拉线式滑坡地表位移实时监测系统应用实例

拉线式滑坡地表位移实时监测系统是测量地表相对位移的监测仪器,在很多滑坡中已得到实际应用。该系统的特点是将传统的拉线式测量原理与现代通讯技术有机结合,由数据采集系统,数据处理中心和客户端这三大部分组成。当测点发生位移时,所连接的钢丝线将随之移动发生变化,这一信息由角位移传感器记录并转化为相应的电信号,数据采集系统将电信号转换成数字信号,并利用现有的移动通信网络将信号传输到数据处理中心。最后利用编制的软件进行数据分析,给出被测点的位移随时间变化的规律。同时,数据处理中心可以根据现场情况,对现场的数据采集频率进行更改。通过对凉水井滑坡实时监测数据的分析和对比,对该滑坡的危险性等级进行了评估和险情预警,证明该系统不受地理环境、气候等因素的限制,并说明了此种监测方法运用于滑坡实时监测具有很高的实用价值。但是该系统还需要作进一步改进,以克服自身存在的一些缺陷。     

基于数据挖掘技术的白水河滑坡多场信息关联准则分析

为探究滑坡多场监测数据间的关联准则,采用数据挖掘技术中的两步聚类法与Apriori算法,开展滑坡多场信息关联准则研究。以三峡库区白水河滑坡为例,分析ZG93监测点于2003年6月—2016年12月期间的监测数据,选取影响滑坡变形的主要诱发因子,采用两步聚类法对不同的影响因子进行预聚类和聚类,将数值型变量转化为离散型变量后,应用Apriori算法进行处理,生成满足最小置信度的关联准则,建立白水河滑坡多场耦合作用模式下的影响因子与滑坡位移变形关联准则判据。研究表明,关联准则对于滑坡灾害的变形分析具有重要的意义,数据挖掘技术可较好地应用于三峡库区地质灾害位移预测预报中。     

基于时间序列与人工蜂群支持向量机的滑坡位移预测研究

总结以往滑坡预测方法存在的诸多不足,针对滑坡监测位移-时间曲线特点,本文提出了一种基于时间序列的人工蜂群算法（ABC）与支持向量回归机（SVR）相结合的滑坡位移预测方法。以三峡库区白水河滑坡为例,通过对滑坡位移、降雨、库水位等因素的分析,研究影响滑坡位移变化的因素。用时间序列加法模型和移动平均法将滑坡位移分解为趋势项和周期项。以多项式最小二乘法拟合滑坡位移趋势项,用人工蜂群支持向量机模型对滑坡位移周期项进行训练和预测。通过灰色系统关联分析法计算多项因子与滑坡位移周期项之间的关联性。最终的滑坡总位移预测值为周期项预测值与趋势项预测值之和。与BP神经网络、PSO-SVR模型方法相比,该方法在滑坡位移预测中有更高的精度,在防灾减灾工作中有较好的推广应用前景。     

三峡树坪滑坡动力学的有限元模拟

三峡工程是迄今为止最大的水利工程,对库区滑坡灾害的监测和机制研究一直是重要的研究课题。本文利用Terra SAR-X的强度图进行相关计算,求解出2009年5月20日至8月5日期间三峡树坪滑坡的形变场。该形变场特征和树坪滑坡体的地形特征吻合甚好,位移大小、方向和三峡大学对滑坡体的野外观测结果基本吻合。以此高精度位移场为外部约束,结合野外观测资料对滑坡体介质力学性质进行分类并选取边界条件,利用有限元方法对滑坡活动进行动力学计算模拟。计算过程中对滑坡体的滑动面形状、因降雨引起材料参数变化和三峡水库水位等因素分别反演和调整,得出符合其变形和发展过程的滑坡动力学特征。发现软弱带的物性参数决定滑坡体总体滑动量,滑坡体的物性参数决定位移分布的峰值位置。在确定了滑坡动力学特征之后,进一步讨论降雨和库区水位下降对滑坡产生的贡献权重,得出降雨是树坪滑坡的决定因素。     

基于光纤监测和PSO-SVM模型的马家沟滑坡深部位移预测研究

滑坡位移预测效果一方面取决于预测模型的优劣,另一方面取决于野外监测数据的质量。针对目前滑坡常规监测技术与评价方法的不足,本文采用光纤监测技术、监测数据与PSO-SVM预测模型相结合的评价方法,对三峡马家沟Ⅰ号滑坡的深部位移进行了预测;通过对320个滑坡深部位移光纤监测数据分析,基于时间序列法,将滑坡位移分为趋势性位移和波动性位移;趋势性位移采用拟合法进行预测,波动性位移采用PSO-SVM模型进行预测;最后将趋势项和波动项位移预测值叠加得到累积位移的预测值。研究结果表明,PSO-SVM模型对波动性位移预测的均方根误差0.51 mm,平均绝对百分误差0.37 mm,能准确预测滑坡波动项位移;累积位移预测值与实测值的相关系数为0.98,均方根误差为0.54 mm,预测效果较好,可以用来对滑坡深部位移进行短期预测。     

地震滑坡危险性概念和基于力学模型的评估方法探讨

在我国大陆地区运用基于力学模型的Newmark位移分析方法开展地震滑坡危险性定量评估,尚处在起步阶段.为了进一步明确地震滑坡危险性概念和改进推广基于力学模型的评估方法,首先阐明了狭义的地震滑坡危险性预测评估与震后反演评估的关系; 同时为了应对地震应急、震后重建及潜在地震条件下的不同评估需求,初步提出了广义的地震滑坡危险性评估框架.随后申述了基于Newmark位移分析的地震滑坡危险性评估方法的理论基础、方法分类及最新进展,并以汶川地震滑坡危险性快速评估为例,剖析了目前影响评估有效性的不确定性及空间数据质量等问题,指出了基于力学模型的地震滑坡危险性评估方法的改进方向.建议开展潜在地震及其诱发滑坡危险性的耦合评估,建立适用于我国大陆地区地震滑坡位移分析的经验模型,以便为国家层面的地震滑坡危险性区划服务.     

