Geospatial susceptibility mapping of earthquake-induced landslides in Nuweiba area,Gulf of Aqaba,Egypt

Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.     

Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir

In order to reach the designated final water level of 175 m, there were three impoundment stages in the Three Gorges Reservoir, with water levels of 135 m, 156 m and 175 m. Baishuihe landslide in the Reservoir was chosen to analyze its displacement characteristics and displacement variability at the different stages. Based on monitoring data, the landslide displacement was mainly influenced by rainfall and drawdown of the reservoir water level. However, the magnitude of the rise and drawdown of the water level after the reservoir water level reached 175 m did not accelerate landslide displacement. The prediction of landslide displacement for active landslides is very important for landslide risk management. The time series of cumulative displacement was divided into a trend term and a periodic term using the Hodrick-Prescott(HP) filter method. The polynomial model was used to predict the trend term. The extreme learning machine(ELM) and least squares support vector machine(LS-SVM) were chosen to predict theperiodic term. In the prediction model for the periodic term, input variables based on the effects of rainfall and reservoir water level in landslide displacement were selected using grey relational analysis. Based on the results, the prediction precision of ELM is better than that of LS-SVM for predicting landslide displacement. The method for predicting landslide displacement could be applied by relevant authorities in making landslide emergency plans in the future.     

基于多源数据的谭家湾滑坡变形机制及稳定性评价

针对2016年5月发生于秭归县西北部的谭家湾滑坡,结合卫星遥感影像、现场勘查资料以及历史资料等多源数据,初步明确了滑坡的影响区域、特征及发生时序;综合采用钻探、槽探、物探等手段,开展室内外相关实验,明确了滑坡区的地层特性以及岩土体物理力学性质指标,通过分析该区裂缝位移及GPS数据,对该边坡的变形机制进行了探讨,并对该区稳定性进行了评价。结果表明:①谭家湾滑坡属于不规则"圈椅形"中型松散层的水库下降型滑坡,滑坡区的地表形态、地质构造及岩性等因素决定了滑坡的形成和发育,库水位和降雨的共同作用激励了滑坡的变形;②滑坡根据时序共分为3级滑体,总体呈现多次、多层、相互影响的演化特点,第三级滑体具有牵引式特征;③滑坡体内地下水位随库水位下降而下降,但下降速率缓于库水位,随之坡体内水力梯度和渗透力显著变大,此时碰到强降雨,将会导致坡体地下水赋存,岩土体软化,加剧滑坡变形,须施加必要的防护措施。④稳定性分析表明,该滑坡现处于临界稳定状态,一旦发生降雨和库水位变化,局部段可能发生失稳滑动。      

Hazard and population vulnerability analysis: a step towards landslide risk assessment

In this paper, an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlán, Puebla, Mexico, is presented. In order to estimate landslide hazard, the susceptibility,magnitude(area-velocity ratio) and landslide frequency of the area of interest were produced based on information derived from a geomorphological landslide inventory; the latter was generated by using very high resolution satellite stereo pairs along with information derived from other sources(Google Earth,aerial photographs and historical information).Estimations of landslide susceptibility were determined by combining four statistical techniques:(i) logistic regression,(ii) quadratic discriminant analysis,(iii) linear discriminant analysis, and(iv)neuronal networks. A Digital Elevation Model(DEM)of 10 m spatial resolution was used to extract the slope angle, aspect, curvature, elevation and relief.These factors, in addition to land cover, lithology anddistance to faults, were used as explanatory variables for the susceptibility models. Additionally, a Poisson model was used to estimate landslide temporal frequency, at the same time as landslide magnitude was obtained by using the relationship between landslide area and the velocity of movements. Then,due to the complexity of evaluating it, vulnerability of population was analysed by applying the Spatial Approach to Vulnerability Assessment(SAVE) model which considered levels of exposure, sensitivity and lack of resilience. Results were expressed on maps on which different spatial patterns of levels of landslide hazard and vulnerability were found for the inhabited areas. It is noteworthy that the lack of optimal methodologies to estimate and quantify vulnerability is more notorious than that of hazard assessments.Consequently, levels of uncertainty linked to landslide risk assessment remain a challenge to be addressed.     

