preprints.org > environmental and earth sciences > geochemistry and petrology > doi: 10.20944/preprints202410.2434.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 30 October 2024 / Approved: 30 October 2024 / Online: 30 October 2024 (11:53:45 CET)

This paper presents a novel machine learning framework that applies evolutionary ensembles of artificial agents to mineralogical analysis and classification. The approach is based on hybridiza-tion techniques that combine diverse machine learning algorithms, creating large and effective communities of agents. These progressively mute and improve through crossover, hybridization, and selection, addressing the challenges of mineral recognition and classification from thin section images. By combining multiple machine learning techniques, the ensemble of agents autono-mously improves by evolving to adapt and enhance its ability to identify mineral species and classify different types of alterations. We detail the method, provide examples using synthetic and real data, and explore the potential to improve mineralogical analysis workflows through this dynamic, self-improving system.

Deep Learning; Mineral,; Classification; Thin section; Machine Learning; Ensemble

Environmental and Earth Sciences, Geochemistry and Petrology

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