RT Journal Article
T1 A deep learning-based surrogate for the XRF approximation of elemental composition within archaeological artefacts before restoration
A1 Stoean, Ruxandra
A1 Ionescu, Leonard
A1 Stoean, Catalin
A1 Boicea, Marinela
A1 Atencia-Ruiz, Miguel Alejandro
A1 Joya-Caparrós, Gonzalo
K1 Espectroscopía de rayos X
K1 Restauración y conservación - Innovaciones tecnológicas
AB The gold standard in approximating the concentration of the elements in its composition (in percentages, between 0 and 100) is performed through an X-ray fluorescence (XRF) machine. While this is a non-invasive approach, it comes at substantial financial and training costs, and possible radiation exposure of the investigator. In this context, the present paper explores the potential of a deep learning regression model to give an estimate on the concentration of a given element from stereo microscopy slides of historical artefacts, as an alternative means to the XRF. Two problems with different degrees of complexity are examined in turn. The first one is represented by the consideration of iron objects, where the metal is strongly dominant in the chemical structure. The second comes both as a complement to the other, in order to expose the model also to non-iron items, and as a more difficult task of identifying the degree of copper that is present only as part of an alloy constitution. While for iron the one absolute value prediction of the model is always very close to the XRF approximation, copper has a wider distribution of its concentration among objects, which is more challenging to learn; hence, performance for a singular absolute estimation can rise only with the increase in the amount of data. A window of error acceptability was also implemented and it allows for an approximation that is sufficient for grasping the degree of the metal in the composition that is necessary for the restoration procedures. The findings therefore provide a first step in putting forward a computational support tool that represents a less expensive and less dangerous alternative for approximating the elemental analysis before artefact reinstatement.
PB Elsevier
YR 2021
FD 2021-10
LK https://hdl.handle.net/10630/41427
UL https://hdl.handle.net/10630/41427
LA eng
NO Ruxandra Stoean, Leonard Ionescu, Catalin Stoean, Marinela Boicea, Miguel Atencia, Gonzalo Joya. A Deep Learning-based Surrogate for the XRF Approximation of Elemental Composition within Archaeological Artefacts before Restoration, Procedia Computer Science, Volume 192, 2021, Pages 2002-2011
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 14 abr 2026