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地质单元法区域成矿预测
引用本文:陈建平,严琼,李伟,尚北川,丁成武.地质单元法区域成矿预测[J].吉林大学学报(地球科学版),2013,43(4):1083-1091.
作者姓名:陈建平  严琼  李伟  尚北川  丁成武
作者单位:中国地质大学(北京)国土资源与高新技术研究中心/北京市国土资源信息研究开发重点实验室,北京100083
基金项目:中国地质调查局地质大调查项目
摘    要:网格单元法和地质体单元法是目前运用在矿产预测中最普遍的地质统计方法,2种方法各有优缺点。为了更好地使2种方法扬长避短,以青海省祁漫塔格地区接触交代型铁铜矿床为例,在其找矿预测模型的背景下,运用网格法和地质体法分别进行资源预测;并在运用地质体单元法时,在传统的圈定地质体界限的同时,也对地质体本身进行了网格划分,从而进一步进行证据权的计算,得到后验概率值。2种方法得到的结果较一致,证明新地质体法的可靠性。将预测结果结合地质体分级情况、地质体法和网格法阈值大小综合考虑,圈定出预测远景区,从而划定靶区。预测出的靶区既是找矿后验概率的高值区域,又是成矿最有利的地质体区域。

关 键 词:网格单元法  地质体单元法  祁漫塔格  找矿预测模型  地质体分级  
收稿时间:2012-11-25

Methods of Geological Statistics in Prediction of Mineral Resources
Chen Jianping,Yan Qiong,Li Wei,Shang Beichuan,Ding Chengwu.Methods of Geological Statistics in Prediction of Mineral Resources[J].Journal of Jilin Unviersity:Earth Science Edition,2013,43(4):1083-1091.
Authors:Chen Jianping  Yan Qiong  Li Wei  Shang Beichuan  Ding Chengwu
Institution:Institute of High and New Techniques Applied to Land Resources,China University of Geosciences (Beijing) /Land Resources Information Development and Research Key Laboratory of Beijing,Beijing100083,China
Abstract:The block method and the geologic body unit method are two most common statistic methods in mineral prediction, but both have advantages and disadvantages. Taking contact metasomatic iron copper deposits in Qimantage as examples, authors introduce the mineral resource prediction of these deposits by the two methods mentioned above on basis of the prospecting prediction model. The geologic body unit method is used to delineate the boundaries of geological bodies; meanwhile, the geologic block method is used to divide the geologic body into geologic grid. The results of the two methods are consistent with each other, which indicates that the new body method is credible. Finally, the authors prospect the target areas based on the forecast area. The target areas are not only the high value areas in posterior probability but also the most favorable mineralized areas.
Keywords:block method  geologic body unit method  Qimantage  prospecting forecasting model  classification of geologic body
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