Mapping landslide susceptibility with frequency ratio,statistical index,and weights of evidence models: a case study in northern Iran

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.     

Application of frequency ratio,statistical index,and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.     

A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models

The purpose of this study is to produce landslide susceptibility map of a landslide-prone area (Daguan County, China) by evidential belief function (EBF) model and weights of evidence (WoE) model to compare the results obtained. For this purpose, a landslide inventory map was constructed mainly based on earlier reports and aerial photographs, as well as, by carrying out field surveys. A total of 194 landslides were mapped. Then, the landslide inventory was randomly split into a training dataset; 70% (136 landslides) for training the models and the remaining 30% (58 landslides) was used for validation purpose. Then, a total number of 14 conditioning factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), and topographic wetness index (TWI) were used in the analysis. Subsequently, landslide susceptibility maps were produced using the EBF and WoE models. Finally, the validation of landslide susceptibility map was accomplished with the area under the curve (AUC) method. The success rate curve showed that the area under the curve for EBF and WoE models were of 80.19% and 80.75% accuracy, respectively. Similarly, the validation result showed that the susceptibility map using EBF model has the prediction accuracy of 80.09%, while for WoE model, it was 79.79%. The results of this study showed that both landslide susceptibility maps obtained were successful and would be useful for regional spatial planning as well as for land cover planning.     

基于确定性系数组合模型的区域滑坡敏感性评价

本文以四川茂县叠溪镇到石大关乡为研究区,根据野外资料并结合研究区的基本情况,选取了坡度、剖面曲率、起伏度、坡向、距河流距离、高程、地层、距断层距离、土地类型、植被覆盖度10个影响因子。以GIS技术作为操作平台,采用确定性系数+层次分析法（CF-AHP）、确定性系数+逻辑回归方法（CF-LR）和确定性系数+神经网络的多层感知器方法（CF-MLP）3种方法对研究区滑坡灾害敏感性进行评价,将该区域滑坡灾害划分为极低、低、中、高敏感区4类,并通过受试者工作特征曲线（ROC）检验模型的效果。CF-AHP、CF-LR和CF-MLP组合模型ROC曲线的线下面积（AUC）分别为0.850、0.884和0.867,CF-LR组合模型效果最好。CF-LR组合模型中,高、中、低和极低敏感区面积分别占研究区总面积的11.3%、25.1%、22.5%和41.1%。研究结果表明,高敏感区主要集中在主要水系周围与断层集中区域,计算出的敏感性分区结果与研究区实际情况接近,能够在地质灾害风险评价中起到重要参考作用。     

Landslide hazard mapping in the Constantine city,Northeast Algeria using frequency ratio,weighting factor,logistic regression,weights of evidence,and analytical hierarchy process methods

Landslides constitute the most widespread and damaging natural hazards in the Constantine city. They represent a significant constraint to development and urban planning. In order to reduce the risk related to potential landslide, there is a need to develop a comprehensive landslide hazard map (LHM) of the area for an efficient disaster management and for planning development activities. The purpose of this research is to prepare and compare the LHMs of the Constantine city, by applying frequency ratio (FR), weighting factor (Wf), logistic regression (LR), weights of evidence (WOE), and analytical hierarchy process (AHP) methods used in a framework of the geographical information system (GIS). Firstly, a landslide inventory map has been prepared based on the interpretation of aerial photographs, high resolution satellite images, fieldwork, and available literature. Secondly, eight landslide-conditioning factors such as lithology, slope, exposure, rainfall, land use, distance to drainage, distance to road, and distance to fault have been considered to establish LHMs using the FR, Wf, LR, WOE, and AHP models in GIS. For verification, the obtained LHMs have been validated comparing the LHMs with the known landslide locations using the receiver operating characteristics curves (ROC). The validated results indicate that the FR method provides more accurate prediction (86.59 %) of LHMs than the WOE (82.38 %), AHP (77.86 %), Wf (77.58 %), and LR (70.45 %) models. On the other hand, the obtained results showed that all the used models in this study provided a good accuracy in predicting landslide hazard in Constantine city. The established maps can be used as useful tools for risk prevention and land use planning in the Constantine region.     

