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.     

库岸滑坡地下水位时间序列混沌特征识别与PSO-LSSVM模型预测

地下水位预测对滑坡稳定性分析具有重要意义,三峡库区库岸滑坡地下水位时间序列在季节性强降雨和周期性库水位涨落等诸多因素影响下呈现混沌特征。在对地下水位序列进行相空间重构的基础上,采用饱和关联维数法和最大Lyapunov指数法对其混沌特征进行验证。再用预测性能优秀的最小二乘支持向量机(LSSVM)模型对其进行预测,并用粒子群算法优化选取LSSVM模型的参数,以克服LSSVM模型参数选取困难的缺点。以三峡库区三舟溪滑坡前缘STK-1水文孔日平均地下水位序列为例进行了混沌分析,分别运用粒子群优化的LSSVM模型(PSO-LSSVM)和BP神经网络模型对STK-1水文孔地下水位进行了预测。结果表明库岸滑坡地下水位序列存在混沌特征,PSO-LSSVM模型预测结果的均方根误差为0.193m,拟合优度为0.815,说明预测效果较理想,且PSO-LSSVM模型预测精度高于BP网络模型,具有较强的实用性。      

新铺下二台滑坡变形机制及中长期预报模型

以奉节新铺下二台滑坡为例, 基于GPS位移监测数据、裂缝数据、降雨量及库水位等多源数据, 总结分析了大型古滑坡的复活规律, 引入滑坡中长期预报模型, 实现了以季度或月份为时间单位的跨水文年滑坡位移预测, 并通过岩土体蠕变压缩模型, 验证了推移式滑坡后缘裂缝形成机理。结果表明: ①降雨是下二台滑坡变形的主导因素, 滑坡变形使得滑体产生裂缝并成为降雨入渗通道, 加剧了岩体破碎与软弱层软化, 降低了滑坡稳定性, 集中持续降雨可使滑坡失稳破坏; ②通过模型预测值与地表监测数据的比较, 将年降雨量作为滑坡中长期预报模型中的主控因素具有实际可操作性且有助于提高滑坡中长预报精度; ③推移式滑坡后缘裂缝由滑坡推移式位移和岩土体压缩形成, 引入蠕变压缩模型计算的裂缝宽度并和监测数据的比较说明, 蠕变压缩模型非常适合该类边坡, 同时应用岩土体蠕变压缩模型反推得到岩土体平均变形模量, 判断岩体破碎程度, 可以为滑坡稳定性分析及后续工程治理提供参考。      

奉节县曾家棚滑坡时空差异性变形特征与成因机制分析

研究库水位波动和降雨影响下滑坡的位移变形特征并分析其破坏机制，对了解三峡库区滑坡的演化过程具有重要意义。以奉节曾家棚滑坡为例，基于GPS地表监测位移分析了滑坡在不同特征库水位运行阶段的变化规律，结合灰色关联度模型确定了滑坡不同部位的变形在不同阶段的主要控制因素，借助GEO-Studio软件模拟了曾家棚滑坡在历史降雨和库水位波动耦合作用下的稳定性变化，并与定量分析结果进行了交叉检验。结果表明:曾家棚滑坡的运动状态随时间变化，从缓慢蠕变状态进入阶跃变形状态。平面上，中东部坡体与西部坡体相比，运动更加强烈；剖面上，前缘变形早且变形量大。曾家棚滑坡变形失稳过程为初期蓄水启动了曾家棚古滑坡，前缘首先发生变形；降雨作为中后期主控因素，和库水位波动联合作用共同诱发了滑坡多次阶跃变形，使滑坡前中后部形成贯通裂缝；最终由二十年一遇的暴雨诱发滑坡发生整体破坏。      

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列与降雨量、库水位高程序列进行时滞互相关分析,确定时滞参数,进而应用多变量灰色系统理论方法,建立了时滞GM(1,3)预测模型,并对滑坡位移速率进行预测验证。结果表明:三峡库区新铺滑坡位移速率与降雨量显著相关,对降雨量的响应滞后时间约为5 d,滑体中后部受降雨影响比前缘更明显;位移速率与库水位高程高度相关,对三峡库区库水位涨落的响应滞后时间约为31 d,滑坡前缘受库水位涨落影响更明显,且离长江越近,滞后时间越短;利用加入时滞参数的时滞GM(1,3)模型进行预测,模型拟合优度达到0.702,相比GM(1,1)模型和未顾及时滞因素的GM(1,3)模型,预测精度分别提升了53.8%和58.3%,平均绝对误差百分比分别降低了7.19%和7.47%,在滑坡位移速率预测及库岸滑坡防灾减灾领域具有一定的工程应用价值。     

