Analysis of Forward Model, Data Type, and Prior Information in Probabilistic Inversion of Crosshole GPR Data

The crosshole ground penetrating radar (GPR) is a widely used tool to map subsurface properties, and inversion methods are derive electrical parameters from GPR data. In this paper, probabilistic algorithm that uses Markov chain Monte Carlo (MCMC) simulations within the Bayesian framework implemented infer posterior distribution of relative permittivity medium. Close attention paid critical elements method, including forward model, data type prior information, their influence on results investigated. First, uniform reflect lack knowledge model parameters, inversions performed using straight-ray with first-arrival traveltime data, finite-difference time-domain (FDTD) FDTD waveform respectively. cases require an unreasonable number evaluations converge, yet not able recover real field. contrast, successfully infers correct parameters. Then, smooth constraint employed as distribution. demonstrate information barely affects but significantly improves by decreasing computing time reducing uncertainties