WANG Xin, FANG Chengyong, TANG Xiaochuan, DAI Lanxin, FAN Xuanmei, XU Qiang. Research on Emergency Evaluation of Landslides Induced by the Luding Ms 6.8 Earthquake[J]. Geomatics and Information Science of Wuhan University, 2023, 48(1): 25-35. DOI: 10.13203/j.whugis20220586
Citation: WANG Xin, FANG Chengyong, TANG Xiaochuan, DAI Lanxin, FAN Xuanmei, XU Qiang. Research on Emergency Evaluation of Landslides Induced by the Luding Ms 6.8 Earthquake[J]. Geomatics and Information Science of Wuhan University, 2023, 48(1): 25-35. DOI: 10.13203/j.whugis20220586

Research on Emergency Evaluation of Landslides Induced by the Luding Ms 6.8 Earthquake

More Information
  • Received Date: September 14, 2022
  • Available Online: September 16, 2022
  • Published Date: January 04, 2023
  •   Objectives  On 5th September 2022, an Ms 6.8 earthquake struck the Luding County, Ganzi Prefecture, Sichuan Province, China. This earthquake triggered extensive geological hazards in the mountainous area, leading to serious casualties. Rapidly and accurately obtaining the spatial distribution of the induced geological hazards is crucial for emergency decision-making and rescue after an earthquake.
      Methods  Based on the global coseismic landslide database and deep learning algorithm, this paper built a near real-time prediction model of spatial distribution probability of coseismic landslides, and obtained the prediction results of the geological hazards induced by the Luding earthquake within 2 hours after the event. Through the post-earthquake unmanned aerial vehicle(UAV)and satellite remote sensing images, machine learning and deep learning algorithms were used to realize the automated recognition of large-scale geological hazards. A total of 3 633 earthquake-induced landslides with an area of 13.78 km2 were interpreted. Finally, the model was optimized by integrating these landslide data, and the prediction results of coseismic landslides with a broader area and higher accuracy were achieved.
      Results  The results show that the coseismic landslide prediction model can realize a rapid capture of spatial distribution of post-earthquake geological hazards, filling the blank period before the acquisition of post-earthquake remote sensing images and providing support for post-disaster emergency rescue.
      Conclusions  Intelligent identification technologies based on UAV and satellite remote sensing images are effective means to rapidly obtain the vital information of large-scale geological hazards. The achievements obtained in this paper played an important role in the emergency rescue after the Luding earthquake.
  • [1]
    邓起东, 张培震, 冉勇康, 等. 中国活动构造与地震活动[J]. 地学前缘, 2003, 10(S1): 66-73. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY2003S1011.htm

    Deng Qidong, Zhang Peizhen, Ran Yongkang, et al. Active Tectonics and Earthquake Activities in China[J]. Earth Science Frontiers, 2003, 10(S1): 66-73. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY2003S1011.htm
    [2]
    邓起东, 程绍平, 马冀, 等. 青藏高原地震活动特征及当前地震活动形势[J]. 地球物理学报, 2014, 57(7): 2025-2042. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201407001.htm

    Deng Qidong, Cheng Shaoping, Ma Ji, et al. Seismic Activities and Earthquake Potential in the Tibetan Plateau[J]. Chinese Journal of Geophysics, 2014, 57(7): 2025-2042. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201407001.htm
    [3]
    许冲, 徐锡伟, 吴熙彦, 等. 2008年汶川地震滑坡详细编目及其空间分布规律分析[J]. 工程地质学报, 2013, 21(1): 25-44. doi: 10.3969/j.issn.1004-9665.2013.01.004

    Xu Chong, Xu Xiwei, Wu Xiyan, et al. Detailed Catalog of Landslides Triggered by the 2008 Wenchuan Earthquake and Statistical Analyses of Their Spatial Distribution[J]. Journal of Engineering Geology, 2013, 21(1): 25-44. doi: 10.3969/j.issn.1004-9665.2013.01.004
    [4]
    殷跃平, 张永双, 马寅生, 等. 青海玉树Ms 7.1级地震地质灾害主要特征[J]. 工程地质学报, 2010, 18(3): 289-296. doi: 10.3969/j.issn.1004-9665.2010.03.001

    Yin Yueping, Zhang Yongshuang, Ma Yinsheng, et al. Research on Major Characteristics of Geohazards Induced by the Yushu Ms 7.1 Earthquake[J]. Journal of Engineering Geology, 2010, 18(3): 289-296. doi: 10.3969/j.issn.1004-9665.2010.03.001
    [5]
    戴岚欣, 许强, 范宣梅, 等. 2017年8月8日四川九寨沟地震诱发地质灾害空间分布规律及易发性评价初步研究[J]. 工程地质学报, 2017, 25(4): 1151-1164. doi: 10.13544/j.cnki.jeg.2017.04.030

