Forward Analysis of GPR for Underground Pipes Using CUDA-Implemented Conformal Symplectic Euler Algorithm

Ground-penetrating radar (GPR) is widely used in the detection and positioning of underground facilities. Through the inversion analysis of echo signals of GPR, information such as pipe material, burial depth and location of pipelines can be obtained. Unfortunately, underground pipelines are cylindr...

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Bibliographic Details
Published inIEEE access Vol. 8; pp. 205590 - 205599
Main Authors Lei, Jianwei, Xue, Binghan, Fang, Hongyuan, Li, Yinping, Yang, Man
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Summary:Ground-penetrating radar (GPR) is widely used in the detection and positioning of underground facilities. Through the inversion analysis of echo signals of GPR, information such as pipe material, burial depth and location of pipelines can be obtained. Unfortunately, underground pipelines are cylindrical, the ladder approximation method used in traditional forward models produces certain errors. In this study, an accurate and efficient numerical model of GPR forward model in underground pipelines is established using symplectic Euler algorithm, graphics processing unit (GPU) acceleration technology and surface conformal technology. With a Ricker wavelet pulse as the GPR source, the convolution perfectly matched layer (CPML) is incorporated in the symplectic Euler algorithm and shown to be effective to truncate the symplectic Euler computational domain. Through the simulation study of different underground pipeline models, GPR image characteristics of the metal pipeline, plastic pipeline and concrete pipeline filled with air and water are obtained. According to the numerical simulation results, parallel conformal symplectic Euler algorithm effectively reduces the false diffracted waves caused by ladder approximation and improves the computational efficiency of the model in metallic and non-metallic media.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3037811