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Otumba and Sierra de Pachuca obsidian deposits in Central Mexico have been important sources of raw material since pre‐Hispanic times. Numerous archaeological investigations have suggested that the economical and political expansion of major Mesoamerican societies were linked to the control of obsidian sources and distribution of quarried material. Sierra de Pachuca contains several obsidian flows and numerous quarries throughout the region that were preferentially exploited by different cultures. The Otumba Volcanic Complex has four important obsidian domes, but three of them have not been studied in detail. A geochemical characterization of subsources from the Sierra de Pachuca and Otumba Volcanic Complex is an important step toward future sourcing of obsidian artifacts that would help provide insight into spheres of influence and trade by past cultures in Central Mexico. Having this purpose in mind, inductively coupled plasma mass spectrometry (ICP‐MS) was used to analyze obsidian samples collected from five separated locations at Sierra de Pachuca and four at Otumba, followed by statistical analysis (density‐based spatial clustering of applications with noise, DBSCAN). We were able to distinguish three chemically distinctive subsources in Sierra de Pachuca and three in Otumba. This study illustrates the importance of accurate characterization of obsidian raw material when attempting to define subsource usage.

In provenance analysis, identifying the origin of the archaeological artifacts plays a significant role. Usually, this problem is addressed by discovering natural groups in data measured with spectroscopic techniques. Then, principal component and classical partitioning cluster analysis are employed to reveal the groups that supposedly define the origin of the investigated artefacts. However, this work shows that maximizing the variance and searching for specific cluster structures can be misleading because it fails to discriminate clearly the different archeological sources. In contrast, the new methodology reveals several acknowledged geological sources present in the materials through the exploitation of emergence and swarm intelligence without prior assumptions about the data structures. A combination of unsupervised and semi-supervised machine learning and chemometric is applied on samples of Mesoamerican geological sources and obsidian artefacts collected from the archaeological site of Xalasco in Mexico. The analysis of the artifacts showed a preference of Xalasco inhabitants to local obsidian deposits. The results show that this approach, in terms of robustness, is suitable for handling unbiased quantitative spectral analysis of archaeological materials revealing the natural groups of archeological data.

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