2008年汶川地震滑坡详细编目及其空间分布规律分析

最新研究成果表明, 2008年5月12日汶川MS 8.0级地震触发了超过197000处滑坡。首先,基于GIS与遥感技术构建了汶川地震滑坡的3类编目图,分别为单体滑坡面分布数据、滑坡中心点位置和滑坡后壁点位置。构建方法为基于地震前后高分辨率遥感影像的目视解译方法,区分单体滑坡并圈定其边界,对滑坡后壁进行识别与定点,并开展了部分滑坡的野外验证工作。这些滑坡分布在一个面积大约为110000km2的区域内,滑坡总面积约为1160km2。选择一个面积约为44031km2的区域作为研究区,区内滑坡数量为196007个,滑坡面积为1150.622km2,这是最详细完整的汶川地震滑坡编录成果,也是单次地震事件触发滑坡最多的记录。其次,开展研究区内的地震滑坡空间分布规律的研究。基于滑坡面与滑坡中心点分别构建滑坡空间分布面积密度图与点密度图,结果表明:滑坡多沿着映秀北川断裂分布,多发生在断裂的上盘。滑坡的高密度区位于映秀北川同震地表破裂的南西段(映秀镇与北川县之间)的上盘区域,这一区域恰对应着逆冲分量为主的断裂上盘,表明逆冲断裂对上盘区域发生滑坡的极强烈的控制作用,而该区域正是形变最大的区域,因此说明是地震滑坡发生的强烈控制作用。基于滑坡面密度(LAP)、滑坡中心点密度(LCND)与滑坡后壁点密度(LTND)这3个衡量指标,使用统计分析方法,评价了汶川地震滑坡与地震参数、地质参数、地形参数的关系。结果表明:LAP、LCND与LTND这3个衡量指标与坡度、地震烈度与PGA存在明显的正相关关系; 与距离震中、距离映秀北川同震地表破裂存在负相关关系; 斜坡曲率越接近0,滑坡越不易发生; LAP、LCND与LTND的高值高程区间为1200～3000m; 滑坡发生的优势坡向为E、SE、S方向; 滑坡发育的易发岩性为砂岩与粉砂岩(Z)、花岗岩; 滑坡与坡位的相关关系不太明显。统计结果还表明LCND与LTND两个衡量指标的差异对地震与地质因子不敏感,而对地形因子较敏感。最后将本文的统计结果与以往的汶川地震滑坡空间分布规律统计成果进行了一些对比,对比结果表明,对于某些因子,如高程、岩性、距离震中、距离映秀北川断裂的统计分析结果,采用不完整的滑坡分布数据或点数据,与采用较完整的滑坡分布面数据会有一定的差异,这种差异并未出现在针对坡度与坡向等因子的统计对比结果中。总之,作者认为一个完备、详细的地震滑坡分布面要素编目图是地震滑坡空间分布规律定量分析、危险性定量分析与滑坡控制的地震区地貌演化研究的重要基础,否则,与实际情况相比,得到统计结果会有一定的偏差,本文的研究成果与以往成果的对比结果证明了这一点。     

GIS-based spatial prediction of debris flows using logistic regression and frequency ratio models for Zêzere River basin and its surrounding area,Northwest Covilhã, Portugal

Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate?=?80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.     

Landslides triggered by the 22 July 2013 Minxian–Zhangxian,China, Mw 5.9 earthquake: Inventory compiling and spatial distribution analysis

