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Numerical simulation of hydrothermal mineralization associated with simplified chemical reactions in Kaerqueka polymetallic deposit, Qinghai, China

青海卡尔却卡多金属矿床热液与化学反应耦合成矿过程的数值模拟(英文)

作     者:Yan-hong ZOU Yao LIU Yong PAN Kuan-da YANG Ta-gen DAI Xian-cheng MAO Jian-qing LAI Hai-long TIAN 邹艳红;刘尧;潘勇;阳宽达;戴塔根;毛先成;赖建清;田海龙

作者机构:中南大学计算地球科学研究中心长沙410083 中南大学地球科学与信息物理学院有色金属成矿预测与地质环境监测教育部重点实验室长沙410083 湖南大学嵌入式系统及网络实验室长沙410082 湖南文理学院资源环境与旅游学院常德415000 吉林大学地下水资源与环境教育部重点实验室长春130021 

出 版 物:《Transactions of Nonferrous Metals Society of China》 (中国有色金属学报(英文版))

年 卷 期:2019年第29卷第1期

页      面:165-177页

核心收录:

学科分类:081803[工学-地质工程] 08[工学] 0818[工学-地质资源与地质工程] 

基  金:Project(2017YFC0601503)supported by the National Key R&D Program of China Projects(41872249,41472302,41772348)supported by the National Natural Science Foundation of China 

主  题:numerical simulation heat transfer chemical reaction hydrothermal mineralization Kaerqueka polymetallic deposit 

摘      要:The Kaerqueka polymetallic deposit, Qinghai, China, is one of the typical skarn-type polymetallic ore deposits in the Qimantage metallogenic belt. The dynamic mechanism on the formation of the Kaerqueka polymetallic deposit is always an interesting topic of research. We used the finite difference method to model the mineralizing process of the chalcopyrite in this region with considering the field geological features, mineralogy and geochemistry. In particular, the modern mineralization theory was used to quantitatively estimate the related chemical reactions associated with the chalcopyrite formation in the Kaerqueka polymetallic deposit. The numerical results indicate that the hydrothermal fluid flow is a key controlling factor of mineralization in this area and the temperature gradient is the driving force of pore-fluid flow. The metallogenic temperature of chalcopyrite in the Kaerqueka polymetallic deposit is between 250 and 350 ℃. The corresponding computational results have been verified by the field observations. It has been further demonstrated that the simulation results of coupled models in the field of emerging computational geosciences can enhance our understanding of the ore-forming processes in this area.

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