滑坡体三维地质建模与可视化分析

针对滑坡地质研究的自身特点,提出了面向滑坡地质体三维建模的NURBS-BRep混合数据结构和地质结构单元实体构造技术.通过对滑坡基础地质数据的预处理、滑坡数字地形和滑动面的三维建模、地质结构面的系统构造、地质结构体的生成和显示,形成了一套完整的滑坡三维地质模型的构建方法.将该方法应用于某水库滑坡,建立了相应的三维滑坡地质模型,并基于此模型进行了三维剖切分析、数字钻孔、等值线生成、滑块自动剖分、滑坡失稳可视化动态模拟和滑坡体方量精确计算等一系列实用的可视化分析,为滑坡稳定性的准确计算和客观评价提供了有力的支持.     

滑坡可视化技术研究

论述了可视化技术在滑坡研究中的应用,阐述了滑坡的三维地质模型和数字地面模型的构建和可视化方法,提出了今后研究过程中尚待解决的问题。     

降雨和库水位联合作用下库岸滑坡模型试验研究

受库区水位波动和降雨影响,库岸大量老滑坡体变形加剧,地质灾害问题十分突出。为研究库岸滑坡影响因素、变形演化规律及失稳条件,以大型物理模型试验为手段,选取三峡库区黄土坡滑坡临江Ⅰ号崩滑体为对象,通过考虑水位波动、降雨及其组合作用等诱发因素,开展了一系列的库岸滑坡模型试验研究。试验结果表明：水位升降,变形主要集中于模型坡体前缘,其中,水位抬升过程中,滑坡模型变形较小,变形加速阶段出现于水位下降期间,且变形速率与水位下降速率成正比,即临江Ⅰ号崩滑体为典型的动水压力型滑坡;降雨影响下坡体变形在时间和空间上存在明显分区现象,时间上,变形发展主要集中于坡体浅表层饱和之后,即短时降雨对坡体变形未产生显著影响,空间上,坡体前缘和后缘变形剧烈;库水位下降和强降雨联合作用下坡体前缘产生局部流滑破坏,并溯源发展至前缘整体破坏,为典型的牵引式破坏模式。试验揭示处于临滑阶段坡体,其孔隙水压力、土压力变化呈现异常频繁的波动现象,可为滑坡预警预报提供一定参考依据。     

一种含断层的复杂层状地质体三维自动构模方法研究

为了使复杂地质体建模过程趋向于自动化、简单化,在分析了复杂层状地质体的空间信息特征的基础上,提出了一种针对含断层的层状地质体的自动构建方法。该方法先对地质钻孔资料、地质报告图进行预处理,从而获得离散的数据点;然后选择基于表面模型的建模方法,根据从钻孔分层信息到剖面图再到地层面的建模流程,分别构建出地质体的顶、底两个地层表面模型;根据空间拓扑关系处理断层结构,将断层以两条首尾相连的约束线嵌入地面模型中;最后使用轮廓线拼接法生成侧面,将顶、底、侧面缝合成封闭的三维地层体模型。利用自主开发的三维可视化系统对该方法进行验证实现,并成功应用于山西省某煤田矿区的三维地质建模中,实践表明,该建模方法高效快速,自动化程度高,所构建的地质体模型表达复杂地质结构的效果良好。     

基于改进三棱柱模型的复杂地质体3D建模方法

建模方法是三维地质模型及其可视化研究的核心内容。针对连续体终止、错断位移等复杂地质体3D地质建摸的需要，本文对现有的三棱柱数据模型进行了改进，构建了完整的数据结构及其初步的拓扑关系，提出了基于改进三棱柱模型的连续体和不连续体的建摸算法，使其在处理地层尖灭、断层等复杂地质问题时更为灵活、适应面更广。     

Yielding mechanism of shallow mass movements in completely decomposed Norwood Tuff: the Zigzag Sign landslide,Utah

Over the past decade, land development activities on hillsides in northern Utah have resulted in a significant increase in landslide activity throughout the region. The majority of recent landslides are shallow and they occurred on cut gentle slopes especially during spring and early summer due to snowmelt induced elevated groundwater tables. The geologic material documented at numerous landslide sites is a soft gray-green completely decomposed Norwood Tuff. The present study addresses the mechanism of a shallow landslide in completely decomposed Norwood Tuff based on field, laboratory and numerical investigations. Detailed slope surface geometry obtained from laser-scan surveys together with strength and stress–strain parameters derived from laboratory triaxial tests on undisturbed samples of completely decomposed Norwood Tuff collected from the landslide site are employed with finite-element modeling to examine the effects of ground surface deformation patterns on the yielding behavior of the slide mass. The numerical results indicate a gradual retreat of the yield zone with progressive landslide deformation, which eventually becomes concentrated within the accumulation zone of the landslide, compared to a well-developed yield zone within the entire slide mass at the onset of landslide movement. Limit equilibrium stability analyses along potential sliding surfaces of extent limited within the yield zone of the displaced slide mass produce lower safety factors than an analysis based on the original sliding surface comprising the entire slide mass.     