On July 22, 2013, an earthquake of Ms. 6.6 occurred at the junction area of Minxian and Zhangxian counties, Gansu Province, China. This earthquake triggered many landslides of various types, dominated by small-scale soil falls, slides, and topples on loess scarps. There were also a few deep-seated landslides, large-scale soil avalanches, and fissure-developing slopes. In this paper, an inventory of landslides triggered by this event is prepared based on field investigations and visual interpretation of high-resolution satellite images. The spatial distribution of the landslides is then analyzed. The inventory indicates that at least 2330 landslides were triggered by the earthquake. A correlation statistics of the landslides with topographic, geologic, and earthquake factors is performed based on the GIS platform. The results show that the largest number of landslides and the highest landslide density are at 2400 m–2600 m of absolute elevation, and 200 m–300 m of relative elevation, respectively. The landslide density does not always increase with slope gradient as previously suggested. The slopes most prone to landslides are in S, SW, W, and NW directions. Concave slopes register higher landslide density and larger number of landslides than convex slopes. The largest number of landslides occurs on topographic position with middle slopes, whereas the highest landslide density corresponds to valleys and lower slopes. The underlying bedrocks consisting of conglomerate and sandstone of Lower Paleogene (E^b) register both the largest number and area of landslides and the highest landslide number and area density values. Correlations of landslide number and landslide density with perpendicular- and along-strike distance from the epicenter show an obvious spatial intensifying character of the co-seismic landslides. The spatial pattern of the co-seismic landslides is strongly controlled by a branch of the Lintan-Dangchang fault, which indicates the effect of seismogenic fault on co-seismic landslides. In addition, the area affected by landslides related to the earthquake is compared to the relationship of “area affected by landslides vs. earthquake magnitude” constructed based on earthquakes worldwide, and it is shown that the area affected by landslides triggered by the Minxian–Zhangxian earthquake is larger than that of almost all other events with similar magnitudes.     

逻辑回归模型在玉树地震滑坡危险性评价中的应用与检验

2010年4月14日07时49分(北京时间),青海省玉树县发生了Ms7.1级大地震。作者基于高分辨率遥感影像解译与现场调查验证的方法,圈定了2036处本次地震诱发滑坡。这些滑坡受地震地表破裂控制强烈,规模相对较小,常常密集成片分布。滑坡类型多样,以崩塌型滑坡为主,还包括滑动型、流滑型、碎屑流型、复合型等类型的滑坡。本文基于地理信息系统(GIS)与遥感(RS)技术,应用逻辑回归模型开展玉树地震滑坡危险性评价,并对结果合理性进行检验。应用GIS技术建立玉树地震滑坡灾害及相关滑坡影响因子空间数据库,选择高程、斜坡坡度、斜坡坡向、斜坡曲率、与水系距离、坡位、断裂、地层岩性、归一化植被指数(NDVI)、公路、同震地表破裂、地震动峰值加速度(PGA)共12个因子作为玉树地震滑坡影响因子,在GIS平台下将这些因子专题图层栅格化。应用逻辑回归模型得到每个因子分级的回归系数,然后建立滑坡危险性指数分布图。利用玉树地震滑坡空间分布图对滑坡危险性指数图进行检验,正确率达到83.21%。滑坡危险性分级结果表明,在占研究区总面积4.97%的"很高危险度"的较小范围内,实际发育滑坡数量为766个,占总滑坡面积的比例高达37.62%,表明地震滑坡危险性评价结果良好。不同危险性级别的滑坡点密度统计结果表明,滑坡点密度随着危险性级别的升高而非常迅速的升高。     

Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu, China earthquake

On 14 April 2010 at 07:49 (Beijing time), a catastrophic earthquake with Ms 7.1 struck Yushu County, Qinghai Province, China. A total of 2,036 landslides were interpreted from aerial photographs and satellite images, verified by selected field checking. These landslides cover about a total area of 1.194 km². The characteristics and failure mechanisms of these landslides are presented in this paper. The spatial distribution of the landslides is evidently strongly controlled by the locations of the main co-seismic surface fault ruptures. The landslides commonly occurred close together. Most of the landslides are small; there were only 275 individual landslide (13.5 % of the total number) surface areas larger than 1,000 m². The landslides are of various types. They are mainly shallow, disrupted landslides, but also include rock falls, deep-seated landslides, liquefaction-induced landslides, and compound landslides. Four types of factors are identified as contributing to failure along with the strong ground shaking: natural excavation of the toes of slopes, which mean erosion of the base of the slope, surface water infiltration into slopes, co-seismic fault slipping at landslide sites, and delayed occurrence of landslides due to snow melt or rainfall infiltration at sites where slopes were weakened by the co-seismic ground shaking. To analyze the spatial distribution of the landslides, the landslide area percentage (LAP) and landslide number density (LND) were compared with peak ground acceleration (PGA), distance from co-seismic main surface fault ruptures, elevation, slope gradient, slope aspect, and lithology. The results show landslide occurrence is strongly controlled by proximity to the main surface fault ruptures, with most landslides occurring within 2.5 km of such ruptures. There is no evident correlation between landslide occurrences and PGA. Both LAP and LND have strongly positive correlations with slope gradient, and additionally, sites at elevations between 3,800 and 4,000 m are relatively susceptible to landslide occurrence; as are slopes with northeast, east, and southeast slope aspects. Q₄ al-pl, N, and T₃ kn ¹ have more concentrated landslide activity than others. This paper provides a detailed inventory map of landslides triggered by the 2010 Yushu earthquake for future seismic landslide hazard analysis and also provides a study case of characteristics, failure mechanisms, and spatial distribution of landslides triggered by slipping-fault generated earthquake on a plateau.     

Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study

The crucial and difficult task in landslide susceptibility analysis is estimating the probability of occurrence of future landslides in a study area under a specific set of geomorphic and topographic conditions. This task is addressed with a data-driven probabilistic model using likelihood ratio or frequency ratio and is applied to assess the occurrence of landslides in the Tevankarai Ar sub-watershed, Kodaikkanal, South India. The landslides in the study area are triggered by heavy rainfall. Landslide-related factors—relief, slope, aspect, plan curvature, profile curvature, land use, soil, and topographic wetness index proximity to roads and proximity to lineaments—are considered for the study. A geospatial database of the related landslide factors is constructed using Arcmap in GIS environment. Landslide inventory of the area is produced by detailed field investigation and analysis of the topographical maps. The results are validated using temporal data of known landslide locations. The area under the curve shows that the accuracy of the model is 85.83%. In the reclassified final landslide susceptibility map, 14.48% of the area is critical in nature, falling under the very high hazard zone, and 67.86% of the total validation dataset landslides fall in this zone. This landslide susceptibility map is a vital tool for town planning, land use, and land cover planning and to reduce risks caused by landslides.     

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.     

俯冲带地区压扭断裂型地震触发滑坡及其剥蚀厚度空间分布规律分析

2010年1月12日海地MW 7.0级地震触发了大量的滑坡。我们基于GIS与遥感技术构建了3类详细完备的海地地震滑坡编录图,分别为单体滑坡面分布数据,滑坡中心点位置数据与滑坡后壁点位置数据。结果表明海地地震触发了30828处滑坡,这些滑坡大致分布在一个面积为3192.85km2的区域内,滑坡覆盖面积为15.736km2。基于滑坡中心点密度(LCND)、滑坡后壁点密度(LTND)、滑坡面积百分比(LAP)与滑坡剥蚀厚度(LET)这4个衡量指标,使用统计分析方法,分析了海地地震滑坡及其剥蚀厚度与地震参数、地形参数、公路参数的关系。分析结果表明滑坡与坡度、地震动峰值加速度(PGA)存在大致的正相关关系; 与距离恩里基约芭蕉园断裂、距离水系存在大致的负相关关系; 滑坡沿着恩里基约芭蕉园断裂距离的统计结果表明,震中以西距离震中22~26km与8~12km的区域,与震中以东距离震中6~18km的区域是地震滑坡易发区域; 斜坡曲率值越接近0,也就是坡面较平的斜坡越不容易在地震条件下发生滑动; LCND、LTND、LAP与LET高值对应的高程区间为200~1200m; 滑坡发生的优势坡向为E方向; 滑坡的发生与距离震中、距离公路没有太明确的关系。     

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.     

