Data-driven classification of bedrocks by the measured uranium content using self-organizing maps

Highlights: • Uranium mapping using measured uranium content and bedrock geology. • An unsupervised machine learning approach for mapping uranium-rich geology. • Classification of bedrock types using self-organizing maps. • The geogenic aspect of radon hazard mapping. • Data-driven geo-mapping. Uranium is a naturally occurring element that can be found almost everywhere in rocks and soils throughout the earth's crust. One of its decay products, radon, is of gaining concern in recent years because this colourless, odourless, tasteless gas is proven to be responsible for many lung cancer cases each year. Analysing the spatial distribution of the uranium concentration in the ground surface can help predict radon hazard regions. In this study, two types of uranium measurements – airborne gamma-ray spectrometry (AGRS) and ground-based rock sample analysis via inductively coupled plasma mass spectrometry (ICP-MS) – are calibrated for the purpose. The two types of data with different sampling schemes are found to have a reasonable correlation to each other when using the mapped geology as categorical units. This finding confirms the feasibility of using geological maps as a first-order predictor to map uranium, and further radon, in a larger scale. We then apply the self-organizing maps (SOM) technique for a data-driven classification of rock types based on the measured uranium content. The presented study area is located at mid-Norway in the Trøndelag county, the same study will be performed in other regions across Norway where both types of measurements are in abundance. This study contributes to an on-going project to map radon hazard zones throughout Norway. While the radon hazard is defined by the indoor radon level which is affected by two folds of factors – geogenic (uranium-rich subsurface) and anthropogenic (dwelling type, indoor air exchange, etc.), our work aims to single out the geogenic factor. Comparing to the current national Radon Awareness Map of Norway (URL: http://geo.ngu.no/kart/radon/), where bedrocks were categorized by their likelihood of hosting elevated indoor radon, our approach utilizes measured uranium concentration of the ground which has a more direct link to the bedrock types.