地质与勘探2017，Vol.53Issue(3)：456-463,8.
隐伏矿床成矿预测理论与方法新进展
New Progress in the Prediction Theory and Prospecting Method for Concealed Deposits
摘要
Abstract
We discuss the definitions,classifications and features of concealed deposits in China and elsewhere in the world.We should change the understanding of the classification of the concealed deposit from three-dimensional to four-dimensional.Time makes the classification of concealed deposits transform each other,therefore,the classification should be combined with the actual situation today.Then we summarize long-term field work,notice the age of big data and 3S technologies,and consider the traditional prediction theory and survey methods for concealed deposits.Finally,we suggest that the following approaches should be applied to rapid evaluation of medium-and small-scale mines:using big-data mineralization prediction theory to guide ore search,and integrating spatial databases,mobile GIS and geophysical and geochemical equipment into highly efficient technologies for prospecting.These are the current trend in this aspect.The focus of further investigations is discussing combination of prospecting methods in different regions and different deposits type.In the background of 3S technology,the traditional concealed deposit theory and method of mineralization tends to large data metallogenic prediction and mobile GIS exploration technology direction.We should strengthen the metallogenic prediction theory and methods effective interface between each other,transformation and application toward Multivariate metallogenic prediction theory,multivariate methods exploration technology,multi-source data integration information metallogenic prediction development.关键词
隐伏矿床预测/成矿预测/空间数据库/移动GIS/大数据Key words
forecast of concealed ore body/metallogenic prediction/spatial database/mobile GIS/big data分类
天文与地球科学引用本文复制引用
程红军,陈川,展新忠,常金雨,丁亚龙,库瓦尼西别克·买买提朱马,杨蕤嘉,加娜尔,付翰泽..隐伏矿床成矿预测理论与方法新进展[J].地质与勘探,2017,53(3):456-463,8.基金项目
国家“十二五”科技支撑计划专题(编号2015BAB05B01)和新疆维吾尔自治区自然科学基金(编号:2016D01C067)联合资助. （编号2015BAB05B01）
