Inversion and sensitivity analysis of GPR data with waveguide dispersion using Markov Chain Monte Carlo simulation

GPR data with waveguide dispersion are known to be very difficult to interpret using traditional inversion methods. Recently, an algorithm was developed that inverts dispersive GPR data using dispersion curves. Here we analyze the sensitivity of this algorithm, and determine posterior probability density functions of the inverted parameters using Markov Chain Monte Carlo simulation. This method is especially designed to work well in the presence of measurement and model error, and is applied to measured CMP data assuming a two layered subsurface. Lower frequencies determine the inverted permittivity of the lower halfspace, whereas higher frequencies contain information about the waveguide height and its permittivity. Removing the beginning or end of the dispersion curve significantly increases parameter uncertainty.