Landslide inventory and susceptibility assessment using multiple statistical approaches along the Karakoram highway,northern Pakistan

China-Pakistan Economic Corridor(CPEC)is a framework of regional connectivity,which will not only benefit China and Pakistan but will have positive impact on Iran,Afghanistan,India,Central Asian Republic,and the region.The surrounding area in CPEC is prone to frequent disruption by geological hazards mainly landslides in northern Pakistan.Comprehensive landslide inventory and susceptibility assessment are rarely available to utilize for landslide mitigation strategies.This study aims to utilize the high-resolution satellite images to develop a comprehensive landslide inventory and subsequently develop landslide susceptibility maps using multiple techniques.The very high-resolution(VHR)satellite images are utilized to develop a landslide inventory using the visual image classification techniques,historic records and field observations.A total of 1632 landslides are mapped in the area.Four statistical models i.e.,frequency ratio,artificial neural network,weights of evidence and logistic regression were used for landslide susceptibility modeling by comparing the landslide inventory with the topographic parameters,geological features,drainage and road network.The developed landslides susceptibility maps were verified using the area under curve(AUC)method.The prediction power of the model was assessed by the prediction rate curve.The success rate curves show 93%,92.8%,92.7%and 87.4%accuracy of susceptibility maps for frequency ratio,artificial neural network,weights of evidence and logistic regression,respectively.The developed landslide inventory and susceptibility maps can be used for land use planning and landslide mitigation strategies.     

Dynamic prediction of landslide displacement using singular spectrum analysis and stack long short-term memory network

An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis(SSA) and stack long short-term memory(SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine(SVM) and auto regressive integrated moving average(ARIMA). The mean relative error(MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error(RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides.     

金沙江结合带巴塘段滑坡群InSAR探测识别与形变特征

金沙江结合带结构破碎,软弱岩层发育,流域性特大高位地质灾害频繁发生.针对该区域开展大范围滑坡调查与监测研究,对减灾防灾具有重要意义.以金沙江结合带巴塘段为试验区,采用堆叠InSAR技术分别利用升轨、降轨Sentinel-1 A卫星数据对该区域滑坡隐患开展了调查研究.在此基础上,以中心绒乡滑坡群为重点研究区,利用多维小基...     

Three-dimensional stability of landslides based on local safety factor

Unlike the limit equilibrium method (LEM), with which only the global safety factor of the landslide can be calculated, a local safety factor (LSF) method is proposed to evaluate the stability of different sections of a landslide in this paper. Based on three-dimensional (3D) numerical simulation results, the local safety factor is defined as the ratio of the shear strength of the soil at an element on the slip zone to the shear stress parallel to the sliding direction at that element. The global safety factor of the landslide is defined as the weighted average of all local safety factors based on the area of the slip surface. Some example analyses show that the results computed by the LSF method agree well with those calculated by the General Limit Equilibrium (GLE) method in two-dimensional (2D) models and the distribution of the LSF in the 3D slip zone is consistent with that indicated by the observed deformation pattern of an actual landslide in China.     

顾及降雨及库水位因素的滑坡时滞分析与预测——以三峡库区新铺滑坡为例

降雨及库水位涨落是引起库岸滑坡形变失稳的主要诱发因素,但滑坡位移速率对此类诱发因素的响应具有一定的滞后性,影响人类对滑坡所处运动状态的判断与预测.针对常规预测模型中未考虑时滞效应的问题,利用三峡库区新铺滑坡的GNSS位移监测数据、奉节气象站降雨数据以及三峡库区库水位涨落数据,通过对监测区内9个GNSS监测点的位移速率序...     

A WD-GA-LSSVM model for rainfall-triggered landslide displacement prediction

This paper proposes a WD-GA-LSSVM model for predicting the displacement of a deepseated landslide triggered by seasonal rainfall,in which wavelet denoising(WD)is used in displacement time series of landslide to eliminate the GPS observation noise in the original data,and genetic algorithm(GA)is applied to obtain optimal parameters of least squares support vector machines(LSSVM)model.The model is first trained and then evaluated by using data from a gentle dipping(~2°-5°)landslide triggered by seasonal rainfall in the southwest of China.Performance comparisons of WD-GA-LSSVM model with Back Propagation Neural Network(BPNN)model and LSSVM are presented,individually.The results indicate that the adoption of WD-GA-LSSVM model significantly improves the robustness and accuracy of the displacement prediction and it provides a powerful technique for predicting the displacement of a rainfall-triggered landslide.     

