THE COMPREHENSIVE INQUIRE INTO THE BOUNDARY FAULT OF NORTH CHINA PLATFORM'S NORTH MARGIN BY REMOTE SENSING AND GEOSCIENCE

doi:10.6046/gtzyyg.1995.02.03

Abstract
References
Related Articles
Metrics

Download: PDF(1233 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

The boundary between North China platform's North margin and XinganNei Mongol fold system,is a deep and great fault zone, which is the kind of boundary between the largest geotectonic units on the Earth Surface.The authors interpreted the landsat TMimages of an area of 4×10⁵ km² of this zone, referred to gravity and air-magnetic survey and other related geological data, and took into consideration their previous field surveys of different parts of the zone, thus got some new understanding about the stretch position,Nature and ore control of this fault zone.

Keywords Remote sensing Riverbed The Beijiang River

Issue Date: 02 August 2011

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	ZHONG Kai-Wen
	LIU Wan-Xia
	HUANG Jian-Ming
	LU Li
	LI Wu
	LI Ji-Peng
	WU Chuan-Zhi
	ZHU Huai-Ping

Cite this article:

ZHONG Kai-Wen,LIU Wan-Xia,HUANG Jian-Ming, et al. THE COMPREHENSIVE INQUIRE INTO THE BOUNDARY FAULT OF NORTH CHINA PLATFORM'S NORTH MARGIN BY REMOTE SENSING AND GEOSCIENCE[J]. REMOTE SENSING FOR LAND & RESOURCES, 1995, 7(2): 15-22.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.1995.02.03 OR https://www.gtzyyg.com/EN/Y1995/V7/I2/15

[1] 王荃.内蒙古中部中朝与西伯利亚古板块间缝合线的确定.地质学报,1986, 6( 1):31-43.

[2] 胡骁,牛树银.内蒙古温都尔庙地区早古生代板块俯冲带的研究,见《中国北方板块构造论文集》第1集,北京:地质出版社,1986, 87-101.

[3] 朱学礼.浅谈华北地台北界中段划分问题.铀矿地质,1991,7(1):31~34

[4] 张允平,唐克东,苏养正.由陆壳增生旋迴的观点试论内蒙古中部地区的加里东运动,《中国北方板块构造论文集》,第一集,北京:地质出版社,1986, 102~114

[5] 内蒙古自治区地质矿产局.内蒙古自治区区域地质志.见中华人民共和国地质矿产部地质专报:一、区域地质,第25号,北京:地质出版社,1991.

[6] 吉林省地质矿产局.吉林省区域地质志.中华人民共和国地质矿产部地质专报:一、区域地质,第10号.北京:地质出版社,1988.

[7] 王荃,刘雪亚,李锦轶.中国华夏与安加拉古陆间的板块构造.见《中国北方板块构造丛书》.北京:北京大学出版社,1991, 6.

[8] 王楫,李双庆.狼山一白云鄂博裂谷系及其成矿特征,见《中国北方板块构造论文集》,第2集,北京:地质出版社,1987, 59~72.

[9] 杨遵仪,程裕祺,王鸿祯.中国地质学.武汉:中国地质大学出版社,1989, 295~246.

[10] 陈辉,邵济安.白云鄂博地区碳酸岩的形成方式及构造背景.见《中国北方板块构造论文集》,第2集北京:地质出版社,1987, 73~79.

[11] 姚大金.中朝地台北缘东段的平移剪切和推覆构造.中国区域地质.1989,(3):262~268.

[1]	LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[2]	WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[3]	LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[4]	ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[5]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[6]	SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[7]	LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[8]	DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[9]	GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[10]	ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[11]	HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[12]	YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[13]	LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[14]	AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[15]	LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.

Viewed

Full text

Abstract

Cited

Shared

Discussed