泸石高速公路沿线历史地震诱发滑坡遥感调查及发育分布规律

Remote sensing investigation and development distribution of historical earthquake-induced landslides along Lushi Expressway

泸(定)石(棉)高速公路沿线断裂构造发育,地震频发,造成区域内历史地震诱发的滑坡数量多且规模大,然而这些地震滑坡体表面生长大量植被,传统调查难以高效查明地震滑坡分布位置及发育规律。为了减少地震滑坡对公路建设带来的安全隐患,首先以高精度机载LiDAR数据对地震滑坡进行了识别,并通过野外复核验证识别的准确性;其次利用高精度机载LiDAR影像对地震滑坡的变形特征进行了分析;最后,综合考虑地形、地质、地震三大因素(6个因子),对地震滑坡的空间分布特征进行了分析。结果表明:利用机载LiDAR技术能够有效地发现植被层下的地震滑坡,在泸石高速公路沿线共识别出地震滑坡23处,野外复核验证精度达100%;通过对控制地震滑坡空间分布的6个因子的分析,得出与断裂构造和地震相关性最高。研究结果为植被茂密山区高速公路的滑坡识别调查提供了一定的参考并为泸石高速公路滑坡灾害防治与风险评价提供了数据支撑。

[Objective] The fault structure along the Lu(ding) Shi(main) highway has developed,and earthquakes are frequent,resulting in a large number and large scale of historical earthquake-induced landslides in the region.However,a large amount of vegetation grows on the surface of these seismic landslides,and it is difficult for traditional surveys to efficiently identify the distribution location and development law of earthquake landslides.[Methods] To reduce the safety hazards caused by earthquake landslides during highway construction,this paper first identifies earthquake landslides with high-precision airborne LiDAR data and verifies the accuracy of identification through field review.Second,high-precision airborne LiDAR images are used to analyse the deformation characteristics of seismic landslides.Finally,the spatial distribution characteristics of the seismic landslides are analysed by comprehensively considering the three major factors(6 factors) of topography,geology and earthquakes.[Results] The results show that airborne LiDAR technology can be used to effectively detect seismic landslides under vegetation layers.A total of 23 seismic landslides are identified along the Lushi Expressway Common Road,and the accuracy of field review and verification is 100%.By analysing the six factors controlling the spatial distribution of seismic landslides,it is concluded that the correlations with fault structure and earthquakes are the highest.[Conclusion]The research results provide a reference for landslide identification and investigations of expressways with dense vegetation and provide data support for landslide disaster prevention and risk assessment of the Lushi Expressway.