Preliminary studies on the dynamic prediction method of rainfall-triggered landslide

Rainfall-triggered landslides have posed significant threats to human lives and property each year in China. This paper proposed a meteorologicalgeotechnical early warning system GRAPES-LFM(GRAPES: Global and Regional Assimilation and Pr Ediction System; LFM: Landslide Forecast Model),basing on the GRAPES model and the landslide predicting model TRIGRS(Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Model) for predicting rainfall-triggered landslides.This integrated system is evaluated in Dehua County,Fujian Province, where typhoon Bilis triggered widespread landslides in July 2006. The GRAPES model runs in 5 km×5 km horizontal resolution, and the initial fields and lateral boundaries are provided by NCEP(National Centers for Environmental Prediction) FNL(Final) Operational Global Analysis data. Quantitative precipitation forecasting products of the GRAPES model are downscaled to 25 m×25 m horizontal resolution by bilinear interpolation to drive the TRIGRS model. Results show that the observed areas locate in the high risk areas, and the GRAPES-LFM model could capture about 74% of the historical landslides with the rainfall intense 30mm/h. Meanwhile, this paper illustrates the relationship between the factor of safety(FS) and different rainfall patterns. GRAPES-LFM model enables us to further develop a regional, early warning dynamic prediction tool of rainfall-induced landslides.     

基于修正Green-Ampt模型的降雨诱发区域浅层斜坡失稳灾害分析

由于具有类似的工程地质和水文地质条件, 在高度相关的降雨作用下, 同一个区域中的降雨诱发浅层斜坡失稳灾害常成群出现。在区域尺度预测浅层斜坡失稳灾害对滑坡灾害的防灾减灾工作具有重要的意义。为此, 提出了一种基于力学原理的降雨诱发浅层斜坡失稳灾害预测新模型RARIL。该模型采用修正Green-Ampt模型进行降雨入渗分析, 采用无限体边坡模型进行安全系数计算, 利用可靠度原理考虑区域斜坡稳定性分析中的参数不确定性。该模型具有可考虑降雨诱发浅层斜坡的失稳力学机理、可考虑区域内斜坡土体参数不确定性, 以及计算效率高、易于在GIS平台上实现等优点。案例分析表明, RARIL模型较为准确地预测了2010年8月12日11∶00至2010年8月14日9∶00期间强降雨在四川省汶川县映秀镇附近的303省道K0-K20段沿线区域引发的滑坡灾害, 研究结果证明RARIL模型在预测降雨诱发区域斜坡失稳灾害方面有很好的应用前景。      

Probability Prediction Model for Landslide Occurrences in Xi’an,Shaanxi Province,China

Landslides are increasing since the 1980s in Xi’an, Shaanxi Province, China. This is due to the increase of the frequency and intensity of precipitation caused by complex geological structures, the presence of steep landforms, seasonal heavy rainfall, and the intensifcation of human activities. In this study, we propose a landslide prediction model based on the analysis of intraday rainfall （IR） and antecedent effective rainfall （AER）. Primarily, the number of days and degressive index of the antecedent effective rainfall which affected landslide occurrences in the areas around Qin Mountains, Li Mountains and Loess Tableland was established. Secondly, the antecedent effective rainfall and intraday rainfall were calculated from weather data which were used to construct critical thresholds for the 10%, 50% and 90% probabilities for future landslide occurrences in Qin Mountain, Li Mountain and Loess Tableland. Finally, the regions corresponding to different warning levels were identified based on the relationship between precipitation and the threshold, that is; “A” region is safe, “B” region is on watch alert, “C” region is on warning alert and “D” region is on severe warning alert. Using this model, a warning program is proposed which can predict rainfall-induced landslides by means of real-time rain gauge data and real-time geo-hazard alert and disaster response programs. Sixteen rain gauges were installed in the Xi’an region by keeping in accordance with the regional geology and landslide risks. Based on the data from gauges, this model accurately achieves the objectives of conducting real-time monitoring as well as providing early warnings of landslides in the Xi’an region.     

The formation of the Wulipo landslide and the resulting debris flow in Dujiangyan City,China

The Wulipo landslide, triggered by heavy rainfall on July 10, 2013, transformed into debris flow,resulted in the destruction of 12 houses, 44 deaths, and 117 missing. Our systematic investigation has led to the following results and to a new understanding about the formation and evolution process of this hazard. The fundamental factors of the formation of the landslide are a high-steep free surface at the front of the slide mass and the sandstone-mudstone mixed stratum structure of the slope. The inducing factor of the landslide is hydrostatic and hydrodynamic pressure change caused by heavy continuous rainfall. The geological mechanical model of the landslide can be summarized as "instability-translational slide-tension fracture-collapse" and the formation mechanism as "translational landslide induced by heavy rainfall". The total volume of the landslide is 124.6×104 m3, and 16.3% of the sliding mass was dropped down from the cliff and transformed into debris flow during the sliding process, which enlarged 46.7% of the original sliding deposit area. The final accumulation area is found to be 9.2×104 m2. The hazard is a typical example of a disaster chain involving landslide and its induced debris flow. The concealment and disaster chain effect is the main reason for the heavy damage. In future risk assessment, it is suggested to enhance the research onpotential landslide identification for weakly intercalated slopes. By considering the influence of the behaviors of landslide-induced debris flow, the disaster area could be determined more reasonably.     

