三峡库区滑坡灾害分布广、数量多、规模大、危害严重,因此开展滑坡灾害易发性评价对该地的地灾防治与处理具有重要参考意义。本文提取了地层岩性、地质构造、坡度、坡向、曲率、斜坡形态、植被指数、水系等17个因子,选用逻辑回归模型、...三峡库区滑坡灾害分布广、数量多、规模大、危害严重,因此开展滑坡灾害易发性评价对该地的地灾防治与处理具有重要参考意义。本文提取了地层岩性、地质构造、坡度、坡向、曲率、斜坡形态、植被指数、水系等17个因子,选用逻辑回归模型、支持向量机模型、集成学习的梯度提升迭代决策树模型和深度学习中的长短期记忆神经网络与卷积神经网络耦合模型四个机器学习模型进行滑坡灾害易发性评价,选取最优评价模型,完成三峡库区的易发性分区评价,总结研究区易发性空间区划特性。对比四种模型的AUC(Area Under Curve)精度可以得出结论:GBDT模型(Gradient Boosting Decision Tree Model)的AUC精度相对较高,优于其他三个模型,更适合三峡库区的滑坡易发性研究。GBDT的易发性评价结果显示:研究区内极高易发性区域和高易发性区域主要集中于渝东、鄂西一带以及长江沿岸和支流沿岸。研究结果是对整个库区的易发性进行评价,可为后续库区的防灾减灾提供参考。展开更多
The dynamic monitoring of landslides in engineering geology has focused on the correlation among landslide stability,rainwater infiltration,and subsurface hydrogeology.However,the understanding of this complicated cor...The dynamic monitoring of landslides in engineering geology has focused on the correlation among landslide stability,rainwater infiltration,and subsurface hydrogeology.However,the understanding of this complicated correlation is still poor and inadequate.Thus,in this study,we investigated a typical landslide in southwestern China via time-lapse electrical resistivity tomography（TLERT） in November 2013 and August 2014.We studied landslide mechanisms based on the spatiotemporal characteristics of surface water infiltration and flow within the landslide body.Combined with borehole data,inverted resistivity models accurately defined the interface between Quaternary sediments and bedrock.Preferential flow pathways attributed to fracture zones and fissures were also delineated.In addition,we found that surface water permeates through these pathways into the slipping mass and drains away as fissure water in the fractured bedrock,probably causing the weakly weathered layer to gradually soften and erode,eventually leading to a landslide.Clearly,TLERT dynamic monitoring can provide precursory information of critical sliding and can be used in landslide stability analysis and prediction.展开更多
文摘三峡库区滑坡灾害分布广、数量多、规模大、危害严重,因此开展滑坡灾害易发性评价对该地的地灾防治与处理具有重要参考意义。本文提取了地层岩性、地质构造、坡度、坡向、曲率、斜坡形态、植被指数、水系等17个因子,选用逻辑回归模型、支持向量机模型、集成学习的梯度提升迭代决策树模型和深度学习中的长短期记忆神经网络与卷积神经网络耦合模型四个机器学习模型进行滑坡灾害易发性评价,选取最优评价模型,完成三峡库区的易发性分区评价,总结研究区易发性空间区划特性。对比四种模型的AUC(Area Under Curve)精度可以得出结论:GBDT模型(Gradient Boosting Decision Tree Model)的AUC精度相对较高,优于其他三个模型,更适合三峡库区的滑坡易发性研究。GBDT的易发性评价结果显示:研究区内极高易发性区域和高易发性区域主要集中于渝东、鄂西一带以及长江沿岸和支流沿岸。研究结果是对整个库区的易发性进行评价,可为后续库区的防灾减灾提供参考。
基金funded by the National Basic Research Program of China(973 Program)(No.2013CB733203)the National Natural Science Foundation of China(No.41474055)
文摘The dynamic monitoring of landslides in engineering geology has focused on the correlation among landslide stability,rainwater infiltration,and subsurface hydrogeology.However,the understanding of this complicated correlation is still poor and inadequate.Thus,in this study,we investigated a typical landslide in southwestern China via time-lapse electrical resistivity tomography（TLERT） in November 2013 and August 2014.We studied landslide mechanisms based on the spatiotemporal characteristics of surface water infiltration and flow within the landslide body.Combined with borehole data,inverted resistivity models accurately defined the interface between Quaternary sediments and bedrock.Preferential flow pathways attributed to fracture zones and fissures were also delineated.In addition,we found that surface water permeates through these pathways into the slipping mass and drains away as fissure water in the fractured bedrock,probably causing the weakly weathered layer to gradually soften and erode,eventually leading to a landslide.Clearly,TLERT dynamic monitoring can provide precursory information of critical sliding and can be used in landslide stability analysis and prediction.
