Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2014, Vol. 26 Issue (2) : 162-169     DOI: 10.6046/gtzyyg.2014.02.26
Technology Application |
Discussion on genetic correlation between Huashan granite body and Guposhan granite body based on comparative analysis of fault structure features in remote sensing image
HAO Min1,2,3, WU Hong1,2,3, JIA Zhiqiang1,2,3, HUANG Daning4, LIU Yan1,2,3, GUAN Zhen1,2,3
1. Guangxi Scientific Experiment Center of Mining, Metallurgy and Environment, Guilin University of Technology, Guilin 541004, China;
2. Guangxi Key Laboratory of Hidden Metallic Ore Deposits Exploration, Guilin 541004, China;
3. Institute for Remote Sensing Application, College of Earth Science, Guilin University of Technology, Guilin 541004, China;
4. Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, China
Download: PDF(7990 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  Huashan granite body and Guposhan granite body are two neighboring granite bodies in Guidong area of Guangxi. They are closely related to the polymetallic mineralization of the area. The study of their formation is of practical significance for expanding prospecting for the non-ferrous metal mineral resources in Guidong area. As the fault structure was genetically closely related to the igneous geological action of the granite body,the authors investigated their spatial contact characteristics so as to help the discussion of the genetic correlation between the granite bodies. Using the ETM+ remote sensing image as the source of information and based on extracting the linear structure information in four directions,i.e., SN,EW,NE and NW, from the remote sensing images of Huashan and Guposhan granite bodies,the authors carried out the comparative statistical analysis,comparative rose diagram analysis and comparative equi-density map analysis as well as comparative structural stress field analysis of the fractures through trend surface simulation of the remote sensing linear structure in the two rock bodies. Finally,the genetic relevance and difference between the two rock bodies were detected through the comprehensive analysis.
Keywords LiDAR      point clouds      airborne imageries      building boundary extraction     
:  TP753  
Issue Date: 28 March 2014
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
LI Yunfan
GONG Weiping
LIN Yuxian
WANG Bo
Cite this article:   
LI Yunfan,GONG Weiping,LIN Yuxian, et al. Discussion on genetic correlation between Huashan granite body and Guposhan granite body based on comparative analysis of fault structure features in remote sensing image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 162-169.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.02.26     OR     https://www.gtzyyg.com/EN/Y2014/V26/I2/162
[1] 广东省地质局南岭区域地质测量普查大队.南岭侵入岩初步研究报告[R].北京:地质出版社,1959:1-230. Guangdong Provincial Bureau of Geology in Regional Geological Survey Team.A preliminary report on Nanling intrusive rocks[R].Beijing:Geological Publishing House,1959:1-230.
[2] 中国科学院地质研究所.富钟贺矿物志[M].北京:科学出版社,1965:1-114. Chinese Academy of Geological Sciences.Fuzhonghe Minerals[M].Beijing:Science Press,1965:1-114.
[3] 袁奎荣,何柏安,吴正扬.广西姑婆山里松花岗岩体的特征及其成因问题[C]//冶金部地质科研会议论文集:锡矿专辑,1966. Yuan K R,He P A,Wu Z Y.Characteristics and genesis of Lisong granite in Guposhan,Guangxi Province[C]//Ministry of Metallurgical Geological Research Conference Proceedings:Tin Ore Album,1966.
[4] 袁奎荣.隐伏花岗岩预测及深部找矿[M].北京:科学技术出版社,1990:92-112. Yuan K R.The prediction of hidden granites and the exploration of deep ore deposits and its prospect[M].Beijing:Science and Technology Publishing House,1990:92-112.
[5] 朱金初,李向东.广西花山花岗岩的岩石学和地球化学特征及成岩物质来源的探讨[J].岩石矿物学杂志,1988(1):30-40. Zhu J C,Li X D.Petrological-geochemical features and source materials of Huashan granites,Guangxi Autonomous Region[J].Acta Petrologica Et Mineralogic,1988(1):30-40.
[6] 顾晟彦,华仁民,戚华文.广西花山-姑婆山燕山期花岗岩的地球化学特征及成因研究[J].岩石矿物学杂志,2006,25(2):97-109. Gu S Y,Hua R M,Qi H W.Geochemistry and petrogenesis of the Yanshanian Huashan-Guposhan granites in Guangxi[J].Acta Petrologica and Mineralogica,2006,25(2):97-109.
[7] 邹仁辉,张小路,朱国器,等.广西花山-姑婆山花岗岩体的三维形态[J].桂林理工大学学报,2010,30(4):495-500. Zou R H,Zhang X L,Zhu G Q,et al.3D geometry of Huashan-Guposhan granite mass in Guangxi[J].Journal of Guilin University of Technology,2010,30(4):495-500.
[8] 黄晓娟,吴虹,周爱萍,等.基于遥感线性构造等密度图的姑婆山花岗岩体单元划分[J].桂林理工大学学报,2010,30(4):490-494. Huang X J,Wu H,Zhou A P,et al.Unit classification of Guposhan granite rock based on remote sensing lineament density image[J].Journal of Guilin University of Technology,2010,30(4):490-494.
