Quantifying the limited explanatory power of traditional classified geological maps

Since William Smith published his ‘Delineation of the Strata of England and Wales with part of Scotland’ in 1815, it has been the accepted practice to map geology as a collection of discrete polygons (or volumes in 3D), each representing a parcel of rock that is sufficiently self-similar in terms of its geological properties to class as a ‘unit’. However, the properties that are used to define these units, such as age, composition and texture, are themselves continuous variables more suited to modelling by regression than by classification. As such, the discrete nature of the traditional classified geological map has limited power to explain real observed values of geological properties, which vary on continuous scales, resulting in a disconnect between our geological maps and reality. In this study, we quantify the explanatory power of a traditional geological map – the British Geological Survey's 1 : 625K bedrock geology of the UK – in relation to observations of major element chemical composition, and compare this with the explanatory power provided by a Bayesian deep learning regression-based approach to ‘properties-first’ geological mapping. We find that, for our selection of elements, the traditional geological map explains between 57 and 66% of their variance, and the Bayesian deep learning approach explains between 66 and 75% of their variance, almost 10% higher explained variance than the traditional classified geological map, which equates to progress of between a fifth and quarter of the way towards achieving the ‘ultimate’ perfectly informative geological map. We discuss the implications of the traditional classified geological map's limited explanatory power and how the inherent constraint of its discrete classified representation should reasonably lead us to pursue geological mapping techniques that are free of this limitation, i.e. which are based on regression rather than classification.