王欣, 方成勇, 唐小川, 戴岚欣, 范宣梅, 许强. 泸定Ms 6.8地震诱发滑坡应急评价研究[J]. 武汉大学学报 ( 信息科学版), 2023, 48(1): 25-35. DOI: 10.13203/j.whugis20220586
引用本文: 王欣, 方成勇, 唐小川, 戴岚欣, 范宣梅, 许强. 泸定Ms 6.8地震诱发滑坡应急评价研究[J]. 武汉大学学报 ( 信息科学版), 2023, 48(1): 25-35. DOI: 10.13203/j.whugis20220586
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

泸定Ms 6.8地震诱发滑坡应急评价研究

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

  • 摘要: 2022-09-05,四川省甘孜州泸定县发生Ms 6.8地震。地震在山区诱发了大量的地质灾害,造成了严重的人员伤亡。快速准确地获取地震诱发地质灾害的空间分布范围对震后应急决策和救援抢险至关重要。基于全球同震滑坡数据库与深度学习算法,构建了地震诱发滑坡空间分布概率近实时预测模型,在震后2 h内获取了泸定地震诱发地质灾害的预测结果。通过震后无人机与卫星遥感影像,采用机器学习与深度学习算法实现了震后大范围地质灾害的智能识别,共解译地震诱发滑坡3 633处,总面积13.78 km2。利用遥感解译的泸定地震滑坡数据,对地震诱发地质灾害预测模型进行了优化,获得了震区范围更广、准确性更高的同震滑坡预测结果。结果表明,同震滑坡预测模型能够快速获取震后地质灾害的空间分布情况,填补震后遥感影像获取前的空窗期,为灾后应急救援提供支撑;基于无人机与卫星遥感影像的智能识别技术是快速获取大范围地质灾害信息的有效手段。所取得的研究成果在泸定地震震后应急救援工作中发挥了重要作用。

     

    Abstract:
      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.

     

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