Affiliation:
1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu 610059 China cdut.edu.cn
2. Disaster Prevention Research Institute Kyoto University Uji 6110011 Kyoto Japan kyoto-u.ac.jp
3. School of Civil and Hydraulic Engineering Xichang University Xichang Sichuan 615000 China xcc.edu.cn
Abstract
Abstract
Strong fluidized rock avalanches with various particle sizes are one of the typical disasters in the mountainous area of southwestern China. Statistical analysis on rock avalanche particles can help understand the traveling behavior, distance, and deposition of rock avalanche, which are useful for potential threat range prediction and disaster prevention. Rock avalanche deposit has more than one oblique slopes different in attitude. Image distortion will happen when particles on the slopes are projected to one plane. Area projection conversion coefficient Ki is applied in this paper to correct the distortion and improve the precision of the geometric dimension parameters of the rock avalanche particle. A rock avalanche (Aerzhai rock avalanche) with a volume of about 10×104 m3 took place in Longxi Township, Wenchuan County, China, on April 8, 2018. Being corrected by the coefficient Ki, the mean area and the standard deviation of the particles within different zones have been obtained, and we analyzed their changes in vertical and transverse directions. Here, we show that the mean particle area decreases by about 61.1% from the source to deposition region and area standard deviation decreases by about 37.4%. The deposition region shows a higher proportion of small crashed particles than the source region. The proportion of small particles gets larger with the longitudinal motion distance and lateral distance. For big particles’ significant potential energy-driving ability and inertia advantages, the longitudinal motion routes can be maintained, and inertia decreases as the particles get smaller, while small particles offset from the center area laterally. These statistical characters are easy to be ignored if projection correction is not performed.
Funder
National Key R&D Program of China
Postdoctoral Foundation of China
National Natural Science Foundation of China
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