• National-scale mineral potential model of Cenozoic HMS REE systems developed for Australia. • Hybrid data- and knowledge-driven approach reduces exploration search space by 78 %. • Four mineral system components predict 90 % of known deposits in 22 % of continental area. • Identifies new and existing regions of high potential for Cenozoic HMS REE systems. • Mineral systems framework applied to surficial, transported (secondary) mineral systems. Heavy mineral sand rare earth element (HMS REE) deposits are globally important strategic critical mineral resources. Key minerals include ilmenite, rutile and zircon, along with REE-bearing minerals such as monazite and xenotime. These are important commodities for modern high-tech manufacturing applications, as well as for national security and the energy transition to net zero emissions. Recognizing their economic and strategic significance, a national-scale mineral potential assessment for Cenozoic HMS REE mineral systems in Australia has been undertaken using a hybrid data- and knowledge-driven approach. Analysis of mineral system components identified five key mappable criteria with statistically significant relationships to known HMS REE mineralization. Modelling of these datasets successfully predicts 90 % of known deposits in 22 % of the continental area, reducing the exploration search space by 78 %. The model highlights known and previously unrecognized regions of high geological potential for Cenozoic HMS REE mineral systems. Known areas include onshore basins in southern Australia, and regions along the southwestern and southeastern coastlines; settings with current or paleo- wave-dominated oceanic conditions and related coastal zone processes. Newly identified regions of elevated prospectivity in northern Australia require further investigation to validate model predictions. This study highlights how publicly available government geoscience data can be integrated using a geologically and statistically robust framework to generate national-scale mineral potential models with strong predictive performance. It also demonstrates that traditional mineral system models for assessing primary deposit prospectivity can, with appropriate interpretations, be applied to less conventional mineral systems, such as secondary (transported) surficial mineral systems.
McPherson et al. (Sun,) studied this question.