Volume 28 Issue 2
Apr.  2022
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Article Navigation >  Journal of Geomechanics  > 2022  >  28(2): 268-280
YUN Jin-biao, ZHAO Li-hua, 2003. RESEARCH ON MOLDING, STRUCTURAL FEATURE AND FORMATION MECHANISM OF THE CENTRAL UPLIFT BELT IN DONGYING DEPRESSION,JIYANG. Journal of Geomechanics, 9 (2): 113-121.
Citation: ZHU Yifei, YAO Xin, YAO Leihua, et al., 2022. Identification and risk assessment of coal mining-induced landslides in Guizhou Province by InSAR and optical remote sensing. Journal of Geomechanics, 28 (2): 268-280. DOI: 10.12090/j.issn.1006-6616.2021054
shu

Identification and risk assessment of coal mining-induced landslides in Guizhou Province by InSAR and optical remote sensing

doi: 10.12090/j.issn.1006-6616.2021054
  • 1.

    Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China

  • 2.

    Key Laboratory of Active Tectonics and Geological Safety, Ministry of Natural Resources, Beijing 100081, China

  • 3.

    Research Center of Neotectonism and Crustal Stability, China Geological Survey, Beijing 100081, China

  • 4.

    School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China

  • 5.

    School of Civil Engineering, Guizhou University, Guiyang 550000, Guizhou, China

Funds:

China Three Gorges Corporation Project YMJ (XLD)/(19) 110

the Chinese Geological Survey Project DD20221738-2

More Information
    Abstract
  • The Zongling landslide group in Guizhou Province is characterized by strong regularity of disaster formation, concentrated development and severe threat. It is represented in the coal mining-induced geological hazards in western Guizhou and eastern Yunnan. In this paper, InSAR and optical remote sensing were used for nuanced identification to obtain the regional landslide information, and the landslide deformation-failure mode in the Zongling region was summarized. Based on this, the volume-distance statistical formula suitable for landslide risk assessment in this region was established, and the typical disaster bodies ware calculated. Some important insights are gained: Underground coal mining is the major contributor to the deformation of the edge of Table Mountain in Zongling. InSAR observation results show that the deformation in the Zongling area has prominent zonal characteristics, and the annual average deformation velocity is between -20.4~10.2 cm/a, which corresponds well with the lower goaf. The areas with great displacement are concentrated in the edge zone of cuesta, with coal mining subsidence and slope gravity superimposed; There are 64 deformations in the Zongling area, including 37 landslides, 27 cracks, and 2 dangerous deformed bodies. Landslides mainly occur in the dark grey limestone strata and dark purplish-red argillaceous siltstone strata of the Feixianguan Formation. According to the lithology and deformation characteristics of the slip source, the landslides can be classified into two types: pull-toppling and pull-clipping, and the former is distinguished by large particle size, long movement distance, and severe threat. The formula for calculating the slip distance of rock landslide debris flow has good applicability to the slip distance of coal mining-induced landslide developed in the "upper hard and lower soft" strata in the Zongling area, and the verification error is less than 5%. The formula is used to calculate the dangerous deformed bodies of Zuojiaying and Jingjiao in the study area, and the danger avoidance distance is predicted to be 220~386 m. The proposed method of coal mining-induced landslide risk assessment based on differential interferometry and optical image play an exemplary role for the prevention and control of coal mining-induced landslides in western Guizhou and eastern Yunnan.

