An analysis of mining intensity about metal mines based on investigation of tailing reservoirs in Tibet

doi:10.6046/gtzyyg.2019.02.30

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Abstract

Based on remote sensing technology, the authors investigated the distribution of tailings reservoir in Tibet, such as its mineral resources, utilization status and scale. The current mining intensity of different administrative regions, different metallogenic belts and different mine types in Tibet was analyzed. Some conclusions have been reached: for different prefectural-level divisions, the metal mines’ mining intensity in Lhasa City is the largest, the metal mines exploitation potential in Lhasa City and Naqu area are larger. For different county-level administrative regions, the metal mines mining intensity in Mozhugongka County of Lhasa City is the largest, the metal mines exploitation potential in Mozhugongka County of Lhasa City and Shenzha County of Naqu area are larger. For different important metallogenic belts, the metal mines mining intensity in Gangdise metallogenic belt is the largest, the metal mines exploitation potential in Gangdise metallogenic belt is also the largest. For different mine types, the metal mines mining intensity of nonferrous minerals is the largest, the metal mines exploitation potential of nonferrous minerals is also the largest. For different specific mine types, the metal mines mining intensity of lead-zinc mines and copper mines are the largest, and the metal mines exploitation potentialof lead-zinc mines is the largest.

Keywords Tibet metal mines mining intensity tailings reservoir remote sensing

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Issue Date: 23 May 2019

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	Haiqing WANG
	Li LI
	Ling CHEN
	Wenjia XU
	Jinzhong YANG
	Qiong LIU

Cite this article:

Haiqing WANG,Li LI,Ling CHEN, et al. An analysis of mining intensity about metal mines based on investigation of tailing reservoirs in Tibet[J]. Remote Sensing for Land & Resources, 2019, 31(2): 218-223.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.30 OR https://www.gtzyyg.com/EN/Y2019/V31/I2/218

