Coal seam thickness estimation using GPR and higher order statistics - the near-surface case

Abstract

A novel pattern recognition-based approach to detect near-surface interfaces using ground penetrating radar (GPR) has been reported in [1]. The approach was used to successfully detect interfaces within 5 cm of the ground surface. This technique has been adapted for the important task of layer thickness estimation in the near-surface range. This is inherently a difficult problem to solve in practice because the radar echo is often dominated by unwanted components such as antenna crosstalk and ring-down, ground reflection effects and clutter. Features derived from the bispectrum and a nearest-neighbour classifier have been utilized for this processing task. It is shown that unlike traditional second order correlation based methods such as matched filtering which can fail in known conditions, layer thickness estimation using this approach can be reliably extended to the near-surface region.