     

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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 7.1 %其他: 7.1 %其他: 0.1 %其他: 0.1 %Central District: 0.1 %Central District: 0.1 %China: 0.2 %China: 0.2 %India: 0.1 %India: 0.1 %Rochester: 0.1 %Rochester: 0.1 %San Lorenzo: 0.2 %San Lorenzo: 0.2 %United States: 0.1 %United States: 0.1 %三明: 0.1 %三明: 0.1 %上海: 1.1 %上海: 1.1 %东莞: 0.2 %东莞: 0.2 %中卫: 1.7 %中卫: 1.7 %临汾: 0.1 %临汾: 0.1 %丹东: 0.1 %丹东: 0.1 %乌鲁木齐: 0.1 %乌鲁木齐: 0.1 %乐山: 0.1 %乐山: 0.1 %佛山: 0.1 %佛山: 0.1 %佳木斯: 0.1 %佳木斯: 0.1 %兰州: 0.2 %兰州: 0.2 %包头: 0.2 %包头: 0.2 %北京: 16.8 %北京: 16.8 %华盛顿州: 0.1 %华盛顿州: 0.1 %南京: 0.8 %南京: 0.8 %南充: 0.1 %南充: 0.1 %南宁: 0.6 %南宁: 0.6 %南昌: 0.6 %南昌: 0.6 %南通: 0.1 %南通: 0.1 %台州: 0.3 %台州: 0.3 %合肥: 0.3 %合肥: 0.3 %呼和浩特: 0.2 %呼和浩特: 0.2 %和田: 0.1 %和田: 0.1 %咸阳: 0.2 %咸阳: 0.2 %哈尔滨: 0.1 %哈尔滨: 0.1 %哈里亚纳: 0.1 %哈里亚纳: 0.1 %嘉兴: 0.2 %嘉兴: 0.2 %固原: 0.1 %固原: 0.1 %大同: 0.2 %大同: 0.2 %大庆: 0.1 %大庆: 0.1 %大连: 0.1 %大连: 0.1 %天津: 0.3 %天津: 0.3 %威海: 0.1 %威海: 0.1 %安康: 0.4 %安康: 0.4 %安顺: 0.5 %安顺: 0.5 %宝鸡: 0.1 %宝鸡: 0.1 %宣城: 0.1 %宣城: 0.1 %巴音郭楞: 0.1 %巴音郭楞: 0.1 %常德: 0.1 %常德: 0.1 %平凉: 0.1 %平凉: 0.1 %广安: 0.1 %广安: 0.1 %广州: 0.6 %广州: 0.6 %延安: 0.1 %延安: 0.1 %弗罗茨瓦夫: 0.3 %弗罗茨瓦夫: 0.3 %张家口: 1.5 %张家口: 1.5 %徐州: 0.3 %徐州: 0.3 %德阳: 0.1 %德阳: 0.1 %惠州: 0.1 %惠州: 0.1 %成都: 1.5 %成都: 1.5 %扬州: 0.5 %扬州: 0.5 %抚顺: 0.1 %抚顺: 0.1 %拉萨: 0.1 %拉萨: 0.1 %拉贾斯坦邦: 0.1 %拉贾斯坦邦: 0.1 %无锡: 0.1 %无锡: 0.1 %昆明: 1.7 %昆明: 1.7 %昭通: 0.5 %昭通: 0.5 %曲靖: 0.1 %曲靖: 0.1 %杭州: 0.6 %杭州: 0.6 %柳州: 0.1 %柳州: 0.1 %株洲: 0.1 %株洲: 0.1 %桂林: 0.1 %桂林: 0.1 %武汉: 1.8 %武汉: 1.8 %沈阳: 0.3 %沈阳: 0.3 %泉州: 0.1 %泉州: 0.1 %法尔肯施泰因: 0.1 %法尔肯施泰因: 