类软土滑坡滑动带的确定

类软土是一种特殊意义上的软土,它与一般沉积软土比较,在形成原因、分布范围、物质组成等其他特性上有区别;由其构成的类软土滑坡则具有普通滑坡不具有的特征。以京珠高速粤南段K108类滑坡为例,介绍了滑坡的地层岩性、构造特征、水文地质特征;应用位移监测方法查明滑坡有五层滑动带,滑面成近于圆弧形的勺形,并对滑坡的产生原因及滑动过程进行了分析,得出降雨和边坡的开挖是导致滑坡的直接诱因。     

滑坡位移灰色预测模型的VB编程实现

灰色模型在社会科学、自然科学的许多方面已得到广泛的应用，并取得了一系列重大成果。在滑坡地质灾害研究方面，灰色模型多用于滑坡变形的中长期预测预报，且精度较高。文章在介绍分析了灰色预测模型GM(1，1)的基本原理基础上，开发了用VB语言编写的滑坡位移灰色预测软件，解决了笔算困难问题。实例验证了该模型用于预测滑坡位移值的适用性及软件的可靠性。VB编程能够很好地与其它软件(GIS软件)集成，在其它专业软件中使用，有聋于滑坡地质灾害的研究。     

基于H-P滤波法、ARIMA和VAR模型的库区滑坡位移综合预测

受库水位涨落及降雨等影响,库区滑坡位移表现出明显的周期性。基于位移时间序列分析,将滑坡监测位移分解为趋势项与周期项之和。趋势项反映滑坡变形的长期趋势,其主要受滑坡本身地质结构等因素影响。周期项反映滑坡变形的波动性,其主要受外部因素影响。以三峡库区巫山塔坪滑坡为例,考虑长江水位与降雨量影响,采用H-P滤波法从滑坡位移中分解出趋势项及周期项,利用差分自回归滑动平均模型（ARIMA）对趋势项进行平稳处理并计算趋势项预测值,利用向量自回归模型（VAR）计算周期项预测值。趋势项预测值与周期项预测值之和为滑坡位移预测值。与实际监测值及多种方法分析比较,表明综合预测所得结果能较好反映滑坡变形的趋势性和波动性,位移预测效果较好。     

复杂矿区三维地质可视化及数值模型构建

复杂三维地质可视化和数值模型的构建是采矿、岩土及水利水电工程中常常遇到的关键问题。针对数值模拟软件在复杂矿区地质体模型构建及单元网格划分等前处理问题上存在的不直观、工作量大等缺点,结合矿业软件在复杂三维地质体建模的强大优势,在研究线框模型和块体模型差异的基础上,提出了基于线框模型的复杂矿区三维地质可视化及数值模型构建技术。根据线框模型特点不同,以地表和采空区模型为型,分别阐述了基于Surpac的表面模型和实体模型无缝转换成MIDAS/GTS数值模型的有效方法,并将该技术成功应用于冬瓜山铜矿复杂采空区群围岩稳定性研究中。结果表明,该方法是可行、有效的,从而为矿山开采设计、安全分析及复杂地质体结构的岩土工程数值模拟分析提供了一种新的建模方法。     

Slope stability evaluation using Back Propagation Neural Networks

The Yudonghe landslide, located in western Hubei Province of China, consists of eastern and western subunits as well as a main landslide mass with upper and lower slip surfaces. As an important landslide close to Shuibuya Dam on the Qing River, its stability is crucial, as the slide might reactivate because of a change in ground-water level caused by filling of the Shuibuya Reservoir. Existing weakness zones, growth of ruptures, the downslope attitude of geologic strata, and water infiltration, which reduced the strength of rocks and soils, have been found to be the most important factors contributing to the Yudonghe landslide. With regard to the landslide processes, it can be noted that the original large-scale slide activity was due to erosion by the Qing River, the second sliding resulted from the fall of blocks from the head scarp, and the final activity was the growth of the eastern and western secondary slides. A base failure was the main type of slope movement, however, it was obvious that more than one sliding event occurred, as inferred from striations and fractures detected by microstructure analysis of soils along the failure surfaces. Slope instability was evaluated by the method of Back Propagation Neural Networks (BPNN), in which a four-layer BPNN model with five input nodes, two hidden layers, and two output nodes was constructed using a training data set of landslide samples throughout the Qing River area. The predicted results of this analysis showed that the factor of safety was 1.10, which indicates that the Yudonghe landslide is currently in a marginally stable condition.     

