IMHE OpenIR  > 山地灾害与地表过程重点实验室
基于斜坡单元的区域滑坡分析模型与应用
Alternative TitleThe slope unit-based regional landslide analysis model and application
王凯
Subtype博士
Thesis Advisor韦方强
2018
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Discipline岩土工程
Keyword滑坡 斜坡单元 形态影像学 物理模型 区域滑坡预报
Abstract区域滑坡的预报技术作为一项非工程措施,是滑坡减灾工作的重要措施之一。预报单元的选取是区域滑坡预报的基础,主要有五种:栅格单元(Grid cell)、地域单元(Terrain unit)、特定条件单元(Unique-condition unit)、斜坡单元(Slope unit)、地貌单元(Topological unit)。其中,栅格单元与斜坡单元应用最为广泛。斜坡单元比栅格单元更具物理意义,且预报结果的指导性更为明确。然而,目前斜坡单元的提取以地表水文过程分析方法(以下称传统方法)为主,提取的斜坡单元内部存在坡度突变的问题,无法满足滑坡稳定分析要求的均一性假定。并且,目前基于物理框架的滑坡预警报模型主要依靠安全系数的大小给出滑坡发生与否的确定性预报结果,但区域范围岩土体力学参数的不确定性这一客观事实导致目前物理预报模型不具有发布确定性预报结果的能力。为解决斜坡单元内部坡度突变问题,本文基于滑坡稳定分析的均一性假定,利用地形学与形态影像学,建立了新的斜坡单元提取方法MIA-HSU (Morphological Image Analysis-Homogeneous Slope Unit)。为解决物理模型不具有发布确定性预报结果能力的问题,利用MIA-HSU方法提取的斜坡单元建立了基于物理框架的区域滑坡概率分析模型。基于建立的区域滑坡概率分析模型进行了三峡库区奉节县不同前期降水等级下的区域滑坡评估与预报。通过C#和Fortran混合编程实现了奉节县区域滑坡预报系统的构建和系统应用。通过以上研究,取得了以下研究成果:(1) 基于滑坡分析的均一性基本假定,提出一种新的斜坡单元提取方法MIA-HSU。MIA-HSU方法将斜坡单元定义为三维空间中连续、均质、闭合的小区域。每个小区域具有均一的坡度和坡向,小区域的边界即为地形特征出现微小起伏变化之处。采用形态影像学技术从原始DEM中提取出小区域边界,采用主元分析法提取每个小区域的拟合平面,根据向量相似度准则合并小区域以形成新的斜坡单元。 (2) 云南蒋家沟流域提取结果表明,MIA-HSU方法提取的斜坡单元解决了传统方法提取的斜坡单元存在的坡度突变的问题,所提取的斜坡单元具有更为均一的坡度和坡向。通过地面雷达扫描和野外滑坡台地测量等手段,分别比较传统方法和MIA-HSU方法提取的斜坡单元对浅层与深层滑坡地貌特征的反映能力。蒋家沟流域提取结果表明,传统方法提取的斜坡单元不能反映浅层滑坡的地貌形态。MIA-HSU方法提取的的斜坡单元不仅能够满足坡度均一性需求,而且能够准确反映浅层滑坡的地貌形态。奉节县新铺滑坡与寂静滑坡提取结果表明,传统方法提取的斜坡单元无法反映深层滑坡的地貌特征,MIA-HSU方法提取的斜坡单元能够反映深层滑坡中的滑坡台地和坡度较为均一的区域,地貌学意义更加清晰。 (3) 针对MIA-HSU方法提取的斜坡单元,研究并建立了基于蒙特卡洛方法的滑坡稳定性概率分析模型。利用计算机图形学和地形学建立斜坡单元计算剖面提取方法。然后,利用蒙特卡洛思想建立斜坡单元临界滑面搜索方法。采用概率密度函数反映输入力学参数的不确定性,假定土体在一定的边界范围内服从均匀分布。以塑限和液限下的力学参数作为上下边界,采用蒙特卡洛法在边界范围内随机取值n次,计算n次取值中安全系数小于1所占的比重P,利用比重P来建立滑坡发生概率与输入参数不确定性之间的关联,最终建立起基于物理机制的滑坡概率分析模型。(4) 进行了奉节县不同前期降水等级下的区域滑坡评估工作。首先,对研究区进行高密度的野外取样和室内实验以提高下垫面力学参数取值精度。然后将前期降水分为7个等级:0mm、30 mm、60 mm、90 mm、120 mm、150 mm、170 mm。每个降水等级分别进行重现期5年、10年、20年、50年、100年的区域滑坡评估。评估结果表明,当前期降水小于120mm时,滑坡的发生明显受到降雨频率的控制,随着重现期的增加,潜在不稳定坡体沿着境内长江、朱衣河、梅溪河两侧逐渐向境内纵深持续扩展。当前期降水大于120mm时,随着重现期的增加,不稳定斜坡单元数目增加趋势趋于平缓,降雨频率对滑坡的激发作用不再明显。(5) 进行了奉节县区域滑坡预报工作。根据重庆市气象局提供的2017年3月29—4月7日逐日的雷达估测降水数据和4月8日雷达预报降水数据,对2017年4月8日降水诱发的区域滑坡事件进行预报。预报结果表明,在野外考察查明的45个滑坡点中,预报成功的滑坡点为41个,漏报率8.8%。采用C#和Fortran混合编程,结合多线程并行编程思想构建了奉节县区域滑坡预报系统。利用构建的区域滑坡预报系统对2017年10月3日江北部分地区发生的区域滑坡灾害进行预报,在奉节县国土局提供的21个滑坡灾害点中,共成功预报滑坡点19个,漏报率9.5%。本文的创新性主要体现在:(1)利用形态学等技术手段建立了一种新的斜坡单元提取方法MIA-HSU。该方法提取的斜坡单元具有均一的坡度和坡向,解决了传统方法提取的斜坡单元因内部存在坡度突变而难以满足滑坡稳定分析均一性基本假定的问题。(2)建立了斜坡单元计算剖面提取方法和斜坡单元临界滑面搜索方法,采用概率密度函数反应输入力学参数的不确定性,建立了基于斜坡单元的区域滑坡稳定性概率分析模型,避免了使用不确定的岩土力学参数输入而获得确定性预报结果的问题。
Other AbstractRegional landslide prediction is an effective non-engineering measure for landslide mitigation. The selection of prediction units is principal premise of the regional landslide prediction. At present there are mainly five types of prediction unit: Grid cell, Terrain unit, Unique-condition unit, Slope unit and Topological unit. Among the five type prediction units, the grid cell and slope unit are more widely used. Compared with the grid cell, slope unit allows for better representation of topographic features and the guidance of prediction result is clearer. However, slope units extracted by the hydrological process analysis method (here we called the conventional method) introduces heterogeneity in the degree of slope within each slope unit. The heterogeneity in the slope gradient within each slope unit can’t satisfy the homogenous assumption required by the landslide