青海门源6.4级地震兰新高铁沿线强震数据分析

基于2016-01-21青海门源6.4级地震,通过分析高铁沿线台站强震数据的峰值加速度、峰值速度和仪器烈度值表明,将实时仪器烈度值作为高铁地震监控系统的报警参数受场地条件影响较小。利用沿线台站近高铁和远高铁监测点的谱比值来解释局部复杂场地条件对峰值加速度的影响,通过比较台站计算与设计的反应谱来预测高铁地震台站附近的桥梁震害情况。     

Predicting landslide scenes under potential earthquake scenarios in the Xianshuihe fault zone,Southwest China

Earthquake-induced landslides can seriously aggravate the earthquake’s destructive consequences and have caused widespread concern in recent decades. The Xianshuihe fault is a large active left-lateral strike-slip fault in the southeast margin of Qinghai-Tibet Plateau, Southwest China, where the frequent strong earthquakes have brought abundant geo-hazards. This study focuses mainly on exploring and predicting the landslide scenes induced by the potential earthquakes. Firstly, the sophisticated Newmark model is improved through landslide cases induced by the Ms7.9 Luhuo earthquake in 1973 to adapt the field seismotectonics of the Xianshuihe fault zone. Then, it is used to predict the landslide scenes under one speculated potential earthquake scenario with the similar focal mechanism with the Luhuo earthquake. The preliminary results show that the slope displacement resulted from Newmark model can reflect spatial distribution characteristics of earthquake-induced landslides. The predicted potential earthquake-induced landslide scenes present an obvious extending trend along the Xianshuihe fault. The landslide hazard is greater in the northeast regions than southwest regions of the Xianshuihe fault, where there are more complex topographic conditions. The study procedure will be a helpful demonstration for exploration and prediction of landslide scenes under potential earthquakes in the regions with high seismic activity.     

基于多期无人机影像的滑坡地表竖向变形测量模型试验与工程应用

滑坡地质灾害严重威胁人民生命财产安全,滑坡地表竖向变形测量属于滑坡监测与预警的重要组成部分。近年来,国内外学者开始尝试使用多期无人机影像开展滑坡地表变形监测,然而基于多期无人机影像的滑坡地表竖向变形测量精度研究却相对较少。首先通过大量室外模型试验,对CloudCompare、Global Mapper和PolyWorks三款软件地表竖向变形识别的结果进行了对比和研究;在此基础上,定量分析了三款软件地表竖向变形测量的精度。研究结果表明:当无人机影像分辨率优于3.0cm/像素,三款软件均能识别5.0cm及以上的地表竖向变形;在不同地表竖向变形工况下,Global Mapper地表竖向变形测量结果最为精确与稳定,竖向变形测量的中误差总体上分布于1.5~4.0 cm之间。三款软件地表竖向变形测量的中误差和测量误差的均值及标准差均与地表竖向变形值不呈现明显相关性,同时地表竖向变形量测误差均近似满足正态分布,因此可选取测量误差的95%置信区间对地表竖向变形测量结果进行修正。在此基础之上,运用Global Mapper软件开展了黑方台党川段滑坡地表竖向变形识别与测量应用,结果表明Global Mapper软件能较为准确地识别出滑坡变形区域并圈定其位置。      

Debris flow hazard assessment by means of numerical simulations: implications for the Rotolon creek valley (Northern Italy)