An open dataset for landslides triggered by the 2016 Mw 7.8 Kaikōura earthquake,New Zealand

On November 14, 2016, the northeastern South Island of New Zealand was hit by the magnitude Mw 7.8 Kaikōura earthquake, which is characterized by the most complex rupturing mechanism ever recorded. The widespread landslides triggered by the earthquake make this event a great case study to revisit our current knowledge of earthquake-triggered landslides in terms of factors controlling the spatial distribution of landslides and the rapid assessment of geographic areas affected by widespread landsliding. Although the spatial and size distributions of landslides have already been investigated in the literature, a polygon-based co-seismic landslide inventory with landslide size information is still not available as of June 2021. To address this issue and leverage this large landslide event, we mapped 14,233 landslides over a total area of approximately 14,000 km². We also identified 101 landslide dams and shared them all via an open-access repository. We examined the spatial distribution of co-seismic landslides in relation to lithologic units and seismic and morphometric characteristics. We analyzed the size statistics of these landslides in a comparative manner, by using the five largest co-seismic landslide inventories ever mapped (i.e., Chi-Chi, Denali, Wenchuan, Haiti, and Gorkha). We compared our inventory with respect to these five ones to answer the question of whether the landslides triggered by the 2016 Kaikōura earthquake are less numerous and/or share size characteristics similar to those of other strong co-seismic landslide events. Our findings show that the spatial distribution of the Kaikōura landslide event is not significantly different from those belonging to other extreme landslide events, but the average landslide size generated by the Kaikōura earthquake is relatively larger compared to some other large earthquakes (i.e., Wenchuan and Gorkha).

     

The Hattian Bala rock avalanche and associated landslides triggered by the Kashmir Earthquake of 8 October 2005

The Kashmir Earthquake of the 8 October killed an estimated 87 350 people, 25 500 through co-seismic landslides. The largest landslide associated with the earthquake was the 68 × 10⁶ m³ Hattian Bala rock avalanche that destroyed a village and killed around 1000 people. The deposit blocks the valley to a depth of 130 m impounding a lake that reached the dam-crest in April 2007. An outburst flood now threatens a major settlement 3 km downstream. A series of space images reveals landslide clusters in the rock avalanche source area prior to the earthquake. The images also reveal a large slow-moving landslide with its toe in the lake, failure of this landslide may induce dam failure through overtopping and scour. Eighty five landslides in the Hattian Bala catchment predate the shaking of 8 October 2005, a further 73 are co-seismic with the main shock, and 21 postdate it in the period up to October 2006. Landslide magnitude–frequency distribution plots derived from satellite images allow an assessment of the contribution of seismically triggered events as compared to background rates of activity.     

基于GIS与ANN模型的地震滑坡易发性区划

基于遥感数据、地理信息系统(GIS)技术和人工神经网络(ANN)模型,开展地震滑坡易发性区划研究.2010年4月14日玉树地震后,基于航片与卫星影像目视解译,并辅以野外调查的方法,在地震区圈定了2036处地震诱发滑坡.选择高程、坡度、坡向、斜坡曲率、坡位、与水系距离、地层岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)、与同震地表破裂距离、地震动峰值加速度(PGA)共12个因子作为地震滑坡易发性评价因子.这些因子均是应用GIS技术与遥感影像处理技术,基于地形数据、地质数据、遥感数据得到.训练样本中的滑动样本有两组,一组是滑坡区整个单滑坡体的质心位置,另一组是滑坡滑源区滑前的坡体高程最高的位置.应用这12个影响因子,分别采用这两组评价样本,基于ANN模型建立地震滑坡易发性索引图,基于GIS工具建立地震滑坡易发性分级图.分别应用训练样本中滑坡分布的点数据去检验各自的结果正确率,正确率分别为81.53％与81.29％,表明ANN模型是一种高效科学的地震滑坡易发性区划模型.     