三维地质结构模型精度评估理论与误差修正方法研究

三维地质模型精度评估与误差修正问题已成为制约三维地质模拟技术深入发展应用的瓶颈。在综合国内外研究现状与发展趋势的基础上,提出了三维地质结构模型精度评估、误差检测、动态修正的总体研究框架。在模型精度评估方面,提出分别构建三维地质结构模型精度评估的一般理论模型、面向特定地质体的实际操作模型和地质结构构造不确定性的三维空间分布模型的研究思路,指出应重点研究地质实体自身特性、三维地质建模方法对三维地质结构模型精度的影响,解决由一般地质界面的内插误差和特殊地质体的外推误差引起的精度评估问题。在模型误差修正方面,提出基于建模初始数据的模型误差修正方法和基于建模中间结果的模型误差修正方法,在具体实现时,引入“数据 模型的可视化交互技术”。这些研究成果为建立一套完整的三维地质结构模型精度评估与误差修正的理论体系和方法体系奠定了基础,有助于完善复杂地质条件下三维地质模拟的方法与技术。     

基于推广Bayes方法参数优化的滑坡稳定性评价

传统的极限平衡法在确定的计算工况下要求抗剪强度参数为常数,但由于各种参数的随机性、测试的误差、地质体的不均匀性、外界因素变化的随机性等特点,对滑坡稳定性评价起主要作用的抗剪强度参数从严格意义上来说均是随时间变化的随机变量,这就需要用破坏概率模型等非传统方法对滑坡进行稳定性评价。破坏概率模型的关键是滑坡抗剪强度参数的概率分布函数,但是在实际工程中,由于试验数据太少无法准确确定抗剪强度参数概率分布函数。本文提出了以区域统计规律得出的概率分布函数为先验分布函数,然后用推广Bayes方法确定该滑坡的概率分布函数,并以里沱河滑坡为例用破坏概率模型对其进行稳定性评价。     

Assessment of shallow landslides from Hurricane Mitch in central America using a physically based model

Shallow landslides induced by heavy rainfall events represent one of the most disastrous hazards in mountainous regions because of their high frequency and rapid mobility. Recent advancements in the availability and accessibility of remote sensing data, including topography, land cover and precipitation products, allow landslide hazard assessment to be considered at larger spatial scales. A theoretical framework for a landslide forecasting system was prototyped in this study using several remotely sensed and surface parameters. The applied physical model SLope-Infiltration-Distributed Equilibrium (SLIDE) takes into account some simplified hypotheses on water infiltration and defines a direct relation between factor of safety and the rainfall depth on an infinite slope. This prototype model is applied to a case study in Honduras during Hurricane Mitch in 1998. Two study areas were selected where a high density of shallow landslides occurred, covering approximately 1,200 km². The results were quantitatively evaluated using landslide inventory data compiled by the United States Geological Survey (USGS) following Hurricane Mitch’s landfall. The agreement between the SLIDE modeling results and landslide observations demonstrates good predictive skill and suggests that this framework could serve as a potential tool for the future early landslide warning systems. Results show that within the two study areas, the values of rates of successful estimation of slope failure locations reached as high as 78 and 75%, while the error indices were 35 and 49%. Despite positive model performance, the SLIDE model is limited by several assumptions including using general parameter calibration rather than in situ tests and neglecting geologic information. Advantages and limitations of this physically based model are discussed with respect to future applications of landslide assessment and prediction over large scales.     

SKUA基岩三维地质建模

以天津平原地区某个工区为例,介绍了三维地质模型建模的数据准备,SKUA建模的流程以及模型分析,共使用28条断层,6个地层的地质构造图,3条地质剖面构建了地质结构模型,表明SKUA建模是高效的和实用的。     

汶川5.12地震发震机理的数值模拟

将围岩看成弹性体, 断层看成具有应变软化的弹塑性体, 断层和围岩组成统一的地质介质系统。在给定的地应力、孔隙压力及边界位移的作用下, 应力逐渐积累, 当达到断层强度时, 断层进入塑性状态, 应变随之软化, 应力突然下降, 能量突然释放, 形成地震。根据这个原理, 利用有限差分FLAC软件, 对汶川5.12地震的机理进行了模拟。模拟结果证实:汶川5.12地震是在青藏高原侧向挤压, 四川盆地阻挡, 使龙门山断裂带受到缓慢增加的挤压应力场作用下形成的。计算结果给出了应力降、能量释放量、断层错动量、地震同震位移、震前位移、地震复发周期等重要参数, 结果与野外调查及其它资料给出的数据具有较好的一致性。      

