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

2025-03-28

AI Summary

This study aims to invert the mineralogical composition of lunar-analog volcanic rocks using hyperspectral imaging (400–1000 nm) to evaluate their fidelity as simulants for lunar basaltic materials. Method: Leveraging basaltic samples from Vulcano Island, Italy, a spectral data cube was constructed and subjected to a novel unsupervised mineral unmixing framework integrating multiple clustering algorithms (K-means, hierarchical clustering, Gaussian mixture modeling, spectral clustering) with non-negative matrix factorization (NMF). Contribution/Results: K-means achieved the best overall performance (mean silhouette coefficient = 0.47; lowest RMSE), while hierarchical clustering yielded a spectral match of 94% with reference olivine spectra. Results confirm significant olivine enrichment in the analog samples, with spatial distribution patterns causally linked to ancient lunar lava flows. This integrated spectral unmixing approach establishes a transferable, high-precision paradigm for extraterrestrial mineral analog studies.

Technology Category

• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Machine Learning: Clustering
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Analyze lunar-like volcanic samples using hyperspectral imaging
• Compare clustering algorithms for mineral classification accuracy
• Identify olivine predominance in samples resembling lunar geology

Innovation

• Hyperspectral imaging for mineral analysis
• Unsupervised clustering algorithms for classification
• Principal Component Analysis for mineral identification