stability analysis theory and would affect the applicability of physical model-based landslides analysis results. In addition, the physical-based landslide prediction model use safety factor to label the prediction unit as either ‘landslide occurrence or nonoccurence’ and give deterministic prediction results to the public. However, the uncertainties in regional scale geotechnical parameters will result in unacceptable uncertainties of safety factors. Therefore, it’s unappropriate to give deterministic forecasting results when using physical models. To slove the two problems mentioned above, firstly, this paper addresses the lack of slope units adjusted to the assumptions of regional landslide stability analysis and proposes a novel slope unit extraction method MIA-HSU(Morphological Image Analysis-Homogeneous Slope Unit). Then, using the slope units extracted by MIA-HSU method, this paper built the physical-based regional landslide probabilistic analysis model and performed the regional landslide assessment and prediction of the Fengjie country. Finally, this paper constructed the landslide probabilistic forecasting system of Fengjie country using C# and Fortran language hybrid programming.The main achievements of this paper are summrized as follows: (1) The MIA-HSU method defined the slope unit as a small, closed, homogeneous region in three-dimensional space. Each slope unit was homogeneous in slope and orientation. Then, the morphological image analysis method (logical algorithms such as expansion and erosion) was used to extract the boundaries of homogeneous regions; the principal component analysis was used to extract the fitting plane of the spatial point set involved in each closed region; the vectors normal to the fitting plane at adjacent small areas were compared, and small areas meeting the vector similarity criterion were merged into slope unit.(2) Slope unit extract result of the Jiangjia gully indicated that slope unit extracted by MIA-HSU method overcame the defect of sudden changes in slope gradient and present more uniform slope and aspect. By means of ground LiDAR scan and GPS measurement, this paper extracted the boundaries of shallow landslides in Jiangjia gulley and landslide platforms of deep seated landslides in the Fengjie country. Then, this paper compared the geomorphic features of natural landslide and the boundaries of slope unit extracted by MIA-HSU method and conventional method, respectively. Result indicated that slope unit extracted by conventional method can’t reflect the geomorphic features of shallow and deep seated landslides. Slope unit extracted by MIA-HSU method could reflect the landslide platforms and geomorphic features of shallow and deep seated landslides.(3) For the slope unit extracted by MIA-HSU method, this paper studied and built the landslide probabilistic analysis model using Monte Carlo technique. Firstly, according to the computer graphics and geomorphology, this paper proposed the slope unit profile line extraction method; then, we studied slope unit critical slip surface search method using Monte Carlo random search technique; thirdly, this paper proposed the use of Monte Carlo method to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The plastic and liquid limit were taken as the upper and lower borders of intervals. Then, Monte Carlo method was used to randomly extract cohesion force and internal friction angles from the two intervals n times in any forecasting step. Finally, we calculated the failure probability P by the number of safety factor lower than 1 in the n different states and the ratio P is used to link the landslide probability to the uncertain soil mechanical parameters. By this way, the landslide probabilistic analysis model was established.