A Combined Weight of Evidence and Logistic Regression Method for Susceptibility Mapping of Earthquake-induced Landslides: A Case Study of the April 20, 2013 Lushan Earthquake,China

The Ms 7.0 Lushan earthquake triggered a huge number of landslides. Landslide susceptibility mapping is of great importance. Weight of Evidence (WoE) and Logistic Regression (LR) methods have been widely used for LSM (Landslide Susceptibility Mapping). However, limitations still exist. WoE is capable of assessing the influence of different classes of each factor, but neglects the correlation between factors. LR is able to analyze the relationship among the factors while it is not capable of evaluating the influence of different classes. This paper proposes a combined method of LR and WoE for LSM, taking advantage of their individual merits and overcoming their limitations. An inventory of 1289 landslides was used: 70% were random-selected for training and the remaining for validation. 11 landslide condition factors were employed in the model and the result was validated using Receiver Operating Characteristic (ROC) curve. The results showed that the LR-WoE model had a better accuracy than the LR model, producing an area below the curve with values of 0.802 success and 0.791 predictive, higher than that of the LR model (0.715 success and 0.722 predictive). It is therefore concluded that the combined method of WoE and LR can provide a promising level of accuracy for earthquake-induced landslide susceptibility mapping.     

白龙江流域崩滑灾害孕灾因子聚类分区与道路工程扰动效应分析

为探究白龙江流域内崩滑灾害的孕灾因子,以及道路工程活动对地质环境的扰动效应,本文通过野外考察和资料收集获取了2730个崩滑灾害点,使用GIS空间分析和遥感解译提取了地形地貌、地质构造、人类活动等方面的多个孕灾因子。基于信息量模型,分析了各因子的孕灾作用;采用空间约束多元聚类分析方法,依据各因子的特征和地理位置将崩滑灾害聚类,并利用随机森林算法获取了各类集群中各因子的重要性,分析了道路工程对崩滑灾害的扰动效应。结果表明,崩滑事件受海拔、坡度、坡向、降雨、岩性、断层、植被覆盖度、土地利用和道路工程扰动等多因子的共同作用。崩滑灾害可以被聚类为4个集群,不同集群区域内各孕灾因子对崩滑事件所起的重要性不同:多年平均降雨量对白龙江中游的A类集群重要性最高;海拔对白龙江上游的B类集群重要性最高;距断层距离对宕昌东北向的C类集群重要性最高;距公路距离对白龙江下游D类集群的重要性最高。道路工程的扰动效应表现为在地质环境脆弱的区域重要性较低,而在地质环境条件较好的区域重要性较高。     

基于离心机模型试验的甘肃江顶崖古滑坡复活机理研究

位于白龙江断裂带的甘肃舟曲江顶崖古滑坡规模巨大,受断裂活动、降雨入渗与河流侵蚀和人类工程活动等因素影响,多次发生复活-堵塞白龙江灾害事件,造成极大危害。为研究江顶崖古滑坡的复活机理,本文在野外地质调查的基础上,重点开展了滑体在含水率为10%、15%和20%条件下的离心机模型试验。研究表明:在滑体含水率为10%情况下,试验结束后仅在坡体中后部产生少量裂缝,但滑坡体整体还处于稳定状态; 而在滑体含水率为15%和20%情况下,滑坡均发生了破坏,在滑体含水率分别为15%、20%情况下坡体失稳所需离心加速度分别为100g和50g。试验测试分析表明,江顶崖古滑坡为推移式滑坡,其变形先从坡体中后部开始,坡体中后部产生裂缝,随后裂缝逐渐向前缘扩展,最终裂缝贯通造成滑坡滑动破坏。滑坡体的变形过程主要分为3个阶段: ①变形启动阶段(裂缝开始形成阶段); ②变形加速阶段(裂缝加速发展阶段); ③失稳阶段。通过离心模拟试验,结合野外调查分析,认为江顶崖古滑坡复活的因素主要受降雨和孔隙水压力的影响,是受前缘河流侵蚀牵引、降雨入渗造成滑坡中后部推移的耦合滑动。     

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.     

基于斜坡单元的山区城镇滑坡灾害易发性评价：以康定为例

山区地质灾害易发性评价对城镇地质灾害风险管理具有重要意义。本文以康定市为例,以斜坡单元为最小评价单元,选取高程、坡度、坡向、曲率、工程地质岩组、距道路距离、距断裂距离、距水系距离和斜坡结构等9个滑坡影响因子,根据各因子滑坡面积比曲线与证据权值曲线的突变点,划分滑坡影响因子二级状态,并对各影响因子进行相关性分析,剔除相关性较高的距道路距离因子,在此基础上,采用证据权模型进行滑坡易发性评价。对已有治理工程的斜坡单元,本文尝试利用折减系数法对其易发性进行进一步评价。结合现场调查,将研究区滑坡易发性程度划分为：极高易发、高易发、中等易发、低易发。评价结果表明,自然工况下极高易发区主要位于康定市炉城镇以及研究区北侧二道桥村一带,高易发区主要位于雅拉河、折多河与瓦斯沟河谷两侧,对治理工程所在的斜坡单元进行折减后,极高易发区面积由11.21%降至8.42%,滑坡比率由4.03降低至2.3,研究结果符合实际情况,模型精度达77.8%。评价结果较好地反映了康定市区的滑坡易发性分布情况,可为城镇精细化评价提供一定的参考依据。     

