Labrador Uranium Inc. announced the completion of the initial phase of its Regional Exploration Targeting, integrating a Mineral Systems Approach combined with Machine Learning, over its Central Mineral Belt Project (the “CMB” or “CMB Project”) in Central Labrador, Canada (the “CMB Project”). The study successfully defined specific areas for further work and de-risks multiple project areas at varying stages. The initial phase of Artificial Intelligence (“AI”) exploration targeting implements a Machine Learning (“ML”) workflow, targeting the potential existence of unknown uranium and copper deposits.

This was facilitated by the recent release and compilation of both public and private aeromagnetic, radiometric, and geological data over the entire Central Mineral Belt. Integration of the Mineral Systems approach, focusing on the processes of source, transport, and deposition, assists in focusing the data collection and interpretation without relying on a single deposit “model”. Using the location of known deposits and prospects allows the training of the ML algorithm, which objectively predicts the location of deposits without a preconceived notion of importance typically seen in one or more deposit types.

The primary objective of this data-driven methodology is to reduce the targeting risk over the CMB Project at an early stage, preparing more target areas for direct detection methods such as drilling.