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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (1) : 187-194     DOI: 10.6046/gtzyyg.2019.01.25
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Research on coseismic deformation in Ukraine River Valley of Tianshan Mountains based on InSAR technology
Dong WANG1,2, Fengbing LAI1,2(), Mengyu CHEN3, Shujiang CHEN1,2, Tiecheng HUANG1,2, Xiang JIA1,2
1.College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830054,China
2.Urumqi Institute of Spatial Remote Sensing Applications, Urumqi 830025, China
3.School of Foreign Languages, Suzhou University of Science and Technology, Suzhou 215009, China
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Abstract  

In this paper, the authors studied the coseismic deformation in Ukraine River valley in Tianshan Mountains, and reconstructed digital elevation modle (DEM) graph of 12.5 m spatial resolution for the study area before and after the earthquake using the ALOS / PALSAR data by InSAR remote sensing technique. Then the authors obtained the remote sensing characteristics of seismic collapse of earthquake by difference method of the DEM after the field verification by RTK calibration. The results show that: ①The area of the collapse of the triangle is 104.47 million m 2, the collapse is 1 416.60 million m 3, the starting elevation is 3 225 m, the average slope is 48°, and slope direction is NNW. ②The trumpet-shaped accumulation body area is 78.61 million m 2, the accumulation is 1 424.27 million m 3, and the buried depth is between 35 m and 80 m. ③The river bed was pushed northward by 100 m, and the eastward advance reached 300 m, due to the accumulation body of the south of the river channel. ④Collapse of the body led to the the formation of quake river. The surface area of the lake increased from 0.039 km 2 to 0.059 km 2 within 30 days and the lake area reached the peak of 0.146 km 2 in 2010. ⑤The factors responsible for the collapse included not only seismic activity but also limestone lithology, terrain slope, fault structure and other comprehensive factors.

Keywords InSAR      coseismic deformation      Tianshan Mountains      remote sensing      dammed lake     
:  P316P694  
Corresponding Authors: Fengbing LAI     E-mail: laifengbing@163.com
Issue Date: 14 March 2019
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Dong WANG
Fengbing LAI
Mengyu CHEN
Shujiang CHEN
Tiecheng HUANG
Xiang JIA
Cite this article:   
Dong WANG,Fengbing LAI,Mengyu CHEN, et al. Research on coseismic deformation in Ukraine River Valley of Tianshan Mountains based on InSAR technology[J]. Remote Sensing for Land & Resources, 2019, 31(1): 187-194.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.01.25     OR     https://www.gtzyyg.com/EN/Y2019/V31/I1/187
Fig.1  Diagram of the study area
发震时刻 纬度/(°) 经度/(°) 震中距/km 深度/km 里氏震级(ML) 短周期体
波震级(mb)
台站数目 标准偏差
2007/07/03
15∶21∶09.1
44.49 N 83.37 E 38.14 10.0 5.7 5.9 10 2.93
Tab.1  Earthquake table of China earthquake station(northern part of Xinjiang,China)
台站 震中距/(°) 方位角/(°) 震相 到时 到时偏差/s 震级 周期/s 振幅/μm
WMQ 3.2 101.0 Pn 15: 22: 01.0 1.4
WMQ 3.2 101.0 Sg 15: 22: 50.0 1.0
WMQ 3.2 101.0 SMN 5.7 0.5 0.260 00
WMQ 3.2 101.0 SME 5.7 0.5 0.250 00
Tab.2  Earthquake seismic record of China earthquake station(northern part of Xinjiang,China)
Fig.2  DEM processing flow of InSAR technology
Fig.3  DEM reconstructed by InSAR technology before and after the earthquake case
Fig.4  Results of the DEM accuracy check
Fig.5  Sketch map of the collapse and accumulation of regional grid
Fig.6  Schematic diagram of the collapse body and the accumulation body
Fig.7  Slope and aspect change after the earthquake case in the study area
Fig.8  Topographic variation after earthquake case in the study area
Fig.9  Monitoring the change of Ukraine quake lake between 2006 and 2016 by Landsat satellite
监测时间 湖面面积/m2 湖面高程/m
2006年8月14日 39 000 2 402.904
2007年8月1日 59 000 2 406.829
2007年8月17日 88 900 2 409.031
2007年9月18日 53 000 2 405.716
2008年7月26日 89 400 2 410.874
2009年6月19日 48 400 2 404.441
2010年8月17日 146 000 2 420.724
2011年7月12日 78 400 2 408.927
2013年8月9日 94 300 2 411.030
2014年8月21日 91 050 2 410.951
2015年7月6日 89 400 2 410.706
2016年8月18日 97 200 2 411.125
Tab.3  Change of lake area and elevation in Ukraine between 2006 and 2016
[1] 章鑫, 杜学彬, 王丽 , 等. 南北地震带大地电流分布与地震活动关系探讨[J]. 地学前缘, 2017,24(2):202-211.