    Dai Lanxin, Xu Qiang, Fan Xuanmei, et al. A Preliminary Study on Spatial Distribution Patterns of Landslides Triggered by Jiuzhaigou Earthquake in Sichuan on August 8th, 2017 and Their Susceptibility Assessment[J]. Journal of Engineering Geology, 2017, 25(4): 1151-1164. doi: 10.13544/j.cnki.jeg.2017.04.030
    [6]
    范宣梅, 方成勇, 戴岚欣, 等. 地震诱发滑坡空间分布概率近实时预测研究: 以2022年6月1日四川芦山地震为例[J]. 工程地质学报, 2022, 30(3): 729-739. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202203012.htm

    Fan Xuanmei, Fang Chengyong, Dai Lanxin, et al. Near Real Time Prediction of Spatial Distribution Probability of Earthquake-Induced Landslides—Take the Lushan Earthquake on June 1, 2022 as an Example[J]. Journal of Engineering Geology, 2022, 30(3): 729-739. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202203012.htm
    [7]
    Yin Y P, Wang F W, Sun P. Landslide Hazards Triggered by the 2008 Wenchuan Earthquake, Sichuan, China[J]. Landslides, 2009, 6(2): 139-152. doi: 10.1007/s10346-009-0148-5
    [8]
    Huang R Q, Fan X M. The Landslide Story[J]. Nature Geoscience, 2013, 6(5): 325-326. doi: 10.1038/ngeo1806
    [9]
    Xu M, Rhee S Y. Becoming Data-Savvy in a Big-Data World[J]. Trends in Plant Science, 2014, 19(10): 619-622. doi: 10.1016/j.tplants.2014.08.003
    [10]
    殷跃平. 汶川八级地震地质灾害研究[J]. 工程地质学报, 2008, 16(4): 433-444. doi: 10.3969/j.issn.1004-9665.2008.04.001

    Yin Yueping. Researches on the Geo-hazards Triggered by Wenchuan Earthquake, Sichuan[J]. Journal of Engineering Geology, 2008, 16(4): 433-444. doi: 10.3969/j.issn.1004-9665.2008.04.001
    [11]
    黄润秋, 李为乐. "5.12"汶川大地震触发地质灾害的发育分布规律研究[J]. 岩石力学与工程学报, 2008, 27(12): 2585-2592. doi: 10.3321/j.issn:1000-6915.2008.12.028

    Huang Runqiu, Li Weile. Research on Development and Distribution Rules of Geohazards Induced by Wenchuan Earthquake on 12th May, 2008[J]. Chinese Journal of Rock Mechanics and Engineering, 2008, 27(12): 2585-2592. doi: 10.3321/j.issn:1000-6915.2008.12.028
    [12]
    许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报(信息科学版), 2019, 44(7): 957-966. doi: 10.13203/j.whugis20190088

    Xu Qiang, Dong Xiujun, Li Weile. Integrated Space-Air-Ground Early Detection, Monitoring and Warning System for Potential Catastrophic Geohazards[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 957-966. doi: 10.13203/j.whugis20190088
    [13]
    李振洪, 宋闯, 余琛, 等. 卫星雷达遥感在滑坡灾害探测和监测中的应用: 挑战与对策[J]. 武汉大学学报(信息科学版), 2019, 44(7): 967-979. doi: 10.13203/j.whugis20190098

    Li Zhenhong, Song Chuang, Yu Chen, et al. Application of Satellite Radar Remote Sensing to Landslide Detection and Monitoring: Challenges and Solutions[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 967-979. doi: 10.13203/j.whugis20190098
    [14]
    Booth A M, Lamb M P, Avouac J P, et al. Landslide Velocity, Thickness, and Rheology from Remote Sensing: La Clapière Landslide, France[J]. Geophysical Research Letters, 2013, 40(16): 4299-4304. doi: 10.1002/grl.50828
    [15]
    Mantovani F, Soeters R, van Westen C J. Remote Sensing Techniques for Landslide Studies and Hazard Zonation in Europe[J]. Geomorphology, 1996, 15(3/4): 213-225.
    [16]
    Li Z B, Shi W Z, Lu P, et al. Landslide Mapping from Aerial Photographs Using Change Detection-Based Markov Random Field[J]. Remote Sensing of Environment, 2016, 187: 76-90. doi: 10.1016/j.rse.2016.10.008
    [17]
    Lu P, Shi W, Wang Q, et al. Co-seismic Landslide Mapping Using Sentinel-2 10-m Fused NIR Narrow, Red-Edge, and SWIR Bands[J]. Landslides, 2021, 18(6): 2017-2037. doi: 10.1007/s10346-021-01636-2
    [18]
    Martha T R, Kerle N, van Westen C J, et al. Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(12): 4928-4943. doi: 10.1109/TGRS.2011.2151866
    [19]
    王敏杰, 李天斌, 孟陆波, 等. 四川"Y字形"断裂交汇部应力场反演分析[J]. 铁道科学与工程学报, 2015, 12(5): 1088-1095. doi: 10.3969/j.issn.1672-7029.2015.05.016

