The Research of the Ground Penetrating Radar Wave Velocity Estimation Method Based on QR Decomposition

2014 ◽  
Vol 522-524 ◽  
pp. 1197-1201 ◽  
Author(s):  
Xu Qiao ◽  
Wan Jun Ji ◽  
Kai Zhu ◽  
Feng Yang
Keyword(s):  
Real Time ◽  
Wave Velocity ◽  
Ground Penetrating Radar ◽  
Estimation Method ◽  
Time Estimation ◽  
Spatial Location ◽  
Qr Decomposition ◽  
Velocity Estimation ◽  
Ground Penetrating ◽  
Radar Wave

Ground penetrating radar (GPR) is a kind of geophysical instruments, which has been widely applied in geology, engineering, resource, environment, military etc. The method mentioned in this paper based on QR decomposition, by solving equations to find the estimated value of the radar wave velocity. This method delivers the features of real-time, accurate, and the accuracy required is not high. It is suitable for real time estimation of GPR detection. By estimating the wave velocity, we can identify the spatial location of underground target. This method provides a convenient way for the detection of underground target.

scholarly journals Researching migration methods, entropy and energy diagram to process ground penetrating radar data

2017 ◽  
Vol 17 (4B) ◽  
pp. 167-174
Author(s):  
Van Nguyen Thanh ◽  
Thuan Van Nguyen ◽  
Trung Hoai Dang ◽  
Triet Minh Vo ◽  
Lieu Nguyen Nhu Vo
Keyword(s):  
Electromagnetic Wave ◽  
Wave Velocity ◽  
Ground Penetrating Radar ◽  
Radar Data ◽  
Maximum Energy ◽  
Velocity Estimation ◽  
Data Migration ◽  
Minimum Entropy ◽  
Energy Diagram ◽  
Ground Penetrating

Electromagnetic wave velocity is the most important parameter in processing ground penetrating radar data. Migration algorithm which heavily depends on wave velocity is used to concentrate scattered signals back to their correct locations. Depending wave velocity in urban area is not easy task by using traditional methods (i.e., common midpoint). We suggest using entropy and energy diagram as standard for achieving suitable velocity estimation. The results of one numerical model and areal data indicate that migrated section using accurate velocity has minimum entropy or maximum energy. From the interpretation, size and depth of anomalies are reliably identified.

scholarly journals Practical Approach for High-Resolution Airport Pavement Inspection with the Yakumo Multistatic Array Ground-Penetrating Radar System

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2684 ◽  
Author(s):  
Li Yi ◽  
Lilong Zou ◽  
Motoyuki Sato
Keyword(s):  
Ground Penetrating Radar ◽  
Estimation Method ◽  
Ground Truth ◽  
Radar System ◽  
Velocity Estimation ◽  
Velocity Spectrum ◽  
Airport Pavement ◽  
Ground Penetrating ◽  
Airport Site ◽  
Good Agreement

It is important to identify the thin cracks within the airport pavement layers. To achieve this goal, a practical interferometric approach using the Yakumo multistatic ground-penetrating radar system was developed to detect the slight variability in wave propagation velocity and/or thickness caused by the thin cracks. In comparison with the conventional common midpoint (CMP) velocity estimation method, the proposed method can provide much higher-resolution estimations of slight deviations in the velocity and thickness from their corresponding reference values in the undamaged asphalt through the comparison of two CMP datasets. These deviations can be obtained analytically instead of graphically extracted from the CMP velocity spectrum. The proposed approach was not only analyzed using the simulated datasets, but also practically demonstrated at both an experimental model site and an actual airport site. In the simulation tests, velocity deviations on the order of a few millimeters per nanosecond were detected, and the experimental results shows good agreement with the ground truth and coring samples. This method provides a novel way to inspect partially damaged pavement when the thin cracks are difficult to detect using the reflected signals.

GPR Wave Velocity Estimation Based on the Method of Curves Character and K-Means Clustering

2014 ◽  
Vol 533 ◽  
pp. 242-246
Author(s):  
Xu Qiao ◽  
Hao Zhang ◽  
Tong Liu ◽  
Yuan Yuan Zhang ◽  
Yun Hai Xia ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Ground Penetrating Radar ◽  
Clustering Algorithm ◽  
Velocity Estimation ◽  
New Method ◽  
Radar Image ◽  
Measurement Results ◽  
Ground Penetrating

According to the need of ground penetrating radar (GPR) measurement of underground targets, we proposed a new method for the prediction of wave velocity. This method based on radar image curves and K-means clustering algorithm, and we can predict the wave velocity accurately. Proved by the experiment, the calculation precision of this method is higher. Although there are some errors in measurement results, it has good robustness to get it corrected.

