Geological and Mineralogical Analysis of Zhuantobe Skarns in Central Kazakhstan Considering the Influence of Textural Features on Iron Ore Quality
V. Korobkin,Assel Nygmanova,Z. Tulemissova,A. Chaklikov
Abstract
The study of the material composition, textural, and structural features of skarns and magnetite ores is of great importance for increasing the efficiency of iron ore mining and its subsequent processing and enrichment. In the northwestern Balkhash region of Central Kazakhstan, there is a reserve iron ore region represented by a series of skarn contact-metasomatic deposits: Bapy, Zhuantobe, Karaulken, Akchagyl, Ushtobe, Kiyik, Taitobe, Tomashev, Kyzyl-Sayak, and others. The results of field investigations and laboratory analyses have enabled the characterization of the mineralogical and petrographic composition of the skarns, as well as their material composition and textural–structural features. All these specified characteristics of skarns reflect the stage-by-stage nature of the contact-metasomatic processes of iron ore formation. The skarn formation model at the Zhuantobe deposit developed over several stages: (1) the formation of skarns during granitoid intrusion and the establishment of conditions for contact metamorphism (resulting in iron-poor, barren diopside hornfels and marbles); (2) early skarn stage, during which anhydrous, dark-colored endo- and exoskarns composed of pyroxenes, magnetite, and hematite develop; (3) late fluid–hydrothermal stage, during which hornblende, epidote, calcite, and sulfides (pyrite, chalcopyrite, sphalerite, and galena) form; (4) oxidative supergene stage under near-surface conditions, during which limonite and iron hydroxides form. The conducted comprehensive analysis of the material composition and textural–structural features of iron ores of the Zhuantobe deposit made it possible to establish the influence of these parameters on the technological properties of ores. The performed studies make it possible to more accurately identify promising iron ore zones in skarns and predict the technological behavior of ore during processing.
