Interpretation of sparse or incomplete datasets is a fundamental part of geology, particularly when building models of the subsurface. Available geological data are often remotely sensed (seismic data) or very limited in spatial extent (borehole data). Understanding how different datasets are interpreted and what makes an interpreter effective is critical if accurate geological models are to be created. A comparison of the interpretation outcome and techniques used by two cohorts interpreting different geological datasets of the same model, an inversion structure, was made. The first cohort consists of interpreters of the synthetic seismic image data in Bond et al. (‘What do you think this is?: “Conceptual uncertainty” in geoscience interpretation’, GSA Today, 2007, 17, 4–10, http://dx.doi.org/10.1130/GSAT01711A.1); the second cohort is new and interpreted borehole data. The outcomes of the borehole interpretation dataset support earlier findings that technique use, specifically evidence of geological evolution thought processes, results in more effective interpretation. The results also show that the borehole interpreters were more effective at arriving at the correct interpretation. Analysis of their final interpretations in the context of psychological and medical image analysis research suggests that the clarity of the original dataset, the amount of noise and white space may play a role in interpretation outcome, through enforced geological reasoning during data interpretation.