Abstract
Spinel group minerals, found within various rock types, exhibit distinct categorizations based on their host rocks. According to Barnes and Roeder (2001), these minerals can be classified into eight primary groups, each further subdivided into variable numbers of subgroups that can be related to a particular tectonic setting. This classification is based on the cations corresponding to the end-members of the spinel prism and is traditionally analyzed in this prismatic space or using projections of it. In this prismatic representation, several categories tend to overlap, making it impossible to determine which is the tectonic environment in that scenario. An alternative to solve this problem is to generate representations of these groups considering more attributes, making the most of the many values measured during the geochemical analysis. In this paper, we present SpinelVA, a visual exploration tool that integrates Machine Learning techniques and allows the identification of groups using the cations considered by Barnes and Roeder and some additional ones obtained from chemical analysis. SpinelVA allows us to know the tectonic environment of unknown samples by categorizing them according to the Barnes and Roeder classification. Additionally, SpinelVA integrates a collection of visual analysis techniques alongside the already used spinel prism projections and provides a set of interactions that assist geologists in the exploration process. Users can perform a complete data analysis by combining the proposed techniques and associated interactions.
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Availability of Data and Material
The data is available for download at the link: https://github.com/visualprojects/spinelVA
Code Availability
The application can be freely accessed at the website https://icic.uns.edu.ar/geoviz/ from any browser.
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Funding
This work was partially supported by PGI 24/N048 and PGI 24/ZN38 research grants from the SecretarÃa General de Ciencia y TecnologÃa, Universidad Nacional del Sur (Argentina), and by 28720210100824CO (PIBAA) granted by National Council for Scientific and Technical Research (CONICET).
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Communicated by: H. Babaie.
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Antonini, A.S., Luque, L., Ferracutti, G.R. et al. SpinelVA. A new perspective for the visual analysis and classification of spinel group minerals. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01393-5
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DOI: https://doi.org/10.1007/s12145-024-01393-5