Numerical modelling of ground‐penetrating radar response from rough subsurface interfaces

Abstract

Ground‐penetrating radar (GPR) modelling is employed to study the electromagnetic wave scattering that emanates from a rough subsurface interface. The numerical analysis is achieved by using a finite‐difference time‐domain numerical modelling algorithm. For the 2D GPR models, the rough interfaces are generated using both Gaussian and fractal statistics. For a given root mean square interface roughness height, we study the effect that the change in correlation length has on the incident wavefield for a randomly generated surface with a Gaussian roughness spectrum. Similarly, for an interface generated using fractal statistics, we examine the effect of fractal dimension for two different values of maximum wavenumber. For the Gaussian case, the correlation length is the most important parameter in defining the target signature. But, when fractals are used, an increase in clutter is observed as the fractal dimension increases. In addition, the clutter is more pronounced when the highest value of maximum wavenumber is used. Detection performance is evaluated by utilising 3D ground‐penetrating radar models. Comparing the 3D models with and without the presence of a rough subsurface interface, increased wavefield attenuation and strong depolarization effects were observed from a fractal subsurface terrain.

A review of practical numerical modelling methods for the advanced interpretation of ground‐penetrating radar in near‐surface environments

“…Hastings et al ; Zhang et al ; Dogaru and Carin ; Saillard and Sentenac ; Saillard and Soriano ), there has been limited research into the GPR‐related FDTD modelling of such problems. Recently, Lampe and Holliger () and Giannopoulos and Diamanti (, ) have applied fractal and Gaussian selection methods to the allocation of material and geometrical properties both within and on the surface of buried targets. Their methods use the inherent sub‐wavelength nature of the FDTD grid to provide a random, yet realistic, variation of permittivity, conductivity and/or shape about a given criterion (i.e.…”

Inversion of dispersive GPR pulse propagation in waveguides with heterogeneities and rough and dipping interfaces

“…The solution is obtained in an iterative fashion directly in the time domain because FDTD is effectively a wave propagator. GprMax3D is part of a suite of GPR modelling algorithms under the name GprMax which has been used for a wide range of GPR simulations by a number of researchers (Giannopoulos and Diamanti, 2008;Jeannin et al, 2006;Persico and Soldovieri, 2008;Saintenoy et al, 2008;Tsoflias and Becker, 2008;Wilson et al, 2009). Some of its features included are: Perfectly Matched Layer (PML) boundary conditions to efficiently truncate the computational domain (Gedney, 1996), modelling of frequency dependent materials, interface roughness, heterogeneous media properties and fine features (Diamanti and Giannopoulos, 2009;Giannopoulos, 2005Giannopoulos, , 2008Giannopoulos and Diamanti, 2003, 2005.…”

“…We adopted the procedure used by Giannopoulos and Diamanti (2008) to generate a rough interface terrain for our synthetic models. We applied a (k r β/2 ) − 1 radial filter on a matrix of complex coefficients, H st , obtained after the Fourier transformation of a Gaussian white noise matrix.…”

Utilities detection through the sum of orthogonal polarization in 3D georadar surveys

“…Wave propagation in anisotropic and heterogeneous media has a significant influence on the backscattered wavelet that is collected by the receiver antenna (Lampe and Holliger ; Giannopoulos and Diamanti ; Sena et al . ).…”