[9] 朱亮璞.遥感地质学[M].北京:地质出版社,2001. Zhu L P,Remote sensing geology[M].Beijing:Geological Publishing House,2001.
[10] 池国祥.广西姑婆山复式岩基的多源特征及其大地构造形成机制[J].大地构造与成矿学,1989,13(1):69-79. Chi G X.Polysource features of Guposhan composite batholith with reference to their geotectonic mechanism[J].Geotectonica Et Metallogenia,1989,13(1):69-79.
[11] 冯佐海,梁金城,张桂林,等.论广西东部中生代花岗岩类岩石谱系单位——以姑婆山-花山花岗岩体为例[J].桂林工学院学报,2002,22(3):333-340. Feng Z H,Liang J C,Zhang G L,et al.On the lithodemic units of Mesozoic granitoid in east Guangxi:A case from Guposhan-Huashan granitic pluton[J].Journal of Guilin Institute of Technology,2002,22(3):333-340.
[12] 张永生.遥感图象信息系统[M].北京:科学出版社,2000. Zhang Y S.Remote sensing image information system[M].Beijing:Science Publishing House,2000.
[13] 楼性满,葛榜军.遥感找矿预测方法[M].北京:地质出版社,1994. Lou X M,Ge B J.Remote sensing prospecting prediction method[M].Beijing:Geological Publishing House,1994.
[14] 薛重生,王京名,刘敏,等.遥感图像构造线性体模式及结构分析[J].地质科技情报,1997,16(增刊):58-63. Xue Z S,Wang J M,Liu M,et al.Analysis of structural linement pattern and texture on remote sensing image[J].Geological Science and Technology Information,1997,16(s1):58-63.
[1] WU Fang, LI Yu, JIN Dingjian, LI Tianqi, GUO Hua, ZHANG Qijie. Application of 3D information extraction technology of ground obstacles in the flight trajectory planning of UAV airborne geophysical exploration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 286-292.
[2] Lei MENG, Chao LIN. Discussion on quality inspection and solution of DEM generated by airborne LiDAR technology[J]. Remote Sensing for Land & Resources, 2020, 32(1): 7-12.
[3] Zhenyu MA, Bowei CHEN, Yong PANG, Shengxi LIAO, Xianlin QIN, Huaiqing ZHANG. Forest fire potential forecast based on FCCS model[J]. Remote Sensing for Land & Resources, 2020, 32(1): 43-50.
[4] Qi LI, Jianchao WANG, Yachao HAN, Zihong GAO, Yongjun ZHANG, Dingjian JIN. Potential evaluation of China’s coastal airborne LiDAR bathymetry based on CZMIL Nova[J]. Remote Sensing for Land & Resources, 2020, 32(1): 184-190.
[5] Chong LI, Haolin LI, Yi SHE. Quality inspection of geographic information products based on multi-source remote sensing data[J]. Remote Sensing for Land & Resources, 2019, 31(4): 258-263.
[6] Juntao ZHU, Lei WANG, Chuan ZHAO, Xudong ZHENG. Point cloud segmentation on the roof of complicated building based on the algorithm of region growing[J]. Remote Sensing for Land & Resources, 2019, 31(4): 20-25.
[7] Lei DU, Jie CHEN, Minmin LI, Xiongwei ZHENG, Jing LI, Zihong GAO. The application of airborne LiDAR technology to landslide survey: A case study of Zhangjiawan Village landslides in Three Gorges Reservoir area[J]. Remote Sensing for Land & Resources, 2019, 31(1): 180-186.
[8] Sirui YANG, Zhaohui XUE, Ling ZHANG, Hongjun SU, Shaoguang ZHOU. Fusion of hyperspectral and LiDAR data: A case study for refined crop classification in agricultural region of Zhangye Oasis in the middle reaches of Heihe River[J]. Remote Sensing for Land & Resources, 2018, 30(4): 33-40.
[9] Li YAN, Yao LI, Hong XIE. Automatic reconstruction of LoD3 city building model based on airborne and vehicle-mounted LiDAR data[J]. Remote Sensing for Land & Resources, 2018, 30(4): 97-101.
[10] Jiasi YI, Xiangyun HU. Extracting impervious surfaces from multi-source remote sensing data based on Grabcut[J]. Remote Sensing for Land & Resources, 2018, 30(3): 174-180.
[11] Xue HE, Zhengrong ZOU, Yunsheng ZHANG, Shouji DU, Te ZHENG. Object-oriented classification method for oblique photogrammetric point clouds[J]. Remote Sensing for Land & Resources, 2018, 30(2): 87-92.
[12] LI Yunfan, TAN Debao, LIU Rui, WU Jianwei. An improved RANSAC algorithm for building point clouds segmentation in consideration of roof structure[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 20-25.
[13] YU Haiyang, LUO Ling, MA Huihui, LI Hui. Application appraisal in catchment hydrological analysis based on SRTM 1 Arc-Second DEM[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 138-143.
[14] LI Jiajun, ZHONG Ruofei. Route design of light airborne LiDAR[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 97-103.
[15] WANG Xudong, DUAN Fuzhou, QU Xinyuan, LI Dan, YU Panfeng. Building extraction based on UAV imagery data with the synergistic use of objected-based method and SVM classifier[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 97-103.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech