Purple crystal spodumene. Credit score: Robert Lavinsky
By means of harnessing patterns in mineral associations, a brand new machine-learning style can are expecting the places of minerals on Earth and probably, different planets. This development is of immense price to science and trade, as they frequently discover mineral deposits to resolve the planet’s historical past and to mine assets for sensible programs, comparable to rechargeable batteries.
A workforce led through Shaunna Morrison and Anirudh Prabhu aimed to expand one way for figuring out the prevalence of specific minerals, an goal that has historically been regarded as as a lot an artwork as this is a science. This procedure has steadily been depending on person revel in in conjunction with a hearty dose of success.
The workforce created a machine learning model that uses data from the Mineral Evolution Database, which includes 295,583 mineral localities of 5,478 mineral species, to predict previously unknown mineral occurrences based on association rules.
The authors tested their model by exploring the Tecopa basin in the Mojave Desert, a well-known Mars analog environment. The model was also able to predict the locations of geologically important minerals, including uraninite alteration, rutherfordine, andersonite, and schröckingerite, bayleyite, and zippeite.
In addition, the model located promising areas for critical rare earth elements and lithium minerals, including monazite-(Ce), and allanite-(Ce), and spodumene. Mineral association analysis can be a powerful predictive tool for mineralogists, petrologists, economic geologists, and planetary scientists, according to the authors.
Reference: “Predicting new mineral occurrences and planetary analog environments via mineral association analysis” by Shaunna M Morrison, Anirudh Prabhu, Ahmed Eleish, Robert M Hazen, Joshua J Golden, Robert T Downs, Samuel Perry, Peter C Burns, Jolyon Ralph and Peter Fox, 16 May 2023, PNAS Nexus.
DOI: 10.1093/pnasnexus/pgad110
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