A predictive model for Canadian carbonatite-hosted REE ± Nb deposits is presented herein. This model was developed by integrating diverse data layers derived from geophysical, geochronological, and geological sources. These layers represent the key components of carbonatite-hosted REE ± Nb mineral systems, including the source, transport mechanisms, geological traps, and preservation processes. Deep learning algorithms were employed to integrate these layers into a comprehensive predictive framework. Here is a link to the publication that describes this product: https://link.springer.com/article/10.1007/s11053-024-10369-7

This model is derived from geological and geophysical data, which is processed using deep learning and natural language processing techniques. Displayed is a Pan-Canadian probability map indicating the likelihood of discovering next-generation lithium-cesium-tantalum (LCT) pegmatites. This map was generated using known Canadian LCT pegmatites and their associated geospatial features, incorporating geological and geophysical data analyzed through deep learning and natural language processing techniques. Higher probability values highlight areas with an increased likelihood of hosting next-generation deposits, making this map a valuable tool for decision-making.