error in dielectric constant estimation.

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