J4 ›› 2010, Vol. 40 ›› Issue (6): 1371-1379.

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Analysis and Prediction of the Spatial Distribution of Landslides Triggered by Wenchuan Earthquakes in Leiguzhen |Region

CHEN Xiao-li, ZHOU Ben-gang, RAN Hong-liu, WANG Ming-ming   

  1. Institute of Geology, China Earthquake Administration, Beijing 100029, China
  • Received:2010-03-08 Online:2010-11-26 Published:2010-11-26

Abstract:

It is useful to study the relationship between distribution of earthquake-induced landslides and influencing factors in order to predict potential hazard places in the future land using. The Wenchuan Earthquake in 8.0 Ms on May 12 triggered a lot of slopes to failure including landslides, rock falls and debris flows. The study area is about 180 km2 and located at Leiguzhen of Beichuan region where the slope-failure hazard is severe. The slope gradient, elevation and aspect are chosen as the influencing factors of the slope-failure distribution.  Their relationships with the distribution of slope failures are analyzed using CF and Rji. The research shows that:1)Landslides occurred more frequently at the elevation lower than 1 000 m. 2)With the increasing of the slope gradient, the occurrence probability of the slope failure becomes higher. The slope failures concentrate in the areas with the gradient greater than 30°. 3)More slope failures occurred at the slopes facing E, NE, and SE. Both predict methods based on factors susceptibility analysis and discrimination analysis, show consistently in the zoning results of high hazard and low hazard. About 66% slope failures occurred in the places classifying as high and second to high degree hazard zone based on the factors susceptibility analysis, and about 73% slope failures occurred in the places judged as unstable by discrimination analysis. The factors used in discrimination analysis method such as slope gradient, curvature and seismic acceleration have widely influence on occurring of earthquake-triggered landslides.

Key words: earthquake, landslides, certainty factor, area develop ratio, prediction

CLC Number: 

  • P642.2
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