Resource expansion with uncertainty quantification of regolith-hosted REE deposits using radiometric data

Rare earth elements (REE) are critical raw materials due to their essential role in modern technologies. In regolith-hosted REE (RH-REE) deposits a substantial fraction of the REE is present as ionically adsorbed, exchangeable cations on secondary clay minerals and amenable to mild extraction routes potentially being less environmentally disruptive than conventional hard-rock REE operations. Shallow drilling in combination with geophysical techniques are used to determine the potential of a regolith resource for economic extraction. In particular, airborne gamma-ray spectrometry provides indirect indicators of the regolith and parent lithology by mapping potassium, equivalent thorium (eTh) and equivalent uranium (eU) from low-altitude, line-spaced surveys. In a brownfield exploration context, neighboring densely drilled areas can be used as training sets to assess the potential undrilled areas. To do so, the flightline data needs to be interpolated to allow for correlation with REE concentration at borehole locations. In this paper, we show that the traditional methods of deterministic interpolation leads to an overly optimistic estimate of the REE concentration. As a solution, we propose a multi-variates stochastic simulation of the radiometric data in a Monte Carlo simulation framework. We illustrate the methodology with a case study from a RH-REE district in south-central Chile, located in the Coastal Range about 15 km NE of Concepción. We show how the uncertainty approach identifies false positives created by deterministic interpolation and allows for a more accurate understanding of risk vs return in undrilled zones of the resource. Since the large majority of online available geophysical data are deterministically interpolated products, we also conjecture that our findings have wider implications for mineral prospectivity mapping involving geophysical and geological databases.