逻辑回归与支持向量机模型在滑坡敏感性评价中的应用

白龙江流域是我国滑坡泥石流灾害四大高发区之一,进行该区域滑坡敏感性评价,能够为决策者在灾害管理和设施建设规划方面提供帮助,对区域防灾减灾具有重要指导意义。本研究采用边坡单元为基本研究单元,在野外调查及前人研究基础上,选择控制该区域滑坡发育的19个要素作为影响因子; 经过主成分分析和独立性检验得到该区域对滑坡形成贡献最大的6个因子:高程、坡度、坡向、岩性、断裂距离和人口密度; 分别使用二元逻辑回归模型（LR）和支持向量机模型（SVM）对该区域进行滑坡敏感性评价; 最后,采用ROC曲线对模型精度进行验证。研究结果表明,两模型各能将38.76%、14.48%、9.40%、11.28%、26.07%和13.49%、21.61%、8.17%、26.70%、30.04%的边坡单元分别预测为极高危险区、高危险区、中度危险区、低危险区和极低危险区; 精度验证结果表明两种模型均能有效地进行该区域滑坡敏感性评价,并且支持向量机模型具有更好的分类能力、预测精度和稳定性。     

基于人工神经网络模型的福建南平市滑坡危险性评价

滑坡灾害持续影响着人民生命财产安全和地区社会经济可持续发展,滑坡危险性评价能够为防灾减灾和区域规划提供有效的理论依据。以福建省南平市为研究区,区内1711个历史滑坡灾害点,选择高程、坡度、坡向、曲率、地质岩性、土壤类型、降雨、水系、土地利用类型、公路和铁路共11个影响因子构成基本评价体系。使用Spearman相关系数对各因子进行共线性分析。基于1711个滑坡样本和1711个随机选取的非滑坡样本数据,利用人工神经网络模型对研究区进行了滑坡危险性评价,并利用混淆矩阵和接收者操作特征曲线（ROC）对模型进行验证。结果表明:混淆矩阵精度84.91%,ROC曲线下面积AUC值0.93,说明模型具有较高精度和预测率。使用自然间断法将滑坡危险性分为5个等级,结果表明研究区内危险性最高地区位于延平区和浦城县,顺昌县和松溪县次之,其余地区多为低危险区和较低危险区。研究结果可为当地区域规划和防灾减灾工程提供一定的理论依据和科学指导。     

基于GIS的白龙江流域甘肃段滑坡易发性评价

在甘肃省白龙江流域地质灾害资料收集及现场调查的基础上, 统计分析了该区滑坡发育与地层岩性、坡度、坡向、高程、断裂、植被等因素之间的关系, 建立了白龙江流域滑坡易发性评价指标体系。采用基于GIS的层次分析法评价模型, 完成了滑坡易发性分区评价, 将研究区滑坡按易发程度划分为高易发区、中易发区、低易发区和极低易发区, 其中, 高易发区占研究区总面积的13.59%, 主要分布在断裂带、白龙江两侧以及软弱岩土体分布的区域; 中易发区占27.85%;主要分布在白龙江支流以及主要道路两侧的一定范围内; 低易发区占33.09%, 主要分布在海拔相对较高、植被覆盖度较高、基本上无断裂带通过的区域; 其余区域为极低易发区, 占25.46%。对比分析显示评价结果与实际滑坡发育情况吻合, 可以较好地反映区内滑坡灾害发育的总体特征。      

Application of logistic regression model for slope instability prediction in Cuyahoga River Watershed,Ohio, USA

High incidences of slope movement are observed throughout Cuyahoga River watershed in northeast Ohio, USA. The major type of slope failure involves rotational movement in steep stream walls where erosion of the banks creates over-steepened slopes. The occurrence of landslides in the area depends on a complex interaction of natural as well as human induced factors, including: rock and soil strength, slope geometry, permeability, precipitation, presence of old landslides, proximity to streams and flood-prone areas, land use patterns, excavation of lower slopes and/or increasing the load on upper slopes, alteration of surface and subsurface drainage. These factors were used to evaluate the landslide-induced hazard in Cuyahoga River watershed using logistic regression analysis, and a landslide susceptibility map was produced in ArcGIS. The map classified land into four categories of landslide susceptibility: low, moderate, high, and very high. The susceptibility map was validated using known landslide locations within the watershed area. The landslide susceptibility map produced by the logistic regression model can be efficiently used to monitor potential landslide-related problems, and, in turn, can help to reduce hazards associated with landslides.     

