Geological models form the basis for scientific investigations of both the surface and subsurface of urban environments. Urban cover, however, usually prohibits the collection of new subsurface data. Therefore, models depend on existing subsurface datasets that are often of poor quality and have an uneven spatial and temporal distribution, introducing significant uncertainty. This research proposes a novel method to mitigate uncertainty caused by clusters of uncertain data points in kriging-based geological modelling. This method estimates orientations from clusters of uncertain data and randomly selects points for geological interpolation. Unlike other approaches, it relies on the spatial distribution of the data and translating geological information from points to geological orientations. This research also compares the proposed approach to locally changing the accuracy of the interpolator through data-informed local smoothing. Using the Ouseburn catchment, Newcastle upon Tyne, UK, as a case study, results indicate good correlation between both approaches and known conditions, as well as improved performance of the proposed methodology in model validation. Findings highlight a trade-off between model uncertainty and model precision when using highly uncertain datasets. As urban planning, water resources, and energy analyses rely on a robust geological interpretation, the modelling objective ultimately guides the best modelling approach.