We compare and contrast the traditional manual interpretation of AEM data of digitizing features with an automatic stochastic based method utilizing a trans-dimensional Bayesian Markov chain Monte Carlo (MCMC) algorithm. The target of interest is the base of an unconfined alluvial aquifer in western Nebraska composed of electrically resistive sand and gravels sitting upon a conductive silt and clay. The objective is to provide a 3-D surface of the base of aquifer for inclusion in groundwater models. The automatic stochastic interpretations provide a robust 3-D surface that compares well with the manually digitized surface. In some areas the stochastic methods provide a more certain interpretation than the single smooth model inversion that were used for the manual interpretation. We feel that the use of the automatic stochastic approach prior to manually inspecting the AEM sections provides substantial timesaving and confidence in the final interpreted results. . The stochastic approch provides an automatic interpretation of the layers within a section and can expedite the interpretation process. We recommend that the use of the automatic stochastic approach prior to integrating complementary data and manually inspecting AEM sections provide substantial timesaving and increased confidence in the final interpreted results. With the continued use of the AEM technique in hydrogeological framework studies a fast and efficient way of providing confident interpretations needs to be implemented.


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