Open Sourcing” Workflow and Machine Learning Approaches for Attributing Obsidian Artifacts to Their Volcanic Origins

A Feasibility Study from the South Caucasus

Bibliographic Data

ID6432943
AuthorsPavol Hnila (0009-0000-9307-5076), Ellery Frahm (0000-0001-7858-3523), Alessandra Gilibert (0000-0001-8165-9330), Արսեն Բոբոխյան (0000-0002-2277-8281)
Year2025
Volume32
Issue1
Publication date2025-01-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Archaeological Method and Theory (JOURNAL)
Journal identifiersISSN: 1072-5369 • E-ISSN: 1573-7764
PublisherSpringer Science+Business Media (PUBLISHER • DE)
DOI10.1007/s10816-025-09695-8
OpenAlexW4406853930
LanguageEN
Citations received1
References cited23

Traditionally, reliable obsidian sourcing requires expensive calibration standards and extensive geological reference collections as well as experience with statistical processing. In the South Caucasus — one of the most obsidian-rich regions on the planet — this combination of requirements has often restricted sourcing studies because few projects have geological reference collections that cover all known obsidian sources. To test an alternative approach, we conducted “open sourcing” using portable X-ray fluorescence (pXRF) analyses of geological specimens with three key changes to the conventional method: (1) commercially available calibration standards were replaced with a loanable Peabody-Yale Reference Obsidians (PYRO) set, (2) a comprehensive geological reference collection was replaced with a published dataset of consensus values (Frahm, 2023a, 2023b), and (3) processing in statistical packages was replaced with two semiautomated machine-learning workflows available online. For comparison, we used classification by-eye with JMP 17.2 statistical software. Furthermore, we propose a new method to evaluate calibrations, which streamlines comparisons and which we refer to as a symmetric difference ratio (SDR). The results of this feasibility study demonstrate that this “open sourcing” workflow is reliable, yet currently only in combination with classification by-eye. When the consensus values were combined with the machine-learning solutions, the classification results were unsatisfactory. The most encouraging aspect of our alternative “open sourcing” workflow is that it enables correct source identification without physically measuring reference collections, therefore surmounting an obstacle that, until now, has severely limited archaeological research. We anticipate that rapid developments in machine-learning will also soon improve the workflow

Archaeology · Database · Geography · Volcano · Workflow · Computer Science · Geochemistry and Geologic Mapping · Geological and Geochemical Analysis · Pleistocene-Era Hominins and Archaeology · Geology · Paleontology

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