# Insights into Lunar Mineralogy: An Unsupervised Approach for Clustering of the Moon Mineral Mapper (M3) spectral data

arXiv: https://arxiv.org/abs/2411.03186 · HTML: https://mlsanity.com/paper/2411.03186/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 5 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Analyzing noise in autoencoders and deep networks](https://mlsanity.com/paper/1406.1831/) (2014)
- [DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning](https://mlsanity.com/paper/2102.07472/) (2021)

## Built on by (most cited first, 2 of 2)

- [Using Machine Learning for Lunar Mineralogy-I: Hyperspectral Imaging of Volcanic Samples](https://mlsanity.com/paper/2503.22617/) (2025)
- [Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning](https://mlsanity.com/paper/2603.24323/) (2026)

## Abstract (arXiv)

This paper presents a novel method for mapping spectral features of the Moon using machine learning-based clustering of hyperspectral data from the Moon Mineral Mapper (M3) imaging spectrometer. The method uses a convolutional variational autoencoder to reduce the dimensionality of the spectral data and extract features of the spectra. Then, a k-means algorithm is applied to cluster the latent variables into five distinct groups, corresponding to dominant spectral features, which are related to the mineral composition of the Moon's surface. The resulting global spectral cluster map shows the distribution of the five clusters on the Moon, which consist of a mixture of, among others, plagioclase, pyroxene, olivine, and Fe-bearing minerals across the Moon's surface. The clusters are compared to the mineral maps from the Kaguya mission, which showed that the locations of the clusters overlap with the locations of high wt% of minerals such as plagioclase, clinopyroxene, and olivine. The paper demonstrates the usefulness of unbiased unsupervised learning for lunar mineral exploration and provides a comprehensive analysis of lunar mineralogy.