The use of 3D models to view complex and diverse geoscience datasets is now common practice for conceptual model evolution, communication to stakeholders, or for testing hypotheses. When applying these models it is important to recognize that their ability to replicate the true situation is controlled by the data used to generate the model and the model algorithms. For the models to be applied correctly the model uncertainty needs to be identified and, where possible, quantified. A method to quantify the uncertainty associated with geological surfaces in a 3D model is presented and tested. Kernel density smoothing and resampling of borehole locations along with expert–user interaction are utilized to provide an estimate of the uncertainty in a geological surface based on data quality, data density and geological complexity. The method is applied to a 3D geological model of shallow superficial deposits, where a sequence of river terrace gravels and alluvial deposits overlie mudstone bedrock. Outcomes indicate that the uncertainty model predictions match well with expert judgement.