Fig.1 Remote sensing images of some tailing reservoirs in study area

Tab.3 List of remote sensing investigation for tailing reservoirs in study area

Tab.4 List of tailing reservoirs in each administrative region

Tab.5 List of tailing reservoirs for different mine types

[1]	中华人民共和国国土资源部. 2017中国矿产资源报告[M]. 北京: 地质出版社, 2017: 14-20.
[1]	Ministry of Land and Resources People’s Republic of China. 2017 China Mineral Resources[M]. Beijing: Geological Publishing House, 2017: 14-20.
[2]	中华人民共和国国土资源部. 2016中国矿产资源报告[M]. 北京: 地质出版社, 2016: 15-22.
[2]	Ministry of Land and Resources People’s Republic of China. 2016 China Mineral Resources[M]. Beijing: Geological Publishing House, 2016: 15-22.
[3]	中华人民共和国国土资源部. 2015中国矿产资源报告[M]. 北京: 地质出版社, 2015: 13-18.
[3]	Ministry of Land and Resources People’s Republic of China. 2015 China Mineral Resources[M]. Beijing: Geological Publishing House, 2015: 13-18.
[4]	中华人民共和国国土资源部. 2014中国矿产资源报告[M]. 北京: 地质出版社, 2014: 13-17.
[4]	Ministry of Land and Resources People’s Republic of China. 2014 China Mineral Resources[M]. Beijing: Geological Publishing House, 2014: 13-17.
[5]	中华人民共和国国土资源部. 2013中国矿产资源报告[M]. 北京: 地质出版社, 2013: 11-14,32.
[5]	Ministry of Land and Resources People’s Republic of China. 2013 China Mineral Resources[M]. Beijing: Geological Publishing House, 2013: 11-14,32.
[6]	中华人民共和国国土资源部. 2012中国矿产资源报告[M]. 北京: 地质出版社, 2012: 20-29.
[6]	Ministry of Land and Resources People’s Republic of China. 2012 China Mineral Resources[M]. Beijing: Geological Publishing House, 2012: 20-29.
[7]	路云阁, 李春霖, 刘采 , 等. 西藏东部地区矿产资源开发状况及其评价[J]. 生态与农村环境学报, 2016,32(3):379-382.
[7]	Lu Y G, Li C L, Liu C , et al. Status of exploitation of mineral resources in east Tibet and its evaluation[J]. Journal of Ecology and Rural Environment, 2016,32(3):379-382.
[8]	何芳, 徐友宁, 乔冈 , 等. 中国矿山环境地质问题区域分布特征[J]. 中国地质, 2010,37(5):1520-1529.
[8]	He F, Xu Y N, Qiao G , et al. Regional distribution characteristics of mine environmental geological problems in China[J]. Geology in China, 2010,37(5):1520-1529.
[9]	周英杰, 王晓红, 姚维岭 , 等. 山东省尾矿库遥感调查与环境影响分析[J]. 中国地质调查, 2017,4(4):88-92.
[9]	Zhou Y J, Wang X H, Yao W L , et al. Remote sensing investigation and environmental impact analysis of tailing ponds in Shandong Province[J]. Geology Survey of China, 2017,4(4):88-92.
[10]	高永志, 初禹, 梁伟 . 黑龙江省矿集区尾矿库遥感监测与分析[J]. 国土资源遥感, 2015,27(1):160-163.doi: 10.6046/gtzyyg.2015.01.25. doi: 10.6046/gtzyyg.2015.01.25
[10]	Gao Y Z, Chu Y, Liang W . Remote sensing monitoring and analysis of tailings ponds in the ore concentration area of Heilongjiang Province[J]. Remote Sensing for Land and Resources, 2015,27(1):160-163.doi: 10.6046/gtzyyg.2015.01.25.
[11]	强建华 . 陕西省尾矿库遥感调查与环境影响分析[J]. 金属矿山, 2013,42(10):112-115.
[11]	Qiang J H . Remote sensing investigation and environmental impact analysis of tailing dams in Shaanxi Province[J]. Metal Mine, 2013,42(10):112-115.
[12]	方雪娟, 丁镭, 张志 . 大冶陈贵镇小型尾矿库分布特征及环境影响分析[J]. 国土资源遥感, 2013,25(1):155-159.doi: 10.6046/gtzyyg.2013.01.27. doi: 10.6046/gtzyyg.2013.01.27
[12]	Fang X J, Ding L, Zhang Z . An analysis of distribution characteristics and environmental effect of small tailing ponds in Chengui town,Daye[J]. Remote Sensing for Land and Resources, 2013,25(1):155-159.doi: 10.6046/gtzyyg.2013.01.27.
[13]	郝利娜, 张志, 何文熹 , 等. 鄂东南尾矿库高分辨率遥感图像识别因子研究[J]. 国土资源遥感, 2012,24(3):154-158.doi: 10.6046/gtzyyg.2012.03.27. doi: 10.6046/gtzyyg.2012.03.27
[13]	Hao L N, Zhang Z, He W X , et al. Tailings reservoir recognition factors of the high resolution remote sensing image in southeastern Hubei[J]. Remote Sensing for Land and Resources, 2012,24(3):154-158.doi: 10.6046/gtzyyg.2012.03.27.
[14]	肖如林, 吕杰, 付卓 , 等. 张家口市尾矿库环境风险遥感监测应用研究[J]. 遥感技术与应用, 2014,29(1):100-105. doi: 10.11873/j.issn.1004-0323.2014.1.0100
[14]	Xiao R L, Lyu J, Fu Z , et al. The application of remote sensing in the environmental risk monitoring of tailings pond in Zhangjiakou City,China[J]. Remote Sensing Technology and Application, 2014,29(1):100-105.
[15]	张静, 郑春丽, 王建英 , 等. 北方稀土尾矿库周边重金属污染调查[J]. 环境科学与技术, 2016,39(4):144-148.
[15]	Zhang J, Zheng C L, Wang J Y , et al. Survey of heavy metal pollution nearby a rare earth mine tailing reservoir in North China[J]. Environmental Science and Technology, 2016,39(4):144-148.
[16]	马国超, 王立娟, 马松 , 等. 矿山尾矿库多技术融合安全监测运用研究[J]. 中国安全科学学报, 2016,26(7):35-40.
[16]	Ma G C, Wang L J, Ma S , et al. Research on mine tailing pond safety monitoring based on multiple technology fusion[J]. China Safety Science Journal, 2016,26(7):35-40.
[17]	王洛锋 . 尾矿库资源调查方法探讨与实践[J]. 中国钨业, 2017,32(1):55-58,74.
[17]	Wang L F . Investigation methods and practice for tungsten resources in tailings reservoir[J]. China Tungsten Industry, 2017,32(1):55-58,74.
[18]	李玉凤, 包景岭, 张锦瑞 . 铁尾矿资源开发利用现状分析[J]. 中国矿业, 2015,24(11):77-81,121.
[18]	Li Y F, Bao J L, Zhang J R . Status analysis of iron tailings comprehensive utilization[J]. China Mining Magazine, 2015,24(11):77-81,121.
[19]	苏建, 欧孝夺, 樊克世 , 等. 广西无主尾矿库闭库程序研究及应用[J]. 中国矿业, 2015,24(4):63-68.
[19]	Su J, Ou X D, Fan K S , et al. The exploration research of optimization for ownerless tailing pond closure procedure in Guangxi[J]. China Mining Magazine, 2015,24(4):63-68.
[20]	星球地图出版社. 西藏自治区地图集[M]. 北京: 星球地图出版社, 2017: 2.
[20]	Star Map Publishing House. Atlas of Tibet Autonomous Region[M]. Beijing: Star Map Publishing House, 2017: 2.

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