Can uncertainty in geological cross-section interpretations be quantified and predicted?

Explicit interpretation of geological data by geologists forms the basis of many geological interpretations. However, quantitative, statistically valid research into how accurate and precise these interpretations of geological data are, and hence their uncertainty, is limited. As a result, the way that uncertainty differs between geological locations is poorly quantified and cannot be predicted. Here we show that uncertainty in cross-section interpretations varies significantly between different geological locations, and we examine the controls on this uncertainty. Using two cross-section interpretation experiments, through non-layer-cake superficial strata, we observe two distinct behaviors of uncertainty. In the first case, from Glasgow, errors in prediction of the depth of the rockhead from surrounding borehole data range from +18 m to -16 m of its actual position with a standard deviation of 6.03 m. The magnitude of this uncertainty, as measured by its mean squared value, is predictable from multiple factors relating to both the borehole data given to the geologist and their interpretation workflow. In the second case, from Manchester, the range in the predicted location of rockhead is +7 m to -9 m of the actual depth, with a standard deviation of the error of 2.69 m, and the uncertainty is only predictable from proximity to a cross-section intersection. We contrast these results with a previous experiment, on layer-cake strata from London, with an error range of +/- 7 m (standard deviation 2.9 m) and where the mean square error was predictable from the experience of the interpreting geologist and the distance to the nearest borehole. Our results show that while one assumption for predicting uncertainty may be appropriate in one case, it cannot be generically applied to other cases. We conclude that care is needed when predicting uncertainty in geological cross sections from parameters associated with initial borehole data, such as data density, and further experiments are required to map out the differing behaviors of uncertainty in geological interpretation, if uncertainty is to be predicted from prior information.