Facies discrimination with electrical resistivity tomography using a probabilistic methodology: effect of sensitivity and regularisation
T. Hermans and J. Irving
Journal name: Near Surface Geophysics
Issue: Vol 15, No 1, February 2017 pp. 13 - 25
Info: Article, PDF ( 1.68Mb )
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Electrical resistivity tomography has become a standard geophysical method in the field of hydrogeology, as it has the potential to provide important information regarding the spatial distribution of facies. However, inverted electrical resistivity tomography images tend to be grossly smoothed versions of reality because of the regularisation of the inverse problem. In this study, we use a probabilistic methodology based upon co-located measurements to assess the utility of electrical resistivity tomography to identify hydrofacies in alluvial aquifers. With this methodology, electrical resistivity tomography images are interpreted in terms of the probability of belonging to pre-defined hydrofacies. We first analyse through a synthetic study the ability of electrical resistivity tomography to discriminate between different facies. As electrical resistivity tomography data suffer from a loss of sensitivity with depth, we find that low-sensitivity regions are more affected by misclassification. To counteract this effect, we adapt the probabilistic framework to include the spatially varying data sensitivity. We then apply our learning to a field case. For the latter, we consider two different regularisation procedures. In contrast to the data sensitivity that affects the facies probability to a limited amount, the regularisation can affect the probability maps more considerably because it has a strong influence on the spatial distribution of inverted resistivity. We find that a regularisation strategy based on the most realistic prior information tends to offer the most reliable discrimination of facies. Our results confirm the ability of electrical resistivity tomography surveys, when properly designed, to detect facies variations in alluvial aquifers. The method can be easily extended to other contexts.