Explicit consideration of measurement uncertainty during Bayesian inversion of dispersive GPR data

Thin layers in the shallow subsurface can act as waveguides and result in dispersive common midpoint (CMP) data. Recently developed algorithms, similar to those used for seismic Rayleigh inversion, are able to successfully solve the inversion problem and obtain values of the waveguide properties. Despite this progress made, parameter uncertainty has not yet been appropriately considered. In this study, we investigate the influence of measurement uncertainty on the final parameter values using a Markov Chain Monte Carlo scheme with synthetic and experimental data. Explicit consideration of measurement uncertainty increases the uncertainty of the inferred waveguide parameters, but improves the reliability of the parameter estimates. The results of this study advocate the use of an explicit definition of the measurement uncertainty in the likelihood function when inverting for waveguide properties.