摘要
针对隐伏断层地震诱发滑坡无明显空间分布规律导致滑坡易发性分区难度较大的问题,提出考虑同震地表变形因子的人工神经网络地震滑坡易发性评价方法.以2004年日本新泻中越地震为例,除选取地层岩性、高程、坡度、坡向、地表曲率、地震加速度峰值、距道路的距离等常规地震滑坡影响因子外,新增同震地表变形作为影响因子,利用地理信息系统(GIS)平台通过神经网络法进行地震滑坡易发性评价.结果表明,基于GIS的人工神经网络法对地震诱发滑坡易发性评价有较高的精度.同震地表变形对预测准确度有一定贡献,优于坡向、高程以及距道路的距离等常规影响因子.
The artificial neural network(ANN)method containing horizontal ground deformation factor,was proposed to solve the difficulty in susceptibility assessment of landslides triggered by buried fault,due to the unapparent relationship between buried fault earthquake and landslide occurrence.The landslide susceptibility assessment was performed in geographic information system(GIS)by taking the case of 2004 Mid-Niigata earthquake in Japan.Lithology,elevation,slope,aspect,curvature,earthquake peak acceleration,distance from roads and horizontal ground deformation were selected as influential factors.Results show that ANN method has a good accuracy in earthquake-induced landslide susceptibility assessment.Ground deformation was proved to have greater contribution to the assessment model accuracy than some ordinary factors such as aspect,elevation and the distance from roads.
引文
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