On 4^th November 2010, a debris flow detached from a large debris cover accumulated above the lowermost portion of the Rotolon landslide (Vicentine Pre-Alps, NE Italy) and channelized in the valley below within the Rotolon Creek riverbed. Such event evolved into a highly mobile and sudden debris flow, damaging some hydraulic works and putting at high risk four villages located along the creek banks. A monitoring campaign was carried out by means of a ground based radar interferometer (GB-InSAR) to evaluate any residual displacement risk in the affected area and in the undisturbed neighbouring materials. Moreover, starting from the current slope condition, a landslide runout numerical modelling was performed by means of DAN-3D code to assess the impacted areas, flow velocity, and deposit distribution of the simulated events. The rheological parameters necessary for an accurate modelling were obtained through the back analysis of the 2010 debris flow event. Back analysis was calibrated with all of the available terrain data coming from field surveys and ancillary documents, such as topographic, geomorphological and geological maps, with pre- and post-event LiDAR derived DTMs, and with orthophotos. Finally, to identify new possible future debris flow source areas as input data for the new modelling, all the obtained terrain data were reanalysed and integrated with the GB-InSAR displacement maps; consequently, new simulations were made to forecast future events. The results show that the integration of the selected modelling technique with ancillary data and radar displacement maps can be a very useful tool for managing problems related to debris flow events in the examined area.     

三峡库区麻柳林滑坡变形特征及演化模拟

在库水位波动和降雨作用的共同影响下，库岸滑坡的变形规律往往更为复杂。以三峡库区麻柳林滑坡为例，基于野外调查、钻探编录、深部位移监测以及数值模拟等手段，分析了库水位波动和降雨作用下滑坡变形特征及演化规律。结果表明:麻柳林滑坡在粉质黏土层和块石层交界处发育一个次级滑带，目前该滑坡主要沿次级滑带运动，导致次级滑动的原因与坡体物质的差异性有关；S_i(S_f)指标分析法揭示滑坡的滑带还未完全破坏，滑坡仍处于蠕变状态；根据三峡水库水位调度规律，将一个完整水文年划分为6个阶段，数值模拟结果表明滑坡在库水位缓慢下降阶段变形速率较小、在快速下降阶段和低水位阶段变形速率持续增大、在快速上升阶段和缓慢上升阶段以及高水位阶段变形速率则保持平稳。其中，降雨的直接影响和降雨导致库水位波动进而对滑坡变形造成的间接影响，使得麻柳林滑坡在低水位阶段的变形显著增加、稳定性最差，应加强该时段内滑坡的监测和预警。      

Panzhihua airport landslide (Oct. 3rd 2009) and an emergency monitoring and warning system based on the internet of things

Panzhihua city(26°05’-27°21’N,101°08’102°15’E),located in a mountainous area,is one of the large cities in Sichuan province,China.A landslide occurred in the filling body of the eastern part of the Panzhihua airport on October 3,2009(hereafter called the 10.3 landslide).We conducted field survey on the landslide and adopted emergency monitoring and warning models based on the Internet of Things(IoT) to estimate the losses from the disaster and to prevent a secondary disaster from occurring.The results showed that four major features of the airport site had contributed to the landslide,i.e,high altitude,huge amount of filling rocks,deep backfilling and great difficulty of backfilling.The deformation process of the landslide had six stages and the unstable geological structure of high fillings and an earthquake were the main causes of the landslide.We adopted relative displacement sensing technology and Global System for Mobile Communications(GSM) technology to achieve remote,real-time and unattended monitoring of ground cracks in the landslide.The monitoring system,including five extensometers with measuring ranges of 200,450 and 700 mm,was continuously working for 17 months and released 7 warning signals with an average warning time of about 26 hours.At 10 am on 6 December 2009,the system issued a warning and on-site workers were evacuated and equipment protected immediately.At 2:20 pm on 7 December,a medium-scale collapse occurred at the No.5 monitoring site,which justified the alarm and proved the reliability and efficiency of the monitoring system.     