汶川地震滑坡-碎屑流致灾距探讨

为了阐明地震滑坡的运动特性并对其进行致灾距的预测,基于遥感影像解译和野外调查数据,借助经验公式法,分析了汶川地震滑坡水平最大运移距离L与前后缘高差H之间的相关性,给出了经验公式;探讨了不同滑坡之间滑程的差异与异常。结果表明:若已知H,可用L=aH+b或L=aHb对总位移进行预测初探;将视摩擦系数H/L=0.45作为汶川地震高速远程型滑坡的上限较合适;滑坡体积、源区破裂面积与L呈正相关,与H/L呈负相关;地震滑坡易发生在山脊线平行于断裂带、垂直于地震波传播方向的山体两侧;崩塌型滑坡易发前后缘高差范围在10~100m之间,大型高速远程型滑坡易发前后缘高差大于200m;滑坡源区易发坡度分布在25°51°之间,滑床坡降变化范围为0~58°,高速远程型滑坡的滑床坡降主要在8°20°之间;分析认为滑程差异和异常是距离效应、能量传递与岩体挡板效应、滚动润滑与气垫效应、体积与破裂面积效应、地质因子、地形因子、颗粒级配与颗粒流效应等因素综合作用的结果,考虑上述因素有益于滑坡-碎屑流致灾距的预测分析。      

一种基于M估计的Robust-ELM滑坡位移预测方法

采用传统ELM算法进行滑坡位移预测时，其网络输出权值由最小二乘估计得出，导致ELM抗差能力较差，从而造成网络训练参数不准确。为此，将M估计与ELM相结合，提出一种基于M估计的Robust-ELM滑坡变形预测方法。该方法利用加权最小二乘方法来取代最小二乘法计算ELM输出权值，以减少滑坡监测数据中粗差对ELM预测的干扰。分别以链子崖、古树屋滑坡体为例，将Robust-ELM进行了单维、多维粗差的抵御性验证。结果表明，该方法能够有效降低粗差对预测的影响，具有良好的抗差能力。     

Reactivation of a huge ancient landslide by surface water infiltration

An ancient landslide, situated in Deqin County, Yunnan Province, China, was used to investigate the reactivation by water infiltration. This study considers the infiltration process and landslide stability using finite-element method(FEM)-based models. The results show that the reactivation of old landslide deposit was triggered by the long-term leakage of diversion ditch before October 2012, and the reactivation was triggered again by the intense rainfall on 7-9 October 2012. The old cracks, which formed in the earlier reactivation of landslide, played a key role for the rainfall infiltration. They offered a preferential path for much more rainfall to infiltrate fast into deep soil, and caused wetting front to move down faster in landslide. The old slip zone with lower permeability was another important factor to cause the infiltrated water to accumulate and form a high pore water pressure above slip zone. Then the high pore water pressure decreased the shear strength of slip zone and triggered the reactivation of the old landslide deposit again.     

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.     

Regional scale rainfall- and earthquake-triggered landslide susceptibility assessment in Wudu County, China

Wudu County in northwestern China frequently experiences large-scale landslide events.High-magnitude earthquakes and heavy rainfall events are the major triggering factors in the region.The aim of this research is to compare and combine landslide susceptibility assessments of rainfalltriggered and earthquake-triggered landslide events in the study area using Geographical Information System(GIS) and a logistic regression model.Two separate susceptibility maps were produced using inventories reflecting single landslide-triggering events,i.e.,earthquakes and heavy rain storms.Two groups of landslides were utilized: one group containing all landslides triggered by extreme rainfall events between 1995 and 2003 and the other group containing slope failures caused by the 2008 Wenchuan earthquake.Subsequently,the individual maps were combined to illustrate the locations of maximum landslide probability.The use of the resulting three landslide susceptibility maps for landslide forecasting,spatial planning and for developing emergency response actions are discussed.The combined susceptibility map illustrates the total landslide susceptibility in the study area.     

