The estimation of buried empty cylindrical tubes characteristics using GPR

This paper proposes a method to estimate three parameters: the radius, the depth of buried empty cylindrical tubes and the dielectric constant of the surrounding medium, by Ground Penetration Radar (GPR). These parameters are detected and characterized by radargrams. Those radargrams contain a parabolic shape that indicates the presence of target. This is can be achieved through two major phases: the processing stage of the electromagnetic (EM) signals which are received by the GPR. This stage is followed by another one which is the fitting curve of the parabolic shape appeared in the radargram. Finally, the results clearly indicate that this method is perfectly able to estimate the depth within 1.66%, mean average error rate and the relative permittivity of the emulsion of 3.41% and that the radius of 29.52%, whish justify and validates the model used.

[1]  Farid Melgani,et al.  Automatic Detection and Classification of Buried Objects in GPR Images Using Genetic Algorithms and Support Vector Machines , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[2]  Imad L. Al-Qadi,et al.  Measuring Rebar Cover Depth in Rigid Pavements with Ground-Penetrating Radar , 2005 .

[3]  Antonios Giannopoulos,et al.  Modelling ground penetrating radar by GprMax , 2005 .

[4]  D. Arosio.  Rock fracture characterization with GPR by means of deterministic deconvolution , 2016 .

[5]  Craig Warren,et al.  An advanced GPR modelling framework: The next generation of gprMax , 2015, 2015 8th International Workshop on Advanced Ground Penetrating Radar (IWAGPR).

[6]  D. Jongmans,et al.  Multiconfiguration GPR measurements for geometric fracture characterization in limestone cliffs (Alps) , 2006 .

[7]  S. Shihab,et al.  Radius Estimation for Cylindrical Objects Detected by Ground Penetrating Radar , 2005 .

[8]  Raffaele Persico,et al.  Design of Advanced GPR Equipment for Civil Engineering Applications , 2015 .

[9]  Paolo Gamba,et al.  Neural detection of pipe signatures in ground penetrating radar images , 2000, IEEE Trans. Geosci. Remote. Sens..

[10]  Waleed Al-Nuaimy,et al.  Image processing and neural network techniques for automatic detection and interpretation of ground penetrating radar data , 2002 .

[11]  Nada Milisavljevic,et al.  Prediction of the effects of soil and target properties on the antipersonnel landmine detection performance of ground-penetrating radar: A Colombian case study , 2007 .

[12]  Kamel Besbes,et al.  Peak detection of GPR data with lifting wavelet transform (LWT) , 2017, 2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET).

[13]  Serena Matucci,et al.  The estimation of buried pipe diameters using ground penetrating radar , 2005 .

[14]  Huanhuan Chen,et al.  Probabilistic robust hyperbola mixture model for interpreting ground penetrating radar data , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).