Internal Structure and Assemblage Features of Tectonic Slices in Dong Cuo Melange Zone

doi:10.6046/gtzyyg.2012.02.14

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Abstract In order to make a thorough study of the Dong Cuo melange zone, the authors used remote sensing interpretation to divide the Dong Cuo melange zone into three secondary tectonic slices belts, and found that the three belts extend nearly in parallel in the plane, converge eastward and westward, and thrust southward one by one in profile. Field investigation shows that the interpretation results are in accordance with the practical situation, and the material composition and geometric structure of rocks in various slices are different. The ophiolite melange belt is the main belt in the Dong Cuo melange zone.

Keywords remote sensing alternation anomaly ASTER Duobuza porphyry copper deposit

TP 79

Issue Date: 03 June 2012

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	HU Zi-hao
	TANG Ju-xing
	ZHANG Ting-bin
	WU Hua
	XU Zhi-zhong
	BIE Xiao-juan

Cite this article:

HU Zi-hao,TANG Ju-xing,ZHANG Ting-bin, et al. Internal Structure and Assemblage Features of Tectonic Slices in Dong Cuo Melange Zone[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 75-78.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2012.02.14 OR https://www.gtzyyg.com/EN/Y2012/V24/I2/75

[1] 高廷林,汤耀庆.西藏雅鲁藏布江中段构造混杂带特征及其构造意义[J].中国地质科学院院报,1984,8(1):1-18.
[2] 曾庆高,毛国政.1:25万改则幅报告[R].西藏:西藏地质调查院(06528),2002.
[3] 王国灿,张克信,梁斌,等.东昆仑造山带结构及构造岩片组合[J].地球科学-中国地质大学学报,1997,22(4):352-356.
[4] 陶晓风,刘登忠,朱利东,等.浅析构造混杂带的类型及组成特征——以滇西兔峨幅、弥沙井幅1:5万区调为例[J].中国区域地质,2001,20(2):137-140.
[5] 刘登忠,王国芝,陶晓风,等.澜沧江构造混杂带的初步研究[J].中国区域地质,2000,19(3):312-317.
[6] 吴新国,吕继东.雅鲁藏布江缝合带内构造岩片划分及形成——以仲巴—桑桑段为例[J].大地构造与成矿学,2005,30(3):320-325.

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