GPR Detection of Tunnel Lining Cavities and Reverse-time Migration Imaging

Abstract

Tunnel lining cavity is one of the diseases that threaten the safe operation of tunnel, so it has been the attention of test personnel. As a nondestructive testing method, Ground Penetrating Radar (GPR) has been widely used in tunnel lining disease inspection. In order to improve the detection accuracy and success rate of tunnel lining cavities, this paper applies the reverse-time migration (RTM) algorithm to image tunnel lining cavities with high accuracy. Firstly, the principle of GPR pre-stack RTM is described in detail, in which the finite difference time domain (FDTD) method is used to calculate the forward and backward electromagnetic fields, and the normalized cross-correlation imaging condition is used to obtain the RTM results. On this basis, the GPR RTM program is compiled and applied to the simulated dataset of a typical lining cavity GPR model. The comparison of RTM and Kirchhoff migration result showed that the diffracted wave in the steel bar and cavities has good convergence, and the profile’s resolution is improved effectively; In contrast to the Kirchhoff migration result, the RTM result can better focus the radar diffraction wave energy of the steel bars and cavities on the real space location generated by them, weaken the shielding effect of the steel mesh, and suppress multiple interference and clutter scattering waves. Finally, a physical model of lining cavities was detected by GPR, and the RTM algorithm was applied to data processing and interpretation. The RTM delimits the positions of steel mesh and cavities effectively, and the interpretation accuracy of GPR profile has been improved, which provide a scientific and effective way for accurately delimit the scope of tunnel lining cavities.