GIS-based evaluation on the fault motion-induced coseismic landslides

Earthquake-induced potential landslides are commonly estimated using landslide susceptibility maps. Nevertheless, the fault location is not identified and the ground motion caused by it is unavailable in the map. Thus, potential coseismic landslides for a specific fault motion-induced earthquake could not be predicted using the map. It is meaningful to incorporate the fault location and ground motion characteristics into the landslide predication model. A new method for a specific fault motion-induced coseismic landslide prediction model using GIS (Geographic Information System) is proposed herein. Location of mountain ridges, slope gradients over 45 o , PVGA (Peak Vertical Ground Accelerations) exceeded 0.15 g, and PHGA (Peak Horizontal Ground Accelerations) exceeded 0.25 g of slope units were representing locations that initiated landslides during the 1999 Chi-Chi earthquake in Taiwan. These coseismic landslide characteristics were used to identify areas where landslides occurred during Meishan fault motion-induced strong ground motions in Chiayi County in Taiwan. The strong ground motion (over 8 Gal in the database, 1 Gal = 0.01 m/s 2 , and 1 g = 981 Gal) characteristics were evaluated by the fault length, site distance to the fault, and topography, and their attenuation relations are presented in GIS. The results of the analysis show that coseismic landslide areas could be identified promptly using GIS. The earthquake intensity and focus depth have visible effects on ground motion. The shallower the focus depth, the larger the magnitude increase of the landslides. The GIS-based landslide predication method is valuable combining the geomorphic characteristics and ground motion attenuation relationships for a potential region landslide hazard assessment and in disaster mitigation planning.     

滑坡图像自动识别浅议

识别滑坡须先了解什么是滑坡,广义滑坡包括崩塌、滑坡、碎屑流、泥石流等所有斜坡重力侵蚀现象;狭义滑坡指部分斜坡沿着斜坡内的一个或数个面在重力的作用下作剪切运动的现象。各类滑坡有自已特殊的地表形态特征,发育的基本地质环境条件和触发因素,据这些特征识别滑坡。利用数字滑坡技术进行滑坡识别大致分为2步:(1)通过RS和GIS技术将不同时间的调查区地物现场以不同分辨率展现在数字图像上,并与地理控制及地质环境信息配准、组合,建立解译基础;(2)在滑坡地学理论指导下,通过人机交互方式进行解译和时空分析,获取减灾防灾需要的信息。该方法尚未达到遥感自动识别滑坡的程度,但建立解译基础的过程已可由计算机通过多种程序软件完成,故认为滑坡模式识别的前2个步骤:数字化及预处理已由计算机实现。现需探索的是用计算机实现基于滑坡地学理论知识,以人机交互方式进行的滑坡识别及分析过程。就狭义滑坡而言,基于DEM的滑坡地形识别已可由计算机实现。如能确定地面滑坡壁及滑体与地下滑面、滑床的关系,了解它们的光谱特征并建立计算模型,便可构建遥感技术的滑坡模式识别。     

基于物理模型试验的滑坡-抗滑桩位移场变化特征

滑坡-抗滑桩体系演化机理是滑坡灾害防治的重要理论基础,其中桩土相互作用是滑坡-抗滑桩体系中的关键,而位移场是相互作用的重要表征之一,因此,对滑坡-抗滑桩体系位移场的研究具有十分重要的工程意义。以物理模型试验为手段,基于三峡库区堆积层滑坡工程地质特征,建立大型物理试验模型,通过逐级施加荷载来模拟滑坡后缘推力,采用高速摄像机及粒子图像测速技术等获取滑坡坡表与桩顶位移数据,定量分析了体系位移场变化特征。试验结果显示:在滑坡-抗滑桩体系演化过程中,坡表位移场变化呈现出很好的规律性,这为桩土相互作用研究机理起到了一定的推动作用。      

基于物理模型试验的多层滑带滑坡变形演化特征

滑坡变形演化特征一直是滑坡灾害预测与防治领域急需解决的关键问题,但对于多层滑带滑坡的变形演化特征却少有研究。以物理模型试验为手段建立了三层滑带滑坡物理试验模型,完成了多层滑带滑坡变形演化全过程的模拟。基于PIV技术获取坡表位移数据,通过柔性测斜仪监测滑坡深部位移,同时布设土压力盒获取滑坡内部土压力的变化情况,实现了多层滑带滑坡演化过程多参量数据分析。试验结果表明,多层滑带滑坡破坏过程可分为初始、等速、加速和破坏4个阶段。不同破坏阶段滑坡的主要变形区域不同,下层滑体受到上层滑体牵引作用,在重力和推力作用下滑坡变形逐渐向浅层发展。变形过程中滑坡应力逐渐向滑带集中,滑坡推力沿埋深方向呈多级梯形分布。加速变形阶段滑带处应力迅速增大,滑坡体内产生多层应力集中带,滑带位置推力变化与滑坡位移显著相关。