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中国铁矿预测模型与资源潜力分析
引用本文:阴江宁,肖克炎,娄德波. 中国铁矿预测模型与资源潜力分析[J]. 地学前缘, 2018, 25(3): 107-117. DOI: 10.13745/j.esf.2018.03.009
作者姓名:阴江宁  肖克炎  娄德波
作者单位:中国地质科学院 矿产资源研究所 国土资源部成矿作用与资源评价重点实验室, 北京 100037
基金项目:国家重点研发计划项目(2017YFC0601500,2017YFC0601501,2017YFC0601502);中国地质调查局地质调查项目“全国矿产资源潜力动态评价”(DD20160100)、“全国重要矿产总量预测”(1212010633905,1212010733806,1212011121040)
摘    要:文章依据全国矿产资源潜力评价项目相关数据和资料对中国铁矿资源进行概况总结。截至2013年底,中国铁矿区共计4 406个,累计查明资源储量882×108 t,保有资源储量76×108 t,但是其中绝大多数为贫铁矿(全铁品位平均为30%),富铁矿(全铁品位大于50%)仅有10.33×108 t,占1.35%。中国铁矿资源查明资源储量超过1 000×104 t的铁矿共计727个,其中超大型铁矿(>5×108 t)共计32个,大型铁矿((1~5)×108 t)共计121个,中型铁矿(1 000×104 t~108 t)共计574个。根据可利用性划分:可利用的铁矿共计678个,其中超大型27个,大型113个,中型538个;难利用的铁矿共计49个,其中超大型5个,大型8个,中型36个。 截止到2010年,全国预测铁矿资源量1 927×108 t。资源量按预测深度统计:500 m以浅775×108 t,1 000 m以浅128×108 t,2 000 m以浅1 927×108 t。按照目前技术经济评价,在预测铁矿资源量中,可利用的铁矿资源量1 382×108 t,暂不可利用的545×108 t。全国划分了铁矿最小预测区共计6 081个,在此基础上共圈定全国3级预测区392个,铁矿预测资源量10×108 t 以上46个,(1~10)×108 t为118个,1×108 t 以下128个。在典型矿床分析和区域成矿规律研究的基础上,结合物探、化探、遥感等异常找矿标志,归纳概括各类型预测要素并构建其区域预测模型,指导该区域的找矿勘查。根据中国铁矿资源潜力和开发现状,全国铁矿划分了8个开发基地和7个勘查基地,这些基地集中分布在我国东部,包括辽宁的鞍山-本溪、河北冀东、安徽庐枞地区等。中国西部铁矿资源潜力总体不大,但四川攀枝花、云南惠民、新疆西天山和西昆仑等也具有较大的找矿远景。

关 键 词:铁矿  已查明资源量  预测资源量  预测模型  
收稿时间:2017-12-15

Prediction model and resource potential analysis of iron in China.
YIN Jiangning,XIAO Keyan,LOU Debo. Prediction model and resource potential analysis of iron in China.[J]. Earth Science Frontiers, 2018, 25(3): 107-117. DOI: 10.13745/j.esf.2018.03.009
Authors:YIN Jiangning  XIAO Keyan  LOU Debo
Abstract:This paper summarizes the Chinese iron ore resources based on the relevant data of the national mineral resources assessment project. By the end of 2013, China had 4406 iron mine districts and 88.2 billion tons (BT) of verifiable iron resources, including 76.8 BT of iron reserves. However, most of the identified iron resources are poor iron ore (total iron grade below 30% on average), rich iron ore (total iron grade above 50%) merely reached 1.033 BT and account for only 1.35% of the total iron resources. Seven hundred and twenty seven iron deposits held more than 10 million tons (MT) of identified iron resources in China. Among these deposits were 32 super large iron deposits (>500 MT), 121 large iron deposits (100500 MT), and 574 medium sized iron ore (10100 MT). Six hundred and seventy eight iron mines met the standard of industrial usage, of which 27 were super large, 113 large and 538 medium sized mines. Among the 49 hard to utilize iron mines, 5 were super large, 8 large, and 36 medium sized mines. By 2010, the total iron resource in China were estimated around 192.10 BT. According to resources statistics at different depth, 775 MT were above 500 m, 128 BT above 1000 m, and 1927 MT above 2000 m by estimation. According to industrial exploitable standard under current economic and technical conditions, China has 138.2 and 54.5 BT of exploitable and temporarily unexploitable iron ores, respectively. Six thousand and eighty one iron target areas were identified throughout China including 392 level III prediction areas, of which 46 held more than 1 BT, 118 between 100 MT to 1 BT, and 128 under 100 MT in total predicted resources. Based on the analysis of typical iron deposit and regional metallogenic research, combined with the prospecting marks from geophysical, geochemical and remote sensing anomalies, the essential prediction elements were extracted and the regional prediction model was constructed to guide regions iron exploration. According to Chinas iron ore resources potential and exploration status, the national iron mines are segregated into eight development and seven exploration bases. These bases are concentrated in eastern China, including Anben area of Liaoning Province, Jidong area of Hebei Province, Luzong area of Anhui Province, etc. The iron potential resources are relatively low in western China, but Panzhihua depost in Sichuan Province, Huimin deposit in Yunnan Province, and West Tianshan and West Kunlun Mountain areas in Xinjiang have proven to possess great potential.
Keywords:iron ore  identified resources  predicted resources  prediction model  
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