doi: 10.13745/j.esf.2017.02.019 url: http://d.wanfangdata.com.cn/Periodical/dxqy201702020
[1] Zhang X, Du X B, Wang L , et al. The spatial distribution of telluric currents and its relationship with earthquake activities in North-South Seismic Belt[J]. Earth Science Frontiers, 2017,24(2):202-211.
[2] 张钰, 陈晓清, 游勇 , 等. 汶川地震后肖家沟泥石流活动特征与灾害防治[J]. 水土保持通报, 2014,34(5):284-289.
url: http://d.wanfangdata.com.cn/Periodical/stbctb201405055
[2] Zhang Y, Chen X Q, You Y , et al. Activity characteristics and disaster control of Xiaojia Gully debris flow after Wenchuan earthquake[J]. Bulletin of Soil and Water Conservation, 2014,34(5):284-289.
[3] 石振明, 郑鸿超, 彭铭 , 等. 考虑不同泄流槽方案的堰塞坝溃决机理分析——以唐家山堰塞坝为例[J]. 工程地质学报, 2016,24(5):741-751.
doi: 10.13544/j.cnki.jeg.2016.05.003 url: http://d.wanfangdata.com.cn/Periodical/gcdzxb201605004
[3] Shi Z M, Zheng H C, Peng M , et al. Breaching mechanism analysis of landslide dams considering different spillway schemes:A case study of Tangjiashan landslide dam[J]. Journal of Engineering Geo-logy, 2016,24(5):741-751.
[4] 陈丹蕾, 刘国祥, 王晓文 , 等. 基于DInSAR技术与断层自动剖分方法反演断层滑动参数[J]. 国土资源遥感, 2016,28(3):25-0.doi: 10.6046.gtzyyg.2016.03.05.
doi: 10.6046/gtzyyg.2016.03.05 url: http://www.cqvip.com/QK/91397X/201603/669995278.html
[4] Chen D L, Liu G X, Wang X W , et al. Inversion of fault slip parameters based on DInSAR and automated fault model discretization method[J]. Remote Sensing for Land and Resources, 2016,28(3):25-30.doi: 10.6046.gtzyyg.2016.03.05.
[5] O’Connor J E, Costa J E . The World’s Largest Floods,Past and Present:Their Causes and Magnitudes[M]. U.S.Geological Survey, 2004: 1-13.
[6] Montgomery D R, Hallet B, Liu Y P , et al. Evidence for Holocene mega floods down the Tsangpo River gorge,Southeastern Tibet[J]. Quaternary Research, 2004,62(2):201-207.
doi: 10.1016/j.yqres.2004.06.008 url: https://www.cambridge.org/core/product/identifier/S003358940002682X/type/journal_article
[7] 赵峰 . 多平台时序InSAR技术的地表形变联合监测方法研究[D]. 徐州:中国矿业大学, 2016.
[7] Zhao F . Study on the Method of Multi-platform SAR Data Sets and MTInSAR Technique Based Ground Deformation Monitoring[D]. Xuzhou:China University of Mining and Technology, 2016.
[8] 雍琦 . InSAR和GPS大地测量形变数据反演地震断层滑动的影响特征研究[D]. 成都:西南交通大学, 2017.
[8] Yong Q . Research on Characteristics of Inversion for Earthquake Fault Slip Constrained by InSAR and GPS Geodetic Deformation Data[D]. Chengdu:Southwest Jiaotong University, 2017.
