Application of Principal Component Analysis (PCA) and K-Means for Stratigraphic Unit Classification in Geochemical Samples from the Misti Volcano
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Abstract
This study presents a comprehensive analysis of the Pacheco stage tephra deposits from Misti Volcano, Arequipa, Peru, using dimensionality reduction techniques through Principal Component Analysis (PCA) and clustering via K-Means. Geochemical samples collected between 2015 and 2023 were analyzed, focusing on major and trace elements such as Ti, Fe, MgO, and SiO₂. PCA allowed for dimensionality reduction, revealing that the first two principal components explained 34.27% of the total variance, with variables such as Ti and Fe having the most significant influence. Subsequently, the K-Means algorithm identified four geochemically differentiated clusters, which were compared with known stratigraphic units, showing a notable correlation with units such as Ponche Gris and La Rosada. The results suggest that these clusters reflect variations in magmatic processes and eruptive phases, offering new insights into the geochemical evolution of Misti Volcano. Future work could include isotopic analysis and the integration of deep learning techniques to enhance the understanding of magmatic sources and their influence on volcanic stratigraphy.
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