Cluster Analysis of Iron Meteorites: Applications for Unsupervised Machine Learning

Iron meteorites are remnants of planetesimal cores, revealing planetary formation processes and the history of the early solar system (Scott, 2020). Currently, the Meteoritical Bulletin Database contains 1445 iron meteorites. Genetic relationships between iron meteorites reveal samples related by formation on the same parent body or in similar bodies in the same region of the solar system (Krot et al., 2014). This can also help to determine a minimum value for the number of differentiated planetesimals that were present in the early solar system. Additionally, knowing which meteorites come from the same parent body allows the study of processes such as fractional crystallisation in the core of the parent body. Classification of iron meteorites is based on major, minor and trace element patterns. Traditionally, iron meteorites have been manually classified using two-dimensional element plots, usually element-Ni or element-Au diagrams. We present here the first study applying machine learning to the classification of iron meteorites (Lobanov and Downes, 2026), which allows for the automated classification in multi-element space using cluster analysis.

The first step involved compiling published iron meteorite compositional data from 67 publications from 1967 to 2023. This resulted in a database with 2,396 individual chemical analyses, covering 880 separate iron meteorites. Each analysis reported a different set of elements, and Figure 1 shows the totals of how many data points are available for each element. 

Figure 1: Bar graphs that show a) the number of analyses in the database per element. b) the number of individual meteorites for every element.

We used unsupervised machine learning, which does not require a training dataset. This is important in a situation with limited and variable available data, and also means the model is not based on any assumptions of the size and limits of currently known iron meteorite groups. This also reduces biases from the existing classification and allows independent verification of the current classification of iron meteorites. Cluster analysis is a form of unsupervised machine learning that can be used to partition data into distinct groups, where the points in a group are as similar as possible, but with groups as distant from each other as possible (Xu and Tain, 2015). Many models can be used for cluster analysis, and after theoretical considerations and numerous trials, we found that hierarchical density-based cluster analysis provided the best results (Lobanov and Downes, 2026). Density-based cluster analysis was applied successfully to the iron meteorite dataset, as the model adheres to density trends in multidimensional space for finding clusters and can therefore adapt to the irregular shapes of the clusters, and different groups being of different sizes and densities. Unlike other clustering algorithms, it does not assume that clusters are of similar sizes or spherical, and no knowledge of the number of clusters is required (Xu and Tain, 2015).

In tests on grouped data, the 7-element model (Ni, Ga, Ge, Ir, Au, As, Co) achieved an Adjusted Rand Index (ARI) of 0.98 (Figure 2), with other element models reaching up to an ARI of 0.992, demonstrating almost perfect reproduction of the existing classification for iron meteorites (Lobanov and Downes, 2026). Seven different element combinations were used in this study, based on data availability, using up to 10 elements simultaneously. Next, the models treated a suite of ungrouped iron meteorites in the same way as the already-classified meteorites. Twenty-nine of these ungrouped iron meteorites were classed by the models as being related to existing groups, based on the combination of 350 runs from the 7 different element combinations. These results propose that twenty-nine ungrouped iron meteorites are related to existing groups based on the available geochemical data and these new classifications will be proposed to the Nomenclature Committee of the Meteoritical Society.

Figure 2: 3-dimensional scatter plot with the results of clustering a smaller grouped dataset with the 7-element model, replicating the existing classification with an ARI of 0.98. Ni is in mg/g and Ga and Ge in μg/g.

This study presents a completely novel way to classify iron meteorites using unsupervised machine learning. Cluster analysis has an immense potential as a new methodology for the unbiased, fast, transparent, reproducible and adaptable classification of iron meteorites. It can be used to verify the existing classification, to classify new iron meteorites, to classify ungrouped meteorites, and to find new groups. As the model is very versatile and adaptable, we hope that it will be implemented in future classifications of iron meteorites and are open to collaborations for the classification.

Machine learning methodologies have an enormous potential to be used by the planetary science community and are currently underused. We hope that in the future our methodology can be used for applications on other meteorite groups. The detailed machine learning methodology, source code, and compiled dataset are available in Lobanov and Downes (2026) and the supplementary materials.

Acknowledgements: We thank the Paneth Trust, administered by the Royal Astronomical Society, for the grant given to Louis-Alexandre Lobanov, enabling him to complete this research.

References:

Krot, A.N., Keil, K., Scott, E.R.D., Goodrich, C.A., Weisberg, M.K., 2014. Classification of Meteorites and Their Genetic Relationships, in: Holland, H.D., Turekian, K.K. (Eds.), Treatise on Geochemistry (Second Edition). Elsevier, Oxford, pp. 1–63. https://doi.org/10.1016/B978-0-08-095975-7.00102-9

Lobanov, L.-A., Downes, H., 2026. An Unsupervised Machine Learning Approach to Iron Meteorite Classification (in press). Meteoritics and Planetary Science.

Scott, E.R.D., 2020. Iron Meteorites: Composition, Age, and Origin, in: Oxford Research Encyclopedia of Planetary Science. https://doi.org/10.1093/acrefore/9780190647926.013.206

Xu, D., Tian, Y., 2015. A Comprehensive Survey of Clustering Algorithms. Ann. Data. Sci. 2, 165–193. https://doi.org/10.1007/s40745-015-0040-1