Neural Networks to Map Archaeological Lithic Artefacts

• Autorzy: Jimenez-Puerto Joaquin , Gallello Gianni , Orozco Teresa

Identyfikatory

DOI

10.29227/IM-2024-01-41

Warianty tytułu

PL

Sieci neuronowe do mapowania archeologicznych artefaktów litowych

• Języki publikacji: EN

Abstrakty

EN

An analytical non-destructive strategy to chemically characterize lithic artefacts has been developed. Around 100 archaeological lithic materials found in Neolithic-Chalcolithic sites in the Mediterranean region of the Iberian Peninsula and nowadays stored in different museums of the Valencian Community (Spain), were studied. The materials belong to different typologies of rock (diabase, sillimanite, ophite and amphibolite) and were analysed employing portable energy dispersive X-ray fluorescence spectroscopy (pXRF) directly in the rock surface. The obtained data were processed through neural networks protocol, specifically the so-called Kohonen networks or Self Organised Maps (SOM), to map the geologic samples. This selforganized topological feature maps are suitable to deal with multidimensional representations and map them in a twodimensional space of neurons, following an unsupervised learning protocol. SOM is used to reduce multidimensional data onto lower-dimensional spaces and clustering procedures. As a result, SOM create spatially organized representations, which enhance the discovery of correlations between data. In this case the method has enabled the evaluation of elemental markers related to each rock type behaving as a fine hidden pattern detector and so understand the possible advantages and disadvantages of the analytical method employed to define provenance issues. The attribution suggested by statistics is mainly driven by the composition of rocks essential minerals which are linked to the different petrogenetic conditions. The results showed that in most of the cases the distribution and dispersion of the chemical profile depend of the kind of rock, and clearly suggest that a good way to identify stone tools raw material procurement is to look for elemental markers, being the prior step to create an approximation to ancient exchange networks and their evolution in a diachronic axis.

Słowa kluczowe

EN

neural networks; archaeological lithic artefacts; non-destructive strategy; Kohonen networks; self organised maps (som); Dolerites

PL

sieci neuronowe; archeologiczne artefakty litowe; strategia nieniszcząca; sieci Kohonena; mapy samoorganizujące (SOM); Doleryci

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2024
• Tom: Vol. 1, no. 1
• Strony: 361--367
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Jimenez-Puerto Joaquin

• Department of Prehistory, Archeology and Ancient History (PREMEDOC Research Group). University of Valencia. Av / Blasco Ibáñez 28, 46010, Valencia, Spain

• https://orcid.org/0000-0001-9760-9602

autor

Gallello Gianni

• Department of Prehistory, Archeology and Ancient History (PREMEDOC Research Group). University of Valencia. Av / Blasco Ibáñez 28, 46010, Valencia, Spain

autor

Orozco Teresa

• Department of Prehistory, Archeology and Ancient History (PREMEDOC Research Group). University of Valencia. Av / Blasco Ibáñez 28, 46010, Valencia, Spain

Bibliografia

• 1. Ramacciotti M, Gallello G, Jiménez-Puerto J, Bernabeu J, Orozco Köhler T, Rubio-Barberá S, et al. "Non-destructive characterisation of dolerite archaeological artefacts". Microchemical Journal. December 2022;183:108080.
• 2. Bernabeu J, Orozco T. 90: "Fuentes dc materias primas y circu· lación de materiales durante el final dei Neolítico en el Pais Valen· ciano. Resultados de! análisis peuológico dei utiJlaje pulimentado". Cuadernos de Prehistoria de la Universidad dc Granada. 1989;14-5.
• 3. Frahm E, Doonan RCP. "The technological versus methodological revolution of portable XRF in archaeology". Journal of Archaeological Science. February 2013;40(2):1425-34.
• 4. Jones MC, Williams-Thorpe O, Potts PJ, Webb PC. "Using Field-Portable XRF to Assess Geochemical Variations Within and Between Dolerite Outcrops of Preseli, South Wales". Geostand Geoanalyt Res. November 2005;29(3):251-69.
• 5. Honório KM, da Silva ABF. "Applications of artificial neural networks in chemical problems". Artificial neural networks-architectures and applications. 2013;203-23.
• 6. Zupan J, Gasteiger J. Neural networks in chemistry and drug design. John Wiley & Sons, Inc.; 1999.
• 7. Rojas R. Neural networks: a systematic introduction. Springer Science & Business Media; 2013.
• 8. Miljković D. "Brief review of self-organizing maps". In: 2017 40th international convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE; 2017. p. 1061-6.
• 9. Orozco-Köhler T. Aprovisionamiento e Intercambio: Análisis petrológico del utillaje pulimentado en la Prehistoria Reciente del País Valenciano (España). Vol. 867. British Archaeological Reports Limited; 2000.
• 10. Jimenez-Puerto J, Gallello G. "pXRF Lithic tools database [Internet]". Zenodo; 2023 [citado 14 de abril de 2023]. Available at: https://zenodo.org/record/7829946
• 11. Fuchs C. "Self-Organizing System". In: Encyclopedia of Governance, ed by Mark Bevir. SAGE; 2007.
• 12. Ashby WR. "Principles of the self-organizing system." In: Systems Research for Behavioral Sciencesystems Research. Routledge; 2017. p. 108-18.
• 13. Kohonen T, Huang TS, Schroeder MR. Self-Organizing Maps. 3rd ed. Berlin, Heidelberg: Springer Berlin / Heidelberg; 2012.
• 14. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2017. Available at: https://www.R-project.org/
• 15. Sievert C. "Interactive web-based data visualization with R, plotly, and shiny.". CRC Press; 2020. 16. Wehrens R, Kruisselbrink J. "Flexible self-organizing maps in kohonen 3.0." Journal of Statistical Software. 2018;87:1-18.
• 17. Potts PJ, Bernardini F, Jones MC, Williams-Thorpe O, Webb PC. "Effects of weathering onin situ portable X-ray fluorescence analyses of geological outcrops: dolerite and rhyolite outcrops from the Preseli Mountains, South Wales". X-Ray Spectrom. January 2006;35(1):8-18.
• 18. Ogburn D, Sillar B, Sierra JC. "Evaluating effects of chemical weathering and surface contamination on the in situ provenance analysis of building stones in the Cuzco region of Peru with portable XRF". Journal of Archaeological Science. April 2013;40(4):1823-37.
• 19. Williams-Thorpe O, Potts PJ, Webb PC. Field-Portable "Non-Destructive Analysis of Lithic Archaeological Samples by X-Ray Fluorescence Instrumentation using a Mercury Iodide Detector: Comparison with Wavelength-Dispersive XRF and a Case Study in British Stone Axe Provenancing". Journal of Archaeological Science. February 1999;26(2):215-37.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).