Skip to Main Content
The use of ground penetrating radar (GPR) for detecting near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques largely ineffective in the unsupervised case. As a solution to this problem, we develop a novel algorithm which utilizes a pattern recognition-based approach using features derived from the bispectrum of the radar data. We show that, unlike traditional second order correlation based methods such as matched filtering which fail in known conditions, the new method reliably allows the determination of layer interfaces using GPR to be extended to the near surface region.