浙江省永嘉县滑坡灾害危险性区划

永嘉县是浙江省滑坡灾害发生频繁的区县之一,其滑坡受地质、地形和人类工程活动等因素的影响。本文根据永嘉县滑坡灾害分布情况,选择了影响滑坡分布的主要因素,将各种因子归一化处理后转换成相同分辨率的定量数据,选择了逻辑回归分析模型和信息量模型进行滑坡灾害危险性评价。在逻辑回归模型中,利用SPSS软件,通过逐步回归分析筛选出影响滑坡的最直接的因子,计算出各个因子的回归系数,得到逻辑回归方程,据此编制了危险性预测分区图。在信息量模型中,通过MAPGIS软件及其二次开发的信息量模型,对永嘉县滑坡灾害进行了危险性区划,并依信息量法的结果编制了该区的危险性预测分区图。两种方法所编制的危险性分区图中高危险区和中危险区重合率达到了87%,具有很高的一致性,起到了相互验证的作用,为滑坡的有效防治提供了依据。最后根据"云娜"台风期间永嘉县实际灾害发生情况的资料分析,新灾害点绝大部分落在危险性预测区中的高危险区,表明模型的预测准确率很高。     

贵州安顺-六枝-纳雍地区地质灾害特点

贵州安顺-六枝一带是贵州省地质灾害多发地区.主要地质灾害为滑坡、崩塌、泥石流.调查发现地质灾害共有785处,其中滑坡372处,崩塌246处,地裂缝77处,泥石流47处,岩溶塌陷11处,洪涝洼地32处.纳雍县的岩脚寨的的基岩顺层滑坡、六枝旧院的切层滑坡、六枝特区龙潭乡朱家寨的松散层滑坡、纳雍县菁口寨崩塌、纳雍县垮山口泥石沟的泥石流等是区内不同类型的地质灾害实例.纳雍县的岩脚寨地区有几个村寨、六枝旧院坐落在古滑坡体上,有很大的隐患,建议通过移民方式解决这个问题.     

青海玉树M_S7.1地震发震过程的数值模拟

根据玉树地区的地应力场、速度场和断层展布,对青海玉树2010年4月14日MS7.1级地震发震机理进行了数值模拟。将围岩看成弹性体,断层看成具有应变软化的弹塑性体,断层和围岩组成统一的地质介质系统。在地应力、孔隙压力及边界位移的作用下,应力逐渐积累,当达到断层摩擦破坏强度时,断层产生应变软化,断层突然滑动,能量突然释放,应力突然下降,形成地震。模拟结果表明:玉树7.1级地震是在印度板块向北推挤,青藏高原向东南侧向挤压,在玉树地区形成主压应力为北东80°方向的水平应力场,使甘孜-玉树断裂带产生左旋走滑错动形成的。计算结果给出了应力降、能量释放量、断层走滑错动量、地震复发周期、应力积累速度等重要参数,模拟结果与野外调查资料具有较好的一致性。     

Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province,China

The rainfall can contribute significantly to landslide events, especially in hilly areas. The landslide susceptibility map (LSM) usually helps to mitigate disasters. However, how to accurately predict the susceptibility of landslides is still a difficult point in the field of disaster research. In this study, five advanced machine learning technologies (MLTs), including the Light Gradient Boosting Machine, extreme gradient boost, categorical boosting (CatBoost), support vector machine, and random forest, are utilized to landslide susceptibility modeling and their capabilities are compared through evaluation indicators. The northern part of Yanping, Fujian Province, China, is selected as the research object, because this area experienced mass landslide events due to extremely heavy rainfall in June 2010, resulting in many casualties and a large number of public facilities destroyed. The influencing factors for landslides, namely topographic, hydrological, geologic and human activities, are prepared from various data sources based on the availability. Through the analysis of the actual situation in the study area, 13 suitable landslide condition factors are considered and the availability of relevant factors is checked according to the multicollinearity test. The landslide inventory including 631 samples in this study area is obtained from historical information, satellite data in Google earth and performed field surveys. The landslide inventory is randomly divided into two datasets for model training and testing with a 7:3 ratio. The area under the curve of ROC, accuracy rate, Kappa index and F1 score are applied to compare the MLTs capabilities. In this paper, the results of factor importance analysis show that the first three important condition factors are the distance to faults, the distance to drainages and the slope. According to the LSMs, in the study area, the central and western regions are at high and very high landslide susceptibility levels, while almost all the eastern and northeastern regions are at medium and low landslide susceptibility levels. The CatBoost model is a very promising technology in landslide research according to the evaluation results, which means that for landslide susceptibility research, gradient boosting algorithms may get more accurate results and show better prospects in the future. Finally, the results of this paper will contribute to environmental protection to a certain extent.