Supervised Machine Learning Algorithms to Discriminate Two Similar Marble Varieties, a Case Study
Casas Duocastella, Lluís (Universitat Autònoma de Barcelona. Departament de Geologia)
Anglisano, Anna (Universitat Autònoma de Barcelona. Departament de Geologia)
Di Febo, Roberta (Universitat Autònoma de Barcelona. Departament de Geologia)
Pedreño Vírseda, Berta (Universitat Autònoma de Barcelona. Departament de Geologia)
Queralt, Ignasi (Institut de Diagnosi Ambiental i Estudis de l'Aigua)

Data:	2023
Resum:	A multi-analytical approach is usually applied in provenance studies of archaeological marbles. However, for very similar marble varieties, additional techniques and approaches are required. This paper uses a case study to illustrate this with two Catalan marble districts (Gualba and Ceret) and three sets of archaeological marbles. The common multi-method approach is unable to discriminate between the two districts, and such distinction is only partially glimpsed using unsupervised multivariate data analyses on a transformed geochemical dataset of reference samples. In contrast, several supervised classification models have been successfully trained to discriminate between the quarries without any special data transformation. All the trained models agree to assign the three sets of archaeological samples to the Gualba quarry district. Additional outcomes of the paper are a comprehensive archaeometric characterization of the little-known marbles of Gualba and Ceret and the first archaeometrically supported evidence of the use of Gualba marble during Roman and Medieval times.
Ajuts:	Ministerio de Ciencia e Innovación CEX2018-000794-S Agencia Estatal de Investigación PID2021-122879OB-I00
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	Minerals, Vol. 13, Issue 7 (July 2023) , art. 861, ISSN 2075-163X

DOI: 10.3390/min13070861

24 p, 23.0 MB

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