[9] Mckillop R J, Clague J J . Statistical remote sensing-based appro-ach for estimating the probability of catastrophic drainage from moraine-dammed lakes in southwestern British Columbia[J]. Global and Planetary Change, 2007,56(1-2):153-171.
doi: 10.1016/j.gloplacha.2006.07.004 url: https://linkinghub.elsevier.com/retrieve/pii/S0921818106001639
[10] 王治华, 徐起德 . 西藏帕里湖卫星遥感监测[J]. 山地学报, 2009,27(2):240-247.
doi: 10.3969/j.issn.1008-2786.2009.02.015 url: http://www.cnki.com.cn/Article/CJFDTOTAL-SDYA200902017.htm
[10] Wang Z H, Xu Q D . Satellite remote sensing monitoring for Pali Lake of Tibet,China[J]. Mountain Research, 2009,27(2):240-247.
[11] 范建容, 田兵伟, 程根伟 , 等. 基于多源遥感数据的5·12汶川地震诱发堰塞体信息提取[J]. 山地学报, 2008,26(3):257-262.
doi: 10.3969/j.issn.1008-2786.2008.03.001 url: http://www.cqvip.com/QK/71135X/201107/27662374.html
[11] Fan J R, Tian B W, Cheng G W , et al. Investigation on damming object induced by the earthquake of Wenchuan on May 12 based on multi-platform remote sensing[J]. Mountain Research, 2008,26(3):257-262.
[12] 殷幼松 . 高相干分布式目标应用于时间序列InSAR技术监测地表形变研究[D]. 北京:中国测绘科学研究院, 2016.
[12] Yin Y S . Highly-coherence Distributed Scatterer Time-series InSAR and Its Application to Monitoring Ground Deformation[D]. Beijing:Chinese Academy of Surveying and Mapping, 2016.
[13] 高明亮, 宫辉力, 陈蓓蓓 , 等. 基于InSAR技术的地表三维形变获取方法综述[J]. 测绘通报, 2017(1):1-4.
doi: 10.13474/j.cnki.11-2246.2017.0001 url: http://www.cqvip.com/QK/93318X/201701/671197381.html
[13] Gao M L, Gong H L, Chen B B , et al. Review of three-dimensional surface deformation acquisition from InSAR measurements and its application[J].Bulletin of Surveying and Mapping, 2017(1):1-4.
[14] 刘振国 . DInSAR技术在矿区地表重复采动开采沉陷监测中的应用研究[D]. 徐州:中国矿业大学, 2014.
[14] Liu Z G . Study on the Subsidence Monitoring Caused by Repeated Excavation with DInSAR Technology[D]. Xuzhou:China University of Mining and Technology, 2014.
[15] 占文俊, 李志伟, 韦建超 , 等. 一种InSAR大气相位建模与估计方法[J]. 地球物理学报, 2015,58(7):2320-2329.
doi: 10.6038/cjg20150710 url: http://www.cqvip.com/QK/94718X/201507/665685072.html
[15] Zhan W J, Li Z W, Wei J C , et al. A strategy for modeling and estimating atmospheric phase of SAR[J]. Chinese Journal of Geophysics, 2015,58(7):2320-2329.
[16] 罗菲 . 查干莫顿水电站调水流量分析[J]. 小水电, 2017(1):34-36.
doi: 10.3969/j.issn.1007-7642.2017.01.012 url: http://d.wanfangdata.com.cn/Periodical/xsd201701012
[16] Luo F . Analysis on water diversion flow of Chagan Morton hydropower station[J]. Small Hydro Power, 2017(1):34-36.
[17] 张悼元, 王士天 .滑坡文集(8)[C]// 滑坡文集编委会. 滑坡文集. 北京: 中国铁道出版社, 1982: 92-95.
[17] Zhang D Y, Wang S T. Landslide articles editorial board (8)[C]// Landslide articles editorial board. Landslide artides. Beijing: China Railway Publishing House, 1982: 92-95.
[18] 周廷儒 . 新疆地貌[M]. 北京: 科学出版社, 1978.
[18] Zhou T R. Xinjiang Geomorphology[M]. Beijing: Science Press, 1978.
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