scholarly journals Geostatistical inference using crosshole ground-penetrating radar

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. J29-J41 ◽  
Author(s):  
Majken C. Looms ◽  
Thomas M. Hansen ◽  
Knud S. Cordua ◽  
Lars Nielsen ◽  
Karsten H. Jensen ◽  
...  
Keyword(s):  
Moisture Content ◽  
Wave Velocity ◽  
Ground Penetrating Radar ◽  
Unsaturated Zone ◽  
Accurate Estimation ◽  
Full Potential ◽  
Field Site ◽  
Correlation Structures ◽  
Ground Penetrating ◽  
Radar Wave

High-resolution tomographic images obtained from crosshole geophysical measurements have the potential to provide valuable information about the geostatistical properties of unsaturated-zone hydrologic-state variables such as moisture content. Under drained or quasi-steady-state conditions, the moisture content will reflect the variation of the physical properties of the subsurface, which determine the flow patterns in the unsaturated zone. Deterministic least-squares inversion of crosshole ground-penetrating-radar (GPR) traveltimes result in smooth, minimum-variance estimates of the subsurface radar wave velocity structure, which may diminish the utility of these images for geostatistical inference. We have used a linearized stochastic inversion technique to infer the geostatistical properties of the subsurface radar wave velocity distribution using crosshole GPR traveltimes directly. Expanding on a previous study, we have determined that it is possible to obtain estimates of global variance andmean velocity values of the subsurface as well as the correlation lengths describing the subsurface velocity structures. Accurate estimation of the global variance is crucial if stochastic realizations of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance function and data noise level. In addition, we have tested the methodology on traveltime data collected at a field site in Denmark. There, inferred correlation structures indicate that structural differences exist between two areas located approximately [Formula: see text] apart, an observation confirmed by a GPR reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisture-content measurements, obtained gravimetrically from samples collected at the field site.

Semi-intrusive determination of ground penetrating radar wave velocity

2001 ◽  
Vol 34 (2) ◽  
pp. 163-171 ◽  
Author(s):  
M.O Gordon ◽  
M.S.A Hardy ◽  
A Giannopoulos
Keyword(s):  
Wave Velocity ◽  
Ground Penetrating Radar ◽  
Ground Penetrating ◽  
Radar Wave

Laboratory test of snow wetness influence on electrical conductivity measured with ground penetrating radar

2009 ◽  
Vol 40 (1) ◽  
pp. 33-44 ◽  
Author(s):  
Nils Granlund ◽  
Angela Lundberg ◽  
James Feiccabrino ◽  
David Gustafsson
Keyword(s):  
Electrical Conductivity ◽  
Electrical Properties ◽  
Laboratory Test ◽  
Liquid Water ◽  
Ground Penetrating Radar ◽  
Wave Attenuation ◽  
Radar Measurements ◽  
Ground Penetrating ◽  
Radar Wave ◽  
The Relationship

Ground penetrating radar operated from helicopters or snowmobiles is used to determine snow water equivalent (SWE) for annual snowpacks from radar wave two-way travel time. However, presence of liquid water in a snowpack is known to decrease the radar wave velocity, which for a typical snowpack with 5% (by volume) liquid water can lead to an overestimation of SWE by about 20%. It would therefore be beneficial if radar measurements could also be used to determine snow wetness. Our approach is to use radar wave attenuation in the snowpack, which depends on electrical properties of snow (permittivity and conductivity) which in turn depend on snow wetness. The relationship between radar wave attenuation and these electrical properties can be derived theoretically, while the relationship between electrical permittivity and snow wetness follows a known empirical formula, which also includes snow density. Snow wetness can therefore be determined from radar wave attenuation if the relationship between electrical conductivity and snow wetness is also known. In a laboratory test, three sets of measurements were made on initially dry 1 m thick snowpacks. Snow wetness was controlled by stepwise addition of water between radar measurements, and a linear relationship between electrical conductivity and snow wetness was established.

scholarly journals On the Introduction of Canny Operator in an Advanced Imaging Algorithm for Real-Time Detection of Hyperbolas in Ground-Penetrating Radar Data

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 541 ◽  
Author(s):  
Željko Bugarinović ◽  
Lara Pajewski ◽  
Aleksandar Ristić ◽  
Milan Vrtunski ◽  
Miro Govedarica ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Penetrating Radar ◽  
Urban Areas ◽  
Radar Data ◽  
Detection Algorithm ◽  
Advanced Imaging ◽  
Canny Operator ◽  
Imaging Algorithm ◽  
Canny Edge ◽  
Ground Penetrating

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB.

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