A Denoising Method for Detecting Reflected Waves from Buried Objects by Ground-penetrating Radar

2012 ◽  
Vol 132 (6) ◽  
pp. 631-641 ◽  
Author(s):  
Makoto Kobayashi ◽  
Kazushi Nakano
2021 ◽  
Vol 13 (9) ◽  
pp. 1846
Author(s):  
Vivek Kumar ◽  
Isabel M. Morris ◽  
Santiago A. Lopez ◽  
Branko Glisic

Estimating variations in material properties over space and time is essential for the purposes of structural health monitoring (SHM), mandated inspection, and insurance of civil infrastructure. Properties such as compressive strength evolve over time and are reflective of the overall condition of the aging infrastructure. Concrete structures pose an additional challenge due to the inherent spatial variability of material properties over large length scales. In recent years, nondestructive approaches such as rebound hammer and ultrasonic velocity have been used to determine the in situ material properties of concrete with a focus on the compressive strength. However, these methods require personnel expertise, careful data collection, and high investment. This paper presents a novel approach using ground penetrating radar (GPR) to estimate the variability of in situ material properties over time and space for assessment of concrete bridges. The results show that attributes (or features) of the GPR data such as raw average amplitudes can be used to identify differences in compressive strength across the deck of a concrete bridge. Attributes such as instantaneous amplitudes and intensity of reflected waves are useful in predicting the material properties such as compressive strength, porosity, and density. For compressive strength, one alternative approach of the Maturity Index (MI) was used to estimate the present values and compare with GPR estimated values. The results show that GPR attributes could be successfully used for identifying spatial and temporal variation of concrete properties. Finally, discussions are presented regarding their suitability and limitations for field applications.


2021 ◽  
Vol 35 (11) ◽  
pp. 1437-1438
Author(s):  
Eder Ruiz ◽  
Daniel Chaparro-Arce ◽  
John Pantoja ◽  
Felix Vega ◽  
Chaouki Kasmiv ◽  
...  

In this paper, the singularity expansion method (SEM) is used to improve the signal-to-clutter ratio of radargrams obtained with a ground penetration radar (GPR). SEM allows to select the poles of the GPR signals corresponding to unwanted signals, clutter, and also reflections of specific buried objects. A highly reflective metallic material was used to assess the use of SEM as a tool to eliminate unwanted reflections and signals produced by a GPR. Selected clutter poles are eliminated from each frame of the SAR image in order to keep only desired poles for analysis. Finally, the reconstructed radargram obtained applying SEM is compared with the image obtained using a well-known processing technique. Results show that the proposed technique can be used to straightforwardly remove undesired signals measured with GPRs.


2016 ◽  
Author(s):  
Hamza Reci ◽  
Tien Chinh Maï ◽  
Zoubir Mehdi Sbartaï ◽  
Lara Pajewski ◽  
Emanuela Kiri

Abstract. This paper presents the results of a series of laboratory measurements carried out to study how the Ground Penetrating Radar (GPR) signal is affected by moisture variation in wood material. The effects of the wood fiber direction, with respect to the polarisation of the electromagnetic field, are investigated. The relative permittivity of wood and the amplitude of the electric field received by the radar are measured for different humidity levels, by using the direct-wave method in Wide Angle Radar Reflection configuration, where one GPR antenna is moved while the other is kept in a fixed position. The received signal is recorded for different separations between transmitting and receiving antennas. Direct waves are compared to reflected waves: it is observed that they show a different behaviour when the moisture content varies, due to their different propagation paths.


2014 ◽  
Vol 1010-1012 ◽  
pp. 1272-1275
Author(s):  
Dan Dan Liu ◽  
Zhi Qiu Yang ◽  
Chun Rui Tang

The ground penetrating radar and radar wave propagation in the subsurface environment is very complex. All kinds of noise and clutter interference is very serious, and detection echo data is a variety of with clutter. Therefore, the key techniques of data processing is to suppress clutter processing of ground penetrating radar record data. Surfacelet transform can efficiently capture and represent local surface singularities with different sizes. In order to improve the reliability of 3D ground penetrating radar detection results and accuracy, this paper presents a three-dimensional ground penetrating radar signal denoising method based on Surfacelet transform. Using Surfacelet transform and 3D context model for ground penetrating radar (GPR) analog signal to denoising, the noise in the case of low signal noise ratio (SNR) still can obtain a better result, and the simulations prove the effectiveness of the method.


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