Using Machine Learning for Lunar Mineralogy-I: Hyperspectral Imaging of Volcanic Samples

• URL: http://arxiv.org/abs/2503.22617v2
• Date: Mon, 07 Apr 2025 19:15:56 GMT
• Title: Using Machine Learning for Lunar Mineralogy-I: Hyperspectral Imaging of Volcanic Samples
• Authors: Fatemeh Fazel Hesar, Mojtaba Raouf, Peyman Soltani, Bernard Foing, Michiel J. A. de Dood, Fons J. Verbeek, Esther Cheng, Chenming Zhou,
• Abstract summary: This study examines the mineral composition of volcanic samples similar to lunar materials, focusing on olivine and pyroxene.<n>Using hyperspectral imaging from 400 to 1000 nm, we created data cubes to analyze the reflectance characteristics of samples from Vulcano, a volcanically active island in the Aeolian Archipelago, north of Sicily, Italy.<n>We applied various unsupervised clustering algorithms, including K-Means, Hierarchical Clustering, GMM, and Spectral Clustering, to classify the spectral profiles.
• Score: 0.3764231189632788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study examines the mineral composition of volcanic samples similar to lunar materials, focusing on olivine and pyroxene. Using hyperspectral imaging from 400 to 1000 nm, we created data cubes to analyze the reflectance characteristics of samples from samples from Vulcano, a volcanically active island in the Aeolian Archipelago, north of Sicily, Italy, categorizing them into nine regions of interest and analyzing spectral data for each. We applied various unsupervised clustering algorithms, including K-Means, Hierarchical Clustering, GMM, and Spectral Clustering, to classify the spectral profiles. Principal Component Analysis revealed distinct spectral signatures associated with specific minerals, facilitating precise identification. Clustering performance varied by region, with K-Means achieving the highest silhouette-score of 0.47, whereas GMM performed poorly with a score of only 0.25. Non-negative Matrix Factorization aided in identifying similarities among clusters across different methods and reference spectra for olivine and pyroxene. Hierarchical clustering emerged as the most reliable technique, achieving a 94\% similarity with the olivine spectrum in one sample, whereas GMM exhibited notable variability. Overall, the analysis indicated that both Hierarchical and K-Means methods yielded lower errors in total measurements, with K-Means demonstrating superior performance in estimated dispersion and clustering. Additionally, GMM showed a higher root mean square error compared to the other models. The RMSE analysis confirmed K-Means as the most consistent algorithm across all samples, suggesting a predominance of olivine in the Vulcano region relative to pyroxene. This predominance is likely linked to historical formation conditions similar to volcanic processes on the Moon, where olivine-rich compositions are common in ancient lava flows and impact melt rocks.

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