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.

‘Open sourcing’ and machine learning workflows for attributing obsidian artifacts to their volcanic origins: a feasibility study from the South Caucasus

Alessandra Gilibert
Writing – Review & Editing
;
Arsen Bobokhyan
Membro del Collaboration Group
2025-01-01

Abstract

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.
2025
32
File in questo prodotto:
File Dimensione Formato  
s10816-025-09695-8.pdf

accesso aperto

Tipologia: Versione dell'editore
Licenza: Accesso gratuito (solo visione)
Dimensione 3.7 MB
Formato Adobe PDF
3.7 MB Adobe PDF Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5089667
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact