Implication of multifractal analysis for quantitative evaluation of mineral resources in the Central Kunlun area, Xinjiang, China

Abstract

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

Prediction of Au-Polymetallic Deposits Based on Spatial Multi-Layer Information Fusion by Random Forest Model in the Central Kunlun Area of Xinjiang, China

“…In addition, two large fold and fault structural belts are located in the study area: the fold-fault zone of the Lower Carboniferous in Feiyunshan and the Triassic fault-fold belt in Yingshishan, Pingling [39]. This complex geological background allowed for the development of element enrichment and mineralization in the study area following the proto-Tethys back-arc basin extinction, the formation and extinction of the Paleo-Tethys Ocean, and the geological evolution associated with the uplift of the Qinghai-Tibet Plateau [40,41].…”

“…The study area spans three metallogenic belts from north to south, namely the Karamiran (compound gully arc belt) Au-Cu-Ag ore belt, Huangyangling (fold belt) Sb-Hg-Au-Cu ore belt, and Yunwuling (fold belt) Cu-Au ore belt [41].…”

“…The main types of mineralization associated with this belt include ophiolite-type chromium, asbestos ore, tough shear-zone crushing, and altering rock-type gold mines, continental sedimentary coal mines, copper-bearing sandstone, and rock salt mines [38]. However, the Cr, Cu, and Ag deposits are distributed outside the study area, and only Au deposits and a large number of rock salt deposits are found within the study area [41].…”

“…This belt has great prospects and is one of the major antimony ore prospect areas in Xinjiang. Although Sb and Hg deposits are present in this mineralization belt, they are distributed outside the study area, and only Au occurrence and placer gold occurrence are located within the study area [38][39][40][41].…”

“…Traditional geostatistical methods have limitations in dealing with complex geological data. Therefore, the application of advanced machine learning algorithms, such as random forest, is imperative for improved mineralization prediction [38][39][40][41][42].…”