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Modeling-Based Multiscale Deep Prospectivity Mapping: A Case Study of the Haoyaoerhudong Gold Deposit,Inner Mongolia,China
Authors:Li  Nan  Cao  Rui  Ye  HuiShou  Li  Qiang  Wang  Yitian  Lv  Xiping  Guo  Na  Su  Yuanxiang  Hao  Jianrui  Yin  Shitao  Chu  Wenkai
Institution:1.MLR Laboratory of Metallogeny and Mineral Resource Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037, China
;2.Inner Mongolia Pacific Mining Co., Ltd., Wulatezhongqi, 015308, China
;3.School of Management Science, Chengdu University of Technology, Chengdu, 610059, China
;4.Graduate School of China University of Geo-Sciences (Beijing), Beijing, 100083, China
;
Abstract:

The mineral system modeling approach for prospectivity mapping is an efficient and economic method to assess undiscovered mineral potential quantitatively. It is a procedure of modeling, acquiring, and coupling the proxies of footprints of mineral systems at multiple scales (e.g., regional, district, and deposit scales). In this approach, the critical issue from multiple scales is that the data collected are asymmetrical from the superficial to the deep or from mine to its brown fields, so that it is hard to employ and integrate them. To complete this study, firstly, multi-tactic 3D geological modeling methods, including the explicit, the implicit, and inversion, were used to build geological models in the condition of asymmetrical datasets at the deposit and district scales. Secondly, indicators acquired in drill-intensive fields among multisource datasets composed of geology, geochemistry, geophysics and alteration data were transferred to studies in deep and brown fields. Finally, deep (~?1,100 m) and circumjacent potentials of mine were targeted in the Haoyaoerhudong gold deposit situated in the Urad Middle Banner area, Inner Mongolia, which is one of the largest black-rock-series-type gold mines in China. This proposed procedure is more visual, clear, intuitive, and transferable to drive mineral system approach to exploration discovery than previous GIS-based studies.

Keywords:
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