Extensive research has been conducted to evaluate mineral resource potential based on geochemical data, but this work is still challenging due to the existence of multiple evaluation solutions based on different methods. In this paper, we combine the multifractal analysis method with typical multivariate statistical methods to analyse the spatial characteristics of geochemical stream sediment data, aiming to quantitatively study the ore-forming potential of the elements in the central Kunlun area of Xinjiang, China. An R-type cluster analysis, Pearson correlation analysis, and principal component analysis are used to explore the correlations among the 12 target elements. The multifractal model is constructed by using the method of moments to analyse the spatial distribution patterns of the elements, and corresponding multifractal parameters are extracted to quantitatively describe their ore-forming strengths in the study area. The results show that Co, V, Ti, Fe2O3, MgO, and Cu compose a group of elements closely related to the regional geological background, while Pb, Zn, Bi, Sn, Au, and Ba are potential metallogenic elements with relatively high ore-forming strengths and favourable ore-forming potential. Multifractal theory further validates and evaluates the favourable ore-forming element group obtained through conventional geochemical multivariate statistical methods, thus providing a new idea for small-scale geochemical prospecting.
Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis