At 5:38 am on the 24th June, 2017, a catastrophic rock avalanche destroyed the whole village of Xinmo, in Maoxian County, Sichuan Province, China. About 4.3 million m3 of rock detached from the crest of the mountain, gained momentum along a steep hillslope, entrained a large amount of pre-existing deposits, and hit the village at a velocity of 250 km/h. The impact produced a seismic shaking of ML = 2.3 magnitude. The sliding mass dammed the Songping gully with an accumulation body of 13 million m3. The avalanche buried 64 houses; 10 people were killed and 73 were reported missing. The event raised great concerns both in China and worldwide. Extensive field investigation, satellite remote sensing, UAV aerial photography, and seismic analysis allowed to identify the main kinematic features, the dynamic process, and the triggering mechanism of the event. With the aid of ground-based synthetic aperture radar monitoring, the hazard deriving from potential further instabilities in the source area has been assessed. The preliminary results suggest that the landslide was triggered by the failure of a rock mass, which had been already weakened by the Ms 7.5 Diexi earthquake in 1933. Several major earthquakes since then, and the long-term effect of gravity and rainfall, contributed to the mass failure. The high elevation, slope angle, and vegetation cover in the source area hinder geological field investigation and make hazard assessment difficult. Nonetheless, monitoring and prevention of similar collapses in mountainous areas must be carried out to protect human lives and infrastructures. To this aim, the integrated use of modern high-precision observation technologies is strongly encouraged.
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After the occurrence of the June 24th Xinmo Village landslide, the State Council, the Ministry of Land and Resources and various governmental departments at all levels in Sichuan Province immediately devoted their efforts into the emergency rescue operations, secondary hazard relief and geological surveying and monitoring. The preliminary results presented in this paper would not be achieved without a quick data acquisition, which has been facilitated greatly by the efforts of many staff members in different departments, to whom we express our sincere appreciation. We thank the Sichuan Provincial Surveying and Mapping Geographic Information Bureau, the High-resolution Earth Observation System Sichuan Data and Application Center, the Sichuan Shu Tong Geotechnical Engineering Company, the Beijing Digital Greenfield Technology Co. Ltd. and other units for the basic data collection. We express our gratitude to Prof. Yueping Yin from China Geological Environment Monitoring Institute, to Prof. Xiao Li from China Geological Survey, to Prof. Zhenhong Li from Newcastle University, to Prof. Lu Zhang and Prof. Mingsheng Liao from Wuhan University, to Prof. Qin Zhang from Chang’an University, to Prof. Shizhong Hong from Chengdu Earthquake Prevention and Hazard Mitigation Bureau, to Dr. Xinghui Huang from China Earthquake Networks Center to Prof. Yong Li from Chengdu University of Technology, to Prof. Chong Xu from Institute of Geology, China Earthquake Administration, for providing valuable information at the earliest time. We express sincere thanks to Dr. Yanan Jiang, Dr. Jing Ran, Xianxuan Xiao, Weiwei Zhan, Jing Ren, Yuanzhen Ju, Chen Guo, and other postgraduate students from Chengdu University of Technology for their hard work on the UAV aerial photography, GBSAR monitoring and other field work.
This research is financially supported by National Science Fund for Distinguished Young Scholars of China (Grant No. 41225011), the Funds for Creative Research Groups of China (Grant No. 41521002), National Science Fund for Outstanding Young Scholars of China (Grant No. 41622206), the AXA Fund.
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Fan, X., Xu, Q., Scaringi, G. et al. Failure mechanism and kinematics of the deadly June 24th 2017 Xinmo landslide, Maoxian, Sichuan, China. Landslides 14, 2129–2146 (2017). https://doi.org/10.1007/s10346-017-0907-7
- Rock avalanche
- Diexi earthquake
- Failure mechanism
- UAV and InSAR