摘要
赛马沟深部隐伏锰矿的发现,是在分析区域地质背景及成矿特征的基础上,建立区域典型矿床成矿模式——青砂沟沉积型锰矿成矿模式,与其进行典型矿床对比分析,认为赛马沟地区具有相似的成矿背景、地球化学异常等特征,以青砂沟锰矿和苦水泉锰矿的含矿建造、地层沉积层序及厚度变化对比分析为依据,在综合运用地质填图、地表槽探揭露的基础上,采用青砂沟锰矿含矿建造、地层沉积层序及厚度数据,估算赛马沟地区隐伏矿体埋藏深度,进而进行钻孔验证,取得了阿尔金成矿带深部隐伏锰矿找矿工作新的突破,为区域上寻找同类型隐伏矿床提供了借鉴。
Based on the analysis of regional geological background and metallogenic characteristics,the discovery of hidden manganese deposits in the deep part of Mashagou is to establish a typical metallogenic model of regional deposits—Qingshagou sedimentary manganese deposit metallogenic model.The comparative analysis of typical deposits shows that there are similar metallogenic geological background,geochemical anomalies and other characteristics in Mashagou area.The ore bearing construction of Qingshagou manganese deposit and Kushuiquan manganese deposit is based on the analysis of regional geological background and metallogenic characteristics,based on the comparison and analysis of the sedimentary sequence and thickness changes of the formation and stratum,and on the basis of the comprehensive use of geological mapping and surface trenching,the buried depth of the concealed ore body in the Mashagou area is estimated by using the data of the ore bearing formation,sedimentary sequence and thickness of the Qingshagou manganese mine,and then the borehole verification is carried out.A new breakthrough in the prospecting for the concealed manganese ore in the deep part of the Altun metallogenic belt is achieved,the regional search for the same type of concealed deposits provides a reference.
作者
俞胜
赵保青
贾轩
董顺利
赵斌斌
白永波
李生喜
杨瑞东
YU Sheng;ZHAO Baoqing;JIA Xuan;DONG Shunli;ZHAO Binbin;BAI Yongbo;LI Shengxi;YANG Ruidong(The second geological and Mineral Exploration Institute qf Gansu geological and mineral exploration and Development Bureau,Lanzhou 730020,Gansu,China;Chengdu university of Technology,Chengdu 610059,Sichuan,China)
出处
《矿产勘查》
2022年第4期473-480,共8页
Mineral Exploration
基金
甘肃省地质勘查基金项目“甘肃省阿克塞县赛马沟锰矿普查”(甘财建[2013]181号)资助。
关键词
沉积型
隐伏矿体
定位预测
深部验证
sedimentary type
concealed ore body
location prediction
deep verification