BDS/GPS单历元阻尼LAMBDA算法及其在边坡变形监测中的应用效果分析

论述了BDS/GPS双系统组合下的单频单历元阻尼LAMBDA算法原理,并基于该算法实现一种实时远程变形监测系统。以理县某边坡变形监测应用为例,比较了GPS、BDS及其组合系统下的观测条件和阻尼LAMBDA算法定位效果：BDS的观测卫星数多于GPS,PDOP值相较于GPS也更加稳定;BDS单历元解标准差在N、E、U方向上分别为0.40 cm、0.31 cm、1.00 cm,优于GPS的0.61 cm、0.40 cm、1.74 cm。长期实验结果表明,在我国南部边坡监测中,BDS相比GPS具有一定优势。理县边坡23个月内位移量显著,3个监测站平均位移在N、E、U方向上分别达到8.70 cm、43.63 cm、18.03 cm。强降雨是引起土质山体滑坡的主要因素,使用累积降雨量和累积位移量建立线型回归模型,线性相关系数在0.98以上。在边坡监测中,可将实时的位移数据和降雨数据作为滑坡预警的重要依据。     

尼泊尔地区季节性水文负荷对地震活动性的调制作用

利用尼泊尔地区GPS时间序列季节性信号,构建区域季节性三维位移场和应力应变场模型,再结合降水和地震目录等数据,研究该地区季节性水文负荷对地震活动性的调制作用。结果表明：1）季节性地表位移与降水存在较强的时空相关性;2）季风期的降水对断层的长期运动趋势产生扰动,使库仑应力得到一定程度的释放,进而对地震活动产生抑制作用,并影响地震发生时间。     

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Effect of rainfall on a colluvial landslide in a debris flow valley

A colluvial landslide in a debris flow valley is a typical phenomena and is easily influenced by rainfall. The direct destructiveness of this kind of landslide is small, however, if failure occurs the resulting blocking of the channel may lead to a series of magnified secondary hazards. For this reason it is important to investigate the potential response of this type of landslide to rainfall. In the present paper, the Goulingping landslide, one of the colluvial landslides in the Goulingping valley in the middle of the Bailong River catchment in Gansu Province, China, was chosen for the study. Electrical Resistivity Tomography (ERT), Terrestrial Laser Scanning (TLS), together with traditional monitoring methods, were used to monitor changes in water content and the deformation of the landslide caused by rainfall. ERT was used to detect changes in soil water content induced by rainfall. The most significant findings were as follows:(1) the water content in the centralupper part (0~41 m) of the landslide was greater than in the central-front part (41~84 m) and (2) there was a relatively high resistivity zone at depth within the sliding zone. The deformation characteristics at the surface of the landslide were monitored by TLS and the results revealed that rainstorms caused three types of deformation and failure: (1) gully erosion at the slope surface; (2) shallow sliding failure; (3) and slope foot erosion. Subsequent monitoring of continuous changes in pore-water pressure, soil pressure and displacement (using traditional methods) indicated that long duration light rainfall (average 2.22 mm/d) caused the entire landslide to enter a state of creeping deformation at the beginning of the rainy season. Shear-induced dilation occurred for the fast sliding (30.09 mm/d) during the critical failure sub-phase (EF). Pore-water pressure in the sliding zone was affected by rainfall. In addition, the sliding L1 parts of the landslide exerted a discontinuous pressure on the L2 part. Through the monitoring and analysis, we conclude that this kind of landslide may have large deformation at the beginning and the late of the rainy season.     

Dynamic process analysis of the Niumiangou landslide triggered by the Wenchuan earthquake using the finite difference method and a modified discontinuous deformation analysis

The Niumiangou landslide was the largest landslide triggered by the 2008 Wenchuan earthquake, which was significantly affected by the amplification effect of seismic acceleration. The ringshear experiments indicated that the materials in the source area of the Niumiangou landslide were subjected to friction degradation under a big shear displacement, which may result in rapid movement of the landslide. In order to better understand the landslide movement and study the effect of the friction degradation on movement mechanisms, the dynamic process of Niumiangou landslide was simulated with a new numerical method, which combines the finite difference method(FDM) and the discontinuous deformation analysis(DDA). First, the FDM was used to study the initiation time, amplification effect and velocity of the landslide. Afterwards, these initiation velocities were applied to the blocks in the DDA model by corresponding coordination in the FDM model. A displacementdependent friction model of the sliding surface was incorporated into DDA code to further understand the kinetic behavior of the landslide. The results show that the displacement-dependent friction strongly decreases the friction coefficient of sliding surface under a big displacement, which can obviously promote the run-out and velocity of landslide. The model output well matches the topographic map formed by the landslide. This implies that the proposed model can be applied to the simulation of earthquake-induced landslides with amplification effect, and the friction degradation model is important to clarify the movement mechanism of high-speed and long-distance landslides.