Introduction to ground penetrating radar: Inverse scattering and data processing, by RaffaelePersico, ISBN: 978‐1‐118‐30500‐3, 392 pp,, 2014, Wiley‐IEEE Press, Hoboken, NJ, hardcover £80.50, eBook £72.99

2019 ◽  
Vol 27 (1) ◽  
pp. 53-53
Author(s):  
Neil Linford
2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Francesco Soldovieri ◽  
Erica Utsi ◽  
Raffaele Persico ◽  
Amir M. Alani

The Romano-British site of Barcombe in East Sussex, England, has suffered heavy postdepositional attrition through reuse of the building materials for the effects of ploughing. A detailed GPR survey of the site was carried out in 2001, with results, achieved by usual radar data processing, published in 2002. The current paper reexamines the GPR data using microwave tomography approach, based on a linear inverse scattering model, and a 3D visualization that permits to improve the definition of the villa plan and reexamine the possibility of detecting earlier prehistoric remains.


Author(s):  
Ilaria Catapano ◽  
Gianluca Gennarelli ◽  
Giovanni Ludeno ◽  
Francesco Soldovieri ◽  
Raffaele Persico

2011 ◽  
Vol 243-249 ◽  
pp. 5381-5385 ◽  
Author(s):  
Ji Shun Pan ◽  
Lei Yang ◽  
Yuan Bao Leng ◽  
Zhi Quan Lv

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.


2015 ◽  
Vol 25 (4) ◽  
pp. 955-960 ◽  
Author(s):  
Piotr Szymczyk ◽  
Sylwia Tomecka-Suchoń ◽  
Magdalena Szymczyk

Abstract In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.


2011 ◽  
Vol 243-249 ◽  
pp. 4351-4355
Author(s):  
Jie Liu ◽  
Yuan Shui Cheng

Stable structure of subgrade of railway is an important foundation for safety of train. Rising of speed and heavy transport tasks accelerate deterioration of ballast and increase disease of subgrade. It is more important how to detect and maintain the condition of ballast and subgrade highly effective, faster and without any destruct. In recent years, the ground penetrating radar has been an effective method for detecting and evaluation ballast and subgrade. This paper present application of detecting and evaluation ballast and subgrade by using the train-mounted-multicenter ground penetrating radar(GPR), data processing and interpretation, acknowledge and some problem.


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