Using Machine Learning for Lunar Mineralogy-I: Hyperspectral Imaging of Volcanic Samples
Journal:
arXiv
Published Date:
Mar 28, 2025
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.
