The Remote Sensing Alteration Information Extraction and Metallogenic Prognosis of the Boka Gold Deposit in Yunnan Province

doi:10.6046/gtzyyg.2011.02.25

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Abstract

Located on the east edge of middle Kangdian axis，the Boka gold deposit has complicated ore-forming background and great ore-prospecting potential. In this paper，ETM+ remote sensing image was used to conduct tectonic interpretation and extraction of mineralized alteration information in the study area，combined with a comprehensive prospecting analysis of regional geological and geochemical information. The intense anomaly information of mineralized alteration at the intersection between the NS-trending and EW-trending structural zones displays regular variation regularity, suggesting that the southern part of the Boka gold deposit and the NS-striking alteration belt of the study area has good ore-search prospects.

Keywords Remote sensing MODIS Vegetation index Vegetation index composing

TP 79

Issue Date: 17 June 2011

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CHENG Zhi-Yan, ZHAO Pei-Song. The Remote Sensing Alteration Information Extraction and Metallogenic Prognosis of the Boka Gold Deposit in Yunnan Province[J]. REMOTE SENSING FOR LAND & RESOURCES,2011, 23(2): 135-140.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.02.25 OR https://www.gtzyyg.com/EN/Y2011/V23/I2/135

［1］龙晓君，何政伟，刘严松，等，西藏羌多地区遥感蚀变与构造信息提取及成矿预测［J］.国土资源遥感，2010(2)：63-72.

［2］段元彬，刘登忠，徐韬，等.稻城地区遥感蚀变信息提取研究［J］.国土资源遥感，2008(4)：92-95.

［3］薛步高.东川托布卡金矿矿化层位与找金方向探讨［J］.化工矿产地质，2005，27(2):71-78.

［4］程辉明.昆明播卡金矿地质［J］.云南地质，2005，24(4):361-370.

［5］廖崇高，陈建平，刘登忠.兰坪盆地遥感地质及化探综合分析在成矿预测中的应用［J］.国土资源遥感，1999(4)：17-22.

［6］刘建军，杨武年，刘登忠.腾格尔拗陷查干诺尔地区古河道型砂岩铀矿遥感信息提取［J］.物化探计算机技术，1999，21(3)：248-251.

［7］张宏伟，杨武年.多源信息综合分析在新疆伊吾地区成矿预测中的应用［J］.物化探计算机技术，2000，22(4)：340-344.

［8］崔志强，刘登忠，刘海军，等.西藏措勤盆地新构造运动遥感研究［J］.新疆地质，2007，25(3)：313-316.

［9］刘登忠，陈建平，兰立波.川北地区岩石反射谱波特征［J］.国土资源遥感，2007(1)：31-36.

［10］王润生，杨苏明，阎柏琨.成像光谱矿物识别方法与识别模型评述［J］.国土资源遥感，2007(1)：1-9.

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