Pink crystal spodumene. Credit score: Robert Lavinsky
By harnessing patterns in mineral associations, a brand new machine-learning mannequin can predict the places of minerals on Earth and doubtlessly, different planets. This development is of immense worth to science and business, as they regularly discover mineral deposits to unravel the planet’s historical past and to mine sources for sensible purposes, reminiscent of rechargeable batteries.
A staff led by Shaunna Morrison and Anirudh Prabhu aimed to develop a way for figuring out the incidence of specific minerals, an goal that has historically been thought-about as a lot an artwork as it’s a science. This course of has typically been depending on particular person expertise together with a wholesome dose of luck.
The staff 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