0.1 %洛阳: 0.2 %洛阳: 0.2 %济南: 0.3 %济南: 0.3 %淮南: 0.1 %淮南: 0.1 %深圳: 0.1 %深圳: 0.1 %温州: 0.1 %温州: 0.1 %渭南: 0.1 %渭南: 0.1 %湖州: 0.3 %湖州: 0.3 %湘潭: 0.2 %湘潭: 0.2 %湛江: 0.1 %湛江: 0.1 %滁州: 0.1 %滁州: 0.1 %漯河: 0.5 %漯河: 0.5 %焦作: 0.2 %焦作: 0.2 %玉林: 0.2 %玉林: 0.2 %石嘴山: 0.2 %石嘴山: 0.2 %石家庄: 0.8 %石家庄: 0.8 %福州: 0.1 %福州: 0.1 %秦皇岛: 0.1 %秦皇岛: 0.1 %纽约: 0.4 %纽约: 0.4 %绥化: 0.1 %绥化: 0.1 %罗马: 0.1 %罗马: 0.1 %舟山: 0.1 %舟山: 0.1 %芒廷维尤: 20.4 %芒廷维尤: 20.4 %芝加哥: 1.2 %芝加哥: 1.2 %苏州: 0.1 %苏州: 0.1 %荆门: 0.1 %荆门: 0.1 %莫斯科: 0.1 %莫斯科: 0.1 %衡水: 0.3 %衡水: 0.3 %衡阳: 0.1 %衡阳: 0.1 %西宁: 16.5 %西宁: 16.5 %西安: 1.9 %西安: 1.9 %西雅图: 0.1 %西雅图: 0.1 %许昌: 0.1 %许昌: 0.1 %诺沃克: 0.1 %诺沃克: 0.1 %贵港: 0.1 %贵港: 0.1 %贵阳: 3.7 %贵阳: 3.7 %赣州: 0.1 %赣州: 0.1 %达州: 0.3 %达州: 0.3 %运城: 0.6 %运城: 0.6 %连云港: 0.3 %连云港: 0.3 %通辽: 0.1 %通辽: 0.1 %遂宁: 0.1 %遂宁: 0.1 %遵义: 0.1 %遵义: 0.1 %邢台: 0.1 %邢台: 0.1 %邯郸: 0.1 %邯郸: 0.1 %邵阳: 0.1 %邵阳: 0.1 %郑州: 0.1 %郑州: 0.1 %酒泉: 0.1 %酒泉: 0.1 %重庆: 0.4 %重庆: 0.4 %钦州: 0.1 %钦州: 0.1 %铁岭: 0.1 %铁岭: 0.1 %银川: 0.1 %银川: 0.1 %长春: 0.6 %长春: 0.6 %长沙: 1.2 %长沙: 1.2 %阜新: 0.2 %阜新: 0.2 %阜阳: 0.1 %阜阳: 0.1 %阳泉: 0.1 %阳泉: 0.1 %青岛: 0.2 %青岛: 0.2 %鞍山: 0.1 %鞍山: 0.1 %马鞍山: 0.1 %马鞍山: 0.1 %黔南: 0.1 %黔南: 0.1 %齐齐哈尔: 0.1 %齐齐哈尔: 0.1 %龙岩: 0.1 %龙岩: 0.1 %其他其他Central DistrictChinaIndiaRochesterSan LorenzoUnited States三明上海东莞中卫临汾丹东乌鲁木齐乐山佛山佳木斯兰州包头北京华盛顿州南京南充南宁南昌南通台州合肥呼和浩特和田咸阳哈尔滨哈里亚纳嘉兴固原大同大庆大连天津威海安康安顺宝鸡宣城巴音郭楞常德平凉广安广州延安弗罗茨瓦夫张家口徐州德阳惠州成都扬州抚顺拉萨拉贾斯坦邦无锡昆明昭通曲靖杭州柳州株洲桂林武汉沈阳泉州法尔肯施泰因洛阳济南淮南深圳温州渭南湖州湘潭湛江滁州漯河焦作玉林石嘴山石家庄福州秦皇岛纽约绥化罗马舟山芒廷维尤芝加哥苏州荆门莫斯科衡水衡阳西宁西安西雅图许昌诺沃克贵港贵阳赣州达州运城连云港通辽遂宁遵义邢台邯郸邵阳郑州酒泉重庆钦州铁岭银川长春长沙阜新阜阳阳泉青岛鞍山马鞍山黔南齐齐哈尔龙岩

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