1. Stochastic post-processing of the deterministic boundary element modelling of the transient electric field from GPR dipole antenna propagating through lower half-space
- Author
-
D. Poljak, S. Sesnic, S. Lallechere, and K. el Khamlichi Drissi
- Subjects
0209 industrial biotechnology ,Computational Mechanics ,02 engineering and technology ,Space (mathematics) ,deterministic boundary element modelling ,ground penetrating radar ,hallen integral equation ,stochastic collocation method ,time domain ,transmitted field ,law.invention ,020901 industrial engineering & automation ,Optics ,law ,Electric field ,0202 electrical engineering, electronic engineering, information engineering ,Time domain ,Dipole antenna ,Boundary element method ,Physics ,business.industry ,Applied Mathematics ,Mathematical analysis ,Lower half ,Computer Science Applications ,Computational Mathematics ,Modeling and Simulation ,Ground-penetrating radar ,020201 artificial intelligence & image processing ,Transient (oscillation) ,business - Abstract
The paper deals with time domain-deterministic stochastic assessment of a transient electric field generated by a ground penetrating radar (GPR) dipole antenna and transmitted into a lower half-space. The deterministic time domain formulation is based on the space-time Hallen integral equation for half-space problems. The Hallen equation is solved via the Galerkin–Bubnov variant of the Indirect Boundary Element Method (GB-IBEM) and the space-time current distribution along the dipole antenna is obtained, thus providing the field calculation. The field transmitted into the lower medium is obtained by solving the corresponding field integrals. As GPR systems are subjected to a rather complex environment, some input parameters, for example the antenna height over ground or earth properties, are partly or entirely unknown and, therefore, a simple stochastic collocation (SC) method is used to properly access relevant statistics about GPR time responses. The SC approach also aids in the assessment of corresponding confidence intervals from the set of obtained numerical results. The expansion of statistical output in terms of mean and variance over a polynomial basis, via the SC method, is shown to be a robust and efficient approach providing a satisfactory convergence rate.
- Published
- 2017