    Wang Minjie, Li Tianbin, Meng Lubo, et al. Back Analysis of Stress Field in the Intersection Region of Y Shaped Fault, Sichuan[J]. Journal of Railway Science and Engineering, 2015, 12(5): 1088-1095. doi: 10.3969/j.issn.1672-7029.2015.05.016
    [20]
    徐晶, 邵志刚, 马宏生, 等. 鲜水河断裂带库仑应力演化与强震间关系[J]. 地球物理学报, 2013, 56(4): 1146-1158. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201304012.htm

    Xu Jing, Shao Zhigang, Ma Hongsheng, et al. Evolution of Coulomb Stress and Stress Interaction Among Strong Earthquakes Along the Xianshuihe Fault Zone[J]. Chinese Journal of Geophysics, 2013, 56(4): 1146-1158. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201304012.htm
    [21]
    吴萍萍, 李振, 李大虎, 等. 基于ANSYS接触单元模型的鲜水河断裂带库仑应力演化数值模拟[J]. 地球物理学进展, 2014, 29(5): 2084-2091. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWJ201405014.htm

    Wu Pingping, Li Zhen, Li Dahu, et al. Numerical Simulation of Stress Evolution on Xianshuihe Fault Based on Contact Element Model[J]. Progress in Geophysics, 2014, 29(5): 2084-2091. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWJ201405014.htm
    [22]
    熊维, 谭凯, 余鹏飞, 等. 鲜水河断裂近期库仑应力演化及其与康定Mw 5.9地震的关系[J]. 大地测量与地球动力学, 2016, 36(2): 95-100. https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201602001.htm

    Xiong Wei, Tan Kai, Yu Pengfei, et al. Triggering of Mw 5.9 Kangding Earthquake by Coulomb Stress Evolution Along Xianshuihe Fault Zone Since 1955[J]. Journal of Geodesy and Geodynamics, 2016, 36(2): 95-100. https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201602001.htm
    [23]
    Chen X L, Liu C G, Wang M M, et al. Causes of Unusual Distribution of Coseismic Landslides Triggered by the Mw 6.1 2014 Ludian, Yunnan, China Earthquake[J]. Journal of Asian Earth Sciences, 2018, 159: 17-23. doi: 10.1016/j.jseaes.2018.03.010
    [24]
    Fan X M, Xu Q, Scaringi G, et al. The "Long" Runout Rock Avalanche in Pusa, China, on August 28, 2017: A Preliminary Report[J]. Landslides, 2019, 16(1): 139-154. doi: 10.1007/s10346-018-1084-z
    [25]
    Hu K H, Zhang X P, You Y, et al. Landslides and Dammed Lakes Triggered by the 2017 Ms 6.9 Milin Earthquake in the Tsangpo Gorge[J]. Landslides, 2019, 16(5): 993-1001. doi: 10.1007/s10346-019-01168-w
    [26]
    Li G, West A J, Densmore A L, et al. Seismic Mountain Building: Landslides Associated with the 2008 Wenchuan Earthquake in the Context of a Generalized Model for Earthquake Volume Balance[J]. Geochemistry, Geophysics, Geosystems, 2014, 15(4): 833-844. doi: 10.1002/2013GC005067
    [27]
    Xu C, Xu X W, Shyu J B H. Database and Spatial Distribution of Landslides Triggered by the Lushan, China Mw 6.6 Earthquake of 20 April 2013[J]. Geomorphology, 2015, 248: 77-92. doi: 10.1016/j.geomorph.2015.07.002
    [28]
    Li S J, Xiong L Y, Tang G A, et al. Deep Learning-Based Approach for Landform Classification from Integrated Data Sources of Digital Elevation Model and Imagery[J]. Geomorphology, 2020, 354: 107045. doi: 10.1016/j.geomorph.2020.107045
    [29]
    Tang X, Tu Z, Wang Y, et al. Automatic Detection of Coseismic Landslides Using a New Transformer Method[J]. Remote Sensing, 2022, 14(12): 2884. doi: 10.3390/rs14122884
    [30]
    Dosovitskiy A, Beyer L, Kolesnikov A, et al. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale[J]. arXiv Preprint, 2020, arXiv: 2010.11929.
    [31]
    Xie E Z, Wang W H, Yu Z D, et al. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers[J]. Advances in Neural Information Processing Systems, 2021, 34: 12077-12090.
    [32]
    Wang X, Fan X M, Xu Q, et al. Change Detection-Based Co-Seismic Landslide Mapping Through Extended Morphological Profiles and Ensemble Strategy[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 187: 225-239. doi: 10.1016/j.isprsjprs.2022.03.011
    [33]
    Loshchilov I, Hutter F. Decoupled Weight Decay Regularization[J]. arXiv Preprint, 2017, arXiv: 1711.05101.
  • Related Articles

