Forward Simulation Of Ground Penetration Radar Based On GprMax For The Road Cavity

In recent years, lots of road collapses are reported by news, which do great harm to people’s lives and properties. Ground penetrating radar(GPR) has the characteristics of high efficacy, non-destructive, high resolution. And the dielectric constant difference between road structure layer and the cavity beneath the road structure is very large, therefore, it is suitable to detect cavity beneath the road by the GPR. However, in the process of practical application, due to a lack of ground penetrating radar abnormal image evaluation criteria and the image interpretations of GPR have multiple solutions with subjective experience. It is difficult to obtain ideal results in the detection for the cavity beneath the road.It is of great significance to make full use of forward simulation by computer. And it is necessary to study all kinds of typical cavity models of electromagnetic wave propagation which will contribute a lot to identify cavity beneath the road in practical application.This paper is based on the finite difference time domain method(FDTD),with the establishment of aeration and watery cavity models, uses the GprMax and Matlab programming to realize forward simulation and makes a analysis of the forward simulation image characteristics.The main contents are as follows:Firstly, according to the characteristic of the cavity forming, the cavities are divided into aeration and watery cavity and make forward simulation respectively to make a conclusion of how to distinguish the two types of cavities.Secondly, according to different shapes, different depths, different sizes, different kinds of excitation sources, different center frequencies and different dielectric constants and conductivities, analyzing the characteristic of the GPR image under different conditions.Finally, compared the forward simulation with the wireless GPR instance, verifying the validity of the forward simulation.In this paper, the forward simulation results contribute to the actual detection and recognition of cavities as well as GPR image interpretation, which can be used for the GPR application in cavity detection theory.