GIS支持下基于CF方法的2013年芦山地震滑坡因子敏感性分析

本文依据研究区现有地形因子(高程、坡度、坡向、斜坡曲率)、地质因子(岩性、距深大活动断裂距离)和地震因子(PGA、距震中距离)等相关影响因子资料并结合地震区基本情况,以GIS技术作为操作平台,采用确定性系数法,开展较为详细的芦山地震区地震滑坡影响因子敏感性分析工作。首先,以GIS技术作为操作平台将地震滑坡的8个影响因子据研究区特征进行分级,构建不同影响因子分级的栅格图层进行地震滑坡分布相关参数统计; 其次,采用确定性系数法计算8个地震滑坡影响因子分级区间对应的CF值,分别提取出地震滑坡最为敏感分级区间以进行地震滑坡影响因子敏感性分析,从而衡量不同影响因子分级区间对地震滑坡易发性敏感程度。地震滑坡影响因子敏感性分析结果表明,除斜坡曲率因子与距震中距离因子对地震滑坡易发性不敏感外,其他6个影响因子对地震滑坡易发性均很敏感,分别是影响滑坡发生的主要地形、地质和地震因子。针对斜坡曲率因子和距震中距离因子对滑坡的易发性不是很敏感是否受内部其他影响因子限制所进行的分析与讨论结果表明,SW向很可能限制了斜坡曲率对地震滑坡易发性的敏感程度,地层岩性对距震中距离因子限制作用更为明显,除奥陶系、志留系外的地层岩性对距震中距离因子敏感性程度都有限制。文章所得成果具有一定的方法理论意义,对于防震减灾工作也具有一定参考意义。     

Statistical evaluation of the effect of earthquake with other related factors on landslide susceptibility: using the watershed area of Shihmen reservoir in Taiwan as a case study

Landslide susceptibility evaluation is one of the most important issues in watershed management. After an earthquake, the landslide susceptibility decreases functionally with increases in the distance from the epicenter. Under the same rainfall intensity, landslides are more likely to occur in an area where earthquakes occur more frequently. However, the questions of how much an earthquake should be weighted and how to evaluate the effects of an earthquake still need to be studied. To understand how earthquakes affect rainfall-triggered landslides, the horizontal peak ground acceleration (PGA) data from the Central Weather Bureau Seismic Network is used as the earthquake factor and combined with other factors to determine the weight of earthquakes in landslide susceptibility using logistic regression. The results indicate that the ability of landslide prediction is better when considering the earthquake factor. This study also proved that although there are no co-seismic landslides (after earthquakes) in the study area, the earthquake factor is still required to increase the model accuracy. PGA has been described as a usable factor. In areas with frequent earthquakes and high geological activity, when using historical data to evaluate landslide susceptibility, the earthquake factor should be taken into consideration to prevent errors.     

A comparative study on the landslide susceptibility mapping using logistic regression and statistical index models

The logistic regression and statistical index models are applied and verified for landslide susceptibility mapping in Daguan County, Yunnan Province, China, by means of the geographic information system (GIS). A detailed landslide inventory map was prepared by literatures, aerial photographs, and supported by field works. Fifteen landslide-conditioning factors were considered: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, STI, SPI, and TWI were derived from digital elevation model; NDVI was extracted from Landsat ETM7; rainfall was obtained from local rainfall data; distance to faults, distance to roads, and distance to rivers were created from a 1:25,000 scale topographic map; the lithology was extracted from geological map. Using these factors, the landslide susceptibility maps were prepared by LR and SI models. The accuracy of the results was verified by using existing landslide locations. The statistical index model had a predictive rate of 81.02%, which is more accurate prediction in comparison with logistic regression model (80.29%). The models can be used to land-use planning in the study area.     

Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area,Yogyakarta, Java

There are different approaches and techniques for landslide susceptibility mapping. However, no agreement has been reached in both the procedure and the use of specific controlling factors employed in the landslide susceptibility mapping. Each model has its own assumption, and the result may differ from place to place. Different landslide controlling factors and the completeness of landslide inventory may also affect the different result. Incomplete landslide inventory may produce significance error in the interpretation of the relationship between landslide and controlling factor. Comparing landslide susceptibility models using complete inventory is essential in order to identify the most realistic landslide susceptibility approach applied typically in the tropical region Indonesia. Purwosari area, Java, which has total 182 landslides occurred from 1979 to 2011, was selected as study area to evaluate three data-driven landslide susceptibility models, i.e., weight of evidence, logistic regression, and artificial neural network. Landslide in the study area is usually affected by rainfall and anthropogenic activities. The landslide typology consists of shallow translational and rotational slide. The elevation, slope, aspect, plan curvature, profile curvature, stream power index, topographic wetness index, distance to river, land use, and distance to road were selected as landslide controlling factors for the analysis. Considering the accuracy and the precision evaluations, the weight of evidence represents considerably the most realistic prediction capacities (79%) when comparing with the logistic regression (72%) and artificial neural network (71%). The linear model shows more powerful result than the nonlinear models because it fits to the area where complete landslide inventory is available, the landscape is not varied, and the occurence of landslide is evenly distributed to the class of controlling factor.     

