The construction of stochastic facies-based models conditioned to ground penetrating radar images

Neural networks are used to estimate radar facies probabilities from ground penetrating radar (GPR) images, yielding stochastic facies-based models that honour the large-scale architecture of the subsurface. For synthetic GPR images, a neural network was able to correctly identify radar facies with an accuracy of over 80%. Manual interpretation of a set of 450 MHz GPR field data from the Borden aquifer resulted in the identification of four radar facies. Of these, a neural network was able to identify two with an accuracy of near 80%, one with an accuracy of 44%, and was not able to identify the fourth.