| May 16, 2023 |
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(Nanowerk News) A machine learning model can predict the locations of minerals on Earth – and potentially other planets – by taking advantage of patterns in mineral associations.
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Science and industry seek mineral deposits to both better understand the history of our planet and to extract for use in technologies like rechargeable batteries.
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| Pink crystal spodumene. (Image: Robert Lavinsky)
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Shaunna Morrison, Anirudh Prabhu, and colleagues sought to create a tool for finding occurrences of specific minerals, a task that has long been as much an art as a science, relying on individual experience, along with a healthy dose of luck.
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The team 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.
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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.
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In addition, the model located promising areas for critical rare earth element and lithium minerals, including monazite-(Ce), and allanite-(Ce), and spodumene.
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Mineral association analysis can be a powerful predictive tool for mineralogists, petrologists, economic geologists, and planetary scientists, according to the authors.
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The research was published in PNAS Nexus (“Predicting new mineral occurrences and planetary analog environments via mineral association analysis”).
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