An InSAR and depth-integrated coupled model for potential landslide hazard assessment

Assessing the hazard of potential landslides is crucial for developing mitigation strategies for landslide disasters. However, accurate assessment of landslide hazard is limited by the lack of landslide inventory maps and difficulty in determining landslide run-out distance. To address these issues, this study developed a novel method combining the InSAR technique with a depth-integrated model. Within this new framework, potential landslides are identified through InSAR and their potential impact areas are subsequently estimated using the depth-integrated model. To evaluate its capability, the proposed method was applied to a landslide event that occurred on November 3, 2018 in Baige village, Tibet, China. The simulated results show that the area with a probability of more than 50% to be affected by landslides matched the real trimlines of the landslide and that the accuracy of the proposed method reached 85.65%. Furthermore, the main deposit characteristics, such as the location of maximum deposit thickness and the main deposit area, could be captured by the proposed method. Potential landslides in the Baige region were also identified and evaluated. The results indicate that in the event of landslides, the collapsed mass has a high probability to block the Jinsha River. It is therefore necessary to implement field monitoring and prepare hazard mitigation strategies in advance. This study provides new insights for regional-scale landslide hazard management and further contributes to the implementation of landslide risk assessment and reduction activities.

     

基于GIS与WOE-BP模型的滑坡易发性评价

区域滑坡易发性研究对地质灾害风险管理具有重要意义.以往研究中,将多元统计模型与机器学习方法相结合用于滑坡易发性评价的研究较少.以三峡库区万州区为例,首先选取9种指标因子（坡度、坡向、剖面曲率、地表纹理、地层岩性、斜坡结构、地质构造、水系分布及土地利用类型）作为滑坡易发性评价指标.基于证据权模型（weights of evidence,WOE）计算得到的对比度和滑坡面积比与分级面积比的相对大小,对各指标因子进行状态分级;再利用粒子群法优化的BP神经网络模型（PSO-BP）得到各指标因子权重.综合两种模型确定的状态分级权重和指标因子权重（WOE-BP）计算滑坡易发性指数（landslide susceptibility index,LSI）,基于GIS平台得到全区滑坡易发性分区图.结果表明:水系、地层岩性和地质构造是影响万州区滑坡发育的主要指标因子;WOE-BP模型的预测精度为80.8%,优于WOE模型的73.1%和BP神经网络模型的71.6%,可为定量计算指标因子权重和优化滑坡易发性评价提供有效途径.      

A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses

Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.     

汶川八级地震滑坡特征分析

汶川地震诱发的15000多处滑坡明显受地震断裂控制,主要沿龙门山主中央断裂带和后山断裂带展布,沿龙门山主中央断裂带汶川映秀安县高川北川陈家坝平武南坝一线,滑坡面密度大于50%以上,最大可达70%。沿断裂带形成了大量的松动山体,在暴雨期间极易发生滑坡、泥石流灾害,对灾后重建构成严重威胁。据初步调查,汶川地震触发的体积最大的滑坡是位于主中央断裂带上的安县高川大光包滑坡,滑动距离长4500m,滑坡堆积体长2800m,宽1700~2200m,最大厚度达580m,若以平均厚度200m计,体积达11亿m3为我国已发生的单体滑坡之最。与常见滑坡明显不同的是,汶川地震极震区滑坡的滑床往往不具连续完整的滑面,剪出口滑坡特征不明显,呈现明显的尖点突起或边缘突出特征,反映出上部滑体被地震力振动解体,甚至抛掷后与下部滑床边缘发生撞击。以阶型滑坡、凸型滑坡、勺型崩滑、座落型（振胀型）滑坡和巨大滚石5种类型最为典型。根据强震地面运动纪录和大量实例调查表明,在汶川地震极震区,触发滑坡的地震竖向力作用是非常明显的,大量滑坡经历了初始斜坡（风化碎裂岩体）地震抛掷撞击崩裂高速滑流的作用过程。