Susceptibility evaluation and mapping of China’s landslides based on multi-source data

Landslides are occurring more frequently in China under the conditions of extreme rainfall and changing climate, according to News reports. Landslide hazard assessment remains an international focus on disaster prevention and mitigation, and it is an important step for compiling and quantitatively characterizing landslide damages. This paper collected and analyzed the historical landslide events data of the past 60 years in China. Validated by the frequencies and distributions of landslides, nine key factors (lithology, convexity, slope gradient, slope aspect, elevation, soil property, vegetation coverage, flow, and fracture) are selected to construct landslide susceptibility (LS) empirical models by back-propagation artificial neural network method. By integrating landslide empirical models with surface multi-source geospatial and remote sensing data, this paper further performs a large-scale LS assessment throughout China. The resulting landslide hazard assessment map of China clearly illustrates the hot spots of the high landslide potential areas, mostly concentrated in the southwest. The study implements a complete framework of multi-source data collecting, processing, modeling, and synthesizing that fulfills the assessment of LS and provides a theoretical basis and practical guide for predicting and mitigating landslide disasters potentially throughout China.

     

Landslide susceptibility assessment using object mapping units,decision tree,and support vector machine models in the Three Gorges of China

Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models.     

Comparison of landslide susceptibility mapping methodologies for Koyulhisar,Turkey: conditional probability,logistic regression,artificial neural networks,and support vector machine

This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study.     

基于频率比与集成学习的滑坡易发性评价——以金沙江上游巴塘—德格河段为例

金沙江上游巴塘—德格河段地处青藏高原东部,该区地质、地形、地貌极其复杂,滑坡灾害最为发育,开展区域滑坡易发性评价对防灾减灾工作有着重要的意义。本文以金沙江上游巴塘—德格河段为研究区,在滑坡编录与野外实际调查的基础上,通过对滑坡分布规律和影响因素分析,选取高程、坡度、坡向、曲率、地形起伏度、地表切割度、地表粗糙度、地层岩性、断层、水系和道路等11个影响因子,构建了滑坡易发性评价指标体系。利用皮尔森系数去除高相关性影响因子,运用频率比方法定量分析各个因子与滑坡发育的关系。通过频率比模型选取非滑坡样本,采用集成学习算法模型进行滑坡易发性评价,根据易发性指数将研究区划分为极高易发区、高易发区、中易发区、低易发区及极低易发区5个等级。由滑坡易发性分区图和ROC曲线表明,高和极高易发区主要沿金沙江沿岸和沟谷分布,随机森林模型的成功率曲线下面积AUC=0.84,历史滑坡灾害位于高-极高易发区的灾害数占总滑坡数的84.8%,梯度提升树模型的成功率曲线下面积AUC=0.79,历史滑坡灾害位于高-极高易发区灾害数占总滑坡数的79.3%。由AUC值和历史灾害的分布可知,随机森林模型比梯度提升树模型在本研究区滑坡易发性评价中有着更好的评价精度和更高的预测能力。     

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.     

舟曲江顶崖滑坡的早期判识及风险评估研究

如何提前判识滑坡变形并对其进行早期风险评估已成为地质灾害防治领域的研究热点。文章以舟曲白龙江流域江顶崖堆积层滑坡为反分析案例,进行了滑坡变形早期判识及风险评估综合研究,提出了小基线集雷达干涉（SBAS-InSAR）技术解译分析、地质-力学联合分析、动力过程数值模拟分析三者相结合的滑坡变形早期判识与风险评估全流程分析模式。基于SBAS-InSAR技术解译能够准确地判识江顶崖滑坡的分布范围及早期形变特征,江顶崖滑坡的变形破坏模式为牵引式,滑坡体长度约680 m,宽度约210 m。基于早期识别信息,地质-力学联合分析表明:江顶崖滑坡为典型的老堆积层滑坡,前缘局部变形,破坏模式为牵引式,滑坡体平均厚度约35 m,滑床整体坡度较缓,失稳后运移速度不大。选取符合江顶崖滑坡体滑移摩擦特征的库伦摩擦模型,基于深度积分连续介质方程,分析计算滑坡体的动力学过程,结果表明:滑坡体滑移速度不大,最大值约为2.2 m/s,运动方式表现为推挤白龙江河道,堵江可能性较小,并且江顶崖滑坡体前缘错动完成后,该滑坡体滑移速度从前缘到后缘快速降为0,表现为牵引式运动特征。本次分析结果与实际相符,吻合度较高,采取的综合分析方法及研究模式可用于舟曲白龙江沿岸类似滑坡的早期判识及风险评估。