    [1]Chen Xinyang, Long Xiaoxiang, Li Qingpeng, Li Jingmei, Han Qijin, Xu Zhaopeng, Yao Weiyuan. Data Proccing and Accuracy Verification for Laser Altimeter of Terrestrial Ecosystem Carbon Inventory Satellite[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230110
    [2]ZHOU Ping, TANG Xinming, WANG Xia, LIU Changru, WANG Zhenming. Geometric Accuracy Evaluation Model of Domestic Push-Broom Mapping Satellite Image[J]. Geomatics and Information Science of Wuhan University, 2018, 43(11): 1628-1634. DOI: 10.13203/j.whugis20160486
    [3]YAN Wei, LIU Jianjun, REN Xin, WANG Fenfei. Accuracy Analysis of CE-3 Moon-Based Ultraviolet Telescope Geometric Positioning[J]. Geomatics and Information Science of Wuhan University, 2018, 43(1): 133-137, 166. DOI: 10.13203/j.whugis20150162
    [4]MENG Weican, ZHU Shulong, CAO Wen, CAO Bincai, GAO Xiang. High Accuracy On-Orbit Geometric Calibration of Linear Push-broom Cameras[J]. Geomatics and Information Science of Wuhan University, 2015, 40(10): 1392-1399,1413. DOI: 10.13203/j.whugis20140534
    [5]YIN Chuan, WANG Yanhui. Target Geometry Matching Threshold in Incremental Updatingof Road Networks Based on OSTU[J]. Geomatics and Information Science of Wuhan University, 2014, 39(9): 1061-1067. DOI: 10.13203/j.whugis20130575
    [6]YAN Li, JIANG Yun, WANG Jun. Building of Rigorous Geometric Processing Model Based onLine-of-Sight Vector of ZY-3 Imagery[J]. Geomatics and Information Science of Wuhan University, 2013, 38(12): 1451-1455.
    [7]LIU Liangming, YE Yuanxin, FAN Dengke, XU Qi. Study on Geometric Rectification for FY-2 S-VISSR Data[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 384-388.
    [8]WU Fang, ZHU Kunpeng. Geometric Accuracy Assessment of Linear Features' Simplification Algorithms[J]. Geomatics and Information Science of Wuhan University, 2008, 33(6): 600-603.
    [9]Li Deren, Wang Xinhua. Geometric Calibration of CCD Array Camera[J]. Geomatics and Information Science of Wuhan University, 1997, 22(4): 308-313,317.
    [10]Fan Yonghong. Geometric Rectification of SAR Image[J]. Geomatics and Information Science of Wuhan University, 1997, 22(1): 39-41.
  • Cited by

    Periodical cited type(32)

