Research on Surface Weathering Patterns of Glass Artifacts Based on Data Analysis and Machine Learning

Abstract

With the deepening of cultural relics conservation and archaeological research, changes in the chemical and physical properties of ancient glass artifacts, especially the phenomenon of surface weathering, have become an important basis for the study of ancient civilizations and for guiding the authentication and preservation of cultural relics. To help reveal whether the glass surface is weathered or not and accurately classify the glass, this paper firstly preprocesses the sample point data of glass artifacts' surface weathering, establishes a multivariate linear model through the regression analysis method, and finally statistically identifies the weathering intervals of two types of glass. It provides a quantitative basis for the detection of the presence or absence of weathering on the glass surface. Further, using cluster analysis and similarity metrics, the glass samples were subclassified and quantitatively assessed for similarity. The research in this paper not only helps to deepen the understanding of the weathering phenomenon of ancient glass artifacts but also provides strong scientific support for the conservation and restoration of cultural relics).