Structural model creation: the impact of data type and creative space on geological reasoning and interpretation

Interpretation of sparse or incomplete datasets is a fundamental part of geology; particularly when building models of the subsurface. Available geological data is 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, has been made. The first cohort consists of interpreters of the synthetic seismic image data in Bond et al. (2007); the second cohort is new and interpreted borehole data. The outcomes of the borehole interpretation dataset support the findings of Bond et al. (2012); that technique use, specifically evidence of geological evolution thought processes, results in more effective interpretation. The results also show 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.