    1. 王蕾,何鑫,廖成. 基于知识库相似检索的自然资源调查监测图斑辅助辨识方法. 测绘通报. 2025(02): 137-142 .
    2. 张秀锦,张秀民. 基于轻便的node.js地图识别模型实现分析. 山东交通科技. 2024(02): 130-132 .
    3. 汤冻,奚晓轶,闫涛. 一种用于电视节目播出异态识别的人工智能模型训练方法. 电视技术. 2023(01): 61-65 .
    4. 梁生珺,于明鑫. 应用于无人机平台的轻量Transformer排水口检测框架. 电子技术与软件工程. 2023(01): 165-168 .
    5. 陶立清,黄国满,杨书成,王童童,盛辉军,范海涛. 一种利用卷积神经网络的干涉图去噪方法. 武汉大学学报(信息科学版). 2023(04): 559-567 .
    6. 桂志鹏,胡晓辉,刘欣婕,凌志鹏,姜屿涵,吴华意. 顾及地理语义的地图检索意图形式化表达与识别. 地球信息科学学报. 2023(06): 1186-1201 .
    7. 李从初,励臣儒,朱佳敏,姚浩立. 基于迁移学习和Xception网络的海雾能见度等级估测研究. 浙江气象. 2023(01): 23-28 .
    8. 田启川,吴施瑶,马英楠. 基于卷积神经网络的光学遥感影像分析综述. 计算机应用与软件. 2023(10): 1-9+45 .
    9. 樊翔宇,张聪,杨柳. 融合梅尔谱和循环残差的小样本音频分类模型. 计算机仿真. 2022(02): 195-202 .
    10. 金海峰,吴楠,张悠然. 智慧家庭中的人体动作识别研究综述. 软件导刊. 2022(04): 240-247 .
    11. 冯新扬,邵超. 跨卷积网络特征融合的SAR图像目标识别. 系统仿真学报. 2021(03): 554-561 .
    12. 任加新,刘万增,李志林,李然,翟曦. 利用卷积神经网络进行“问题地图”智能检测. 武汉大学学报(信息科学版). 2021(04): 570-577 .
    13. 王建华,冉煜琨. 适用于便携式设备的深度神经网络眼动跟踪. 计算机与现代化. 2021(08): 58-63 .
    14. 郑雯,沈琪浩,任佳. 基于Improved DR-Net算法的糖尿病视网膜病变识别与分级. 光学学报. 2021(22): 72-83 .
    15. 任福,侯宛玥. 面向机器阅读的地图名称注记类别识别方法. 武汉大学学报(信息科学版). 2020(02): 273-280 .
    16. 吴晓玲,黄金雪,何文海. 基于深度卷积神经网络的塑料垃圾分类研究. 塑料科技. 2020(04): 86-89 .
    17. 叶宇光. 基于深度残差网络的图像识别技术研究. 韶关学院学报. 2020(06): 18-22 .
    18. 王科举,廉小亲,陈彦铭,安飒,龚永罡. 基于深度学习的机械臂视觉系统. 信息技术与信息化. 2020(08): 203-208 .
    19. 侯东阳,武昊,陈军. 时空数据Web搜索的研究进展. 地理信息世界. 2020(04): 1-12+21 .
    20. 刘彩玲,岳荷荷. 基于(2D)~2-PCANet的种子图像识别. 计算机应用与软件. 2020(10): 232-238 .
    21. 谢万里,李宏志,周辉,尹绍武. 基于迁移学习与卷积神经网络的鱼濒死预警系统研究. 中国农机化学报. 2019(02): 186-192 .
    22. 宋益盛,林志杰. 基于迁移学习和数据增强技术的物种识别. 现代计算机. 2019(14): 57-63 .
    23. 李雄,文开福,钟小明,杨辉,秦德浩. 基于深度学习的人脸识别考勤管理系统开发. 实验室研究与探索. 2019(07): 115-118+123 .
    24. 李静,韩震,王文柳,崔艳荣. 基于OverFeat模型的长江口南汇潮滩植被分类. 生态科学. 2019(04): 135-141 .
    25. 江涛,王新杰. 基于卷积神经网络的高分二号影像林分类型分类. 北京林业大学学报. 2019(09): 20-29 .
    26. 呙鹏程,吴礼洋. 融合卷积特征与判别字典学习的低截获概率雷达信号识别. 兵工学报. 2019(09): 1881-1889 .
    27. 刘洋,冯全,王书志. 基于轻量级CNN的植物病害识别方法及移动端应用. 农业工程学报. 2019(17): 194-204 .
    28. 门计林,刘越岩,张斌,周繁. 多结构卷积神经网络特征级联的高分影像土地利用分类. 武汉大学学报(信息科学版). 2019(12): 1841-1848 .
    29. 赵波,廖坤,邓春宇,谈元鹏,曹生现. 基于卷积神经学习的光伏板积灰状态识别与分析. 中国电机工程学报. 2019(23): 6981-6989+7111 .
    30. 尹宗天,谢超逸,刘苏宜,刘新如. 低分辨率图像的细节还原. 软件. 2018(05): 199-202 .
    31. 宋俊芳. 基于BP神经网络的图像分割. 数字通信世界. 2018(03): 66+170 .
    32. 朱祺夫,赵俊三,陈磊士,李易. 基于深度学习的遥感影像城市建筑用地提取. 软件导刊. 2018(10): 18-21 .

    Other cited types(62)

Catalog

    Article views (1766) PDF downloads (304) Cited by(94)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return