Non-destructive characterisation of dolerite archaeological artefacts

Ramacciotti, Mirco; Gallello, Gianni; Jiménez-Puerto, Joaquín; Bernabeu, Joan; Köhler, Teresa Orozco; Rubio-Barberá, Sonia; Pastor, Agustı́n

doi:10.1016/j.microc.2022.108080

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Polished stone tools are common archaeological artifacts allover the Mediterranean basin of the Iberian Peninsula. Previous works (1,2) highlighted the potential enclosed in the characterization and identification of raw material outcrops to enable the study of plausible exchange networks during the late Neolithic and Copper Age. This work, carried out in the frame of the project NEONETS 1 , aims to develop a methodological approach for the classification of unknown lithic material, getting a deeper dive in the available analytical methods to perform this task in a non-invasive way.…”

Section: Introductionmentioning

confidence: 99%

“…Previous works (1,2) highlighted the potential enclosed in the characterization and identification of raw material outcrops to enable the study of plausible exchange networks during the late Neolithic and Copper Age. This work, carried out in the frame of the project NEONETS 1 , aims to develop a methodological approach for the classification of unknown lithic material, getting a deeper dive in the available analytical methods to perform this task in a non-invasive way. More specifically the use of a portable X-ray fluorescence (pXRF), which provides a reliable tool to determine the concentrations of a set of elements present in lithic artefacts through a semi-quantitative method.…”

Section: Introductionmentioning

confidence: 99%

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Sieci neuronowe do mapowania archeologicznych artefaktów litowych

Jiménez-Puerto,

Gallello,

Orozco

2024

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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 self-organized topological feature maps are suitable to deal with multidimensional representations and map them in a two-dimensional 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sieci neuronowe do mapowania archeologicznych artefaktów litowych

Jiménez-Puerto,

Gallello,

Orozco

2024

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“…This focus has led to a material bias which has been highlighted by Prieto et al (2019: 15). Recent decades, however, have seen an increase in interest for non-flint lithics and raw material studies (Blomme et al, 2012;Columbu et al, 2023;Dalpra and Pitblado, 2016;Prieto et al, 2019Prieto et al, , 2020Prieto et al, , 2021Ramacciotti et al, 2019Ramacciotti et al, , 2022Ramacciotti et al, , 2023Sherman et al, 2023).…”

Section: Introductionmentioning

confidence: 99%