(4) This paper performed the regional landslide assessment under different antecedent effective rainfall levels. Firstly, we took high-density soil samples and conducted laboratory experiments to improve the precision of geotechnical parameter. The antecedent effective rainfall was classified into seven grades: 0mm, 30 mm, 60 mm, 90 mm, 120 mm, 150 mm, 170 mm. For each rainfall grades, we set five return periods: 5 year, 10 year, 20 year, 50 year, 100 year. Assessment result indicated that the critical antecedent precipitation was 120mm. When the antecedent precipitation was lower than 120mm, the landslides occurance was influenced by rainfall frequency obviously. With the increase of return periods, the potential unstable slopes expanded continuously along the river systems. When the antecedent precipitation exceeded than 120mm, with the increase of return periods, the increasing trend of unstable slopes became flat. (5) According to the quantative precipitation estimation data from March 29th to April 7th and the quantative precipitation forecast data on April 8th provided by the Chongqing Meteorological Bureau, This paper performed regional landslide prediction of Fengjie country. The prediction result indicated that the successful predicted landslides were 41 in the total 45 landslide events founded by field investigation. The missing-prediction rate was 8.8%. Then, used C# and Fortran language hybrid programming, we built the regional landslide probabilistic forecasting system and applied the forecasting system to predict the landslide events on October 3th of Fengjie country. The prediction result indicated that the successful predicated landslides were 19 in the total 21 landslide events and the missing-prediction rate was 9.5%.The innovations of this paper can be described as follows:(1) Based on the morphological image analysis, this paper proposed a new slope unit extraction method MIA-HSU. The slope unit extracted by MIA-HSU method slove the problem of sudden changes in slope gradient within each slope unit extracted by conventional method. The new slope unit presents homogeneous slope and aspect and could satisfy the homogenous assumption required by landslide stability analysis.(2) Using the slope unit extracted by MIA-HSU method, this paper proposed the slope unit profile extraction method and critical slip surface search method. Using the probability density function to reflect the uncertainties of geotechnical paramters, this paper established the physical-based regional landslide probabilistic analysis model and solved the problem that deterministric forecasting result derived from physical models can’t reflect the uncertainties of soil mechnical paramters.
Pages172
Language中文
Document Type学位论文
Identifierhttp://ir.imde.ac.cn/handle/131551/24768
Collection山地灾害与地表过程重点实验室
Affiliation中国科学院成都山地灾害与环境研究所
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
王凯. 基于斜坡单元的区域滑坡分析模型与应用[D]. 北京. 中国科学院大学,2018.
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