Measuring precursory movements of the recent Xinmo landslide in Mao County,China with Sentinel-1 and ALOS-2 PALSAR-2 datasets

The Xinmo landslide occurred in the early morning of 24 June 2017 at about 5:38 am local time. This catastrophic event caused enormous casualties and huge economic losses in Xinmo Village, Mao County, Sichuan Province, China. In this study, Synthetic Aperture Radar (SAR) datasets acquired by X-band TerraSAR-X, Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard the Advanced Land Observing Satellite-2 (ALOS-2), and C-band Sentinel-1 over the disaster area were collected and analyzed to characterize this landslide. The high-resolution TerraSAR-X intensity images were used to evaluate the landslide disaster and delineate the sliding area. Afterwards, two ALOS-2 PALSAR-2 image pairs and a stack of 45 Sentinel-1 images were processed to detect precursory movements of the landslide surface, using the conventional differential InSAR (DInSAR) method and advanced time series InSAR analysis. The unstable source area near the ridge was identified from the displacement rate map derived from Sentinel-1 datasets. The maximum displacement rate detected at the source area was ?35mm/year along the radar line of sight (LOS) direction. The time series of LOS displacements over 2 years presents an easily discerned seasonal evolution pattern. In particular, a sudden acceleration of the displacement, dozens of days before the collapse was clearly captured by the Sentinel-1 observations, which might suggest that early warning of landslide disasters is possible given the availability of operational SAR data acquired in frequent repeat-pass mode, such as the Sentinel-1 twin-satellite constellation.     

New Zealand’s National Landslide Database

A New Zealand Landslide Database has been developed to hold all of New Zealand’s landslide data and provide factual data for use in landslide hazard and risk assessment, including a probabilistic landslide hazard model for New Zealand, which is currently being developed by GNS Science. Design of a national Landslide Database for New Zealand required consideration of existing landslide data stored in a variety of digital formats and future data yet to be collected. Pre-existing landslide datasets were developed and populated with data reflecting the needs of the landslide or hazard project, and the database structures of the time. Bringing these data into a single database required a new structure capable of containing landslide information at a variety of scales and accuracy, with many different attributes. A unified data model was developed to enable the landslide database to be a repository for New Zealand landslides, irrespective of scale and method of capture. Along with landslide locations, the database may contain information on the timing of landslide events, the type of landslide, the triggering event, volume and area data, and impacts (consequences) for each landslide when this information is available. Information from contributing datasets include a variety of sources including aerial photograph interpretation, field reconnaissance and media accounts. There are currently 22,575 landslide records in the database that include point locations, polygons of landslide source and deposit areas, and linear landslide features. Access to all landslide data is provided with a web application accessible via the Internet. This web application has been developed in-house and is based on open-source software such as the underlying relational database (PostGIS) and the map generating Web Map Server (GeoServer). Future work is to develop automated data-upload routines and mobile applications to allow people to report landslides, adopting a consistent framework.     

位移监测在边坡治理工程中的应用

位移监测可为分析滑坡的结构和成因、评价预测滑坡的稳定性及发展趋势等提供重要信息。实例分析表明,将位移监测资料与斜坡变形破坏现象结合起来,能准确地判定斜坡的变形阶段,评价和预测边坡施工及其使用过程中边坡的稳定状况。     

时序InSAR技术三峡库区藕塘滑坡稳定性监测与状态更新

坡体表面形变是表征坡体稳定性的重要信息,因此,非常有必要对滑坡多发区域进行时序常规变形监测.近年来,星载合成孔径雷达数据由于其覆盖范围大、形变监测精度高的特点,被越来越多的用于山区滑坡识别与探测.首先介绍了联合分布式目标与点目标的时序InSAR方法,并将该方法应用于分析覆盖三峡藕塘滑坡的2007年至2011年的19景ALOS PALSAR数据和2015年至2018年的47景Sentinel-1数据,提取了数据覆盖时间段内的藕塘地区的变形速率.发现相比于2007年至2011年,2015年至2018年新增三处不稳定斜坡.进一步对滑坡的时序变形分析表明,降雨和水位变化是坡体稳定性最大的两个影响因素.实验证明时序InSAR方法可以作为常规形变手段来识别与监测三峡库区等地区潜在的滑坡,为防灾减灾提供支持与依据.      

滑坡动态力学监测及破坏过程案例分析

边坡临滑预警一直是地灾研究的难点与热点问题。本文采用一种力学监测方法(牛顿力监测)对雅安宝兴县某傍山公路边坡进行监测,该边坡位于唐包滑坡老滑坡体下缘边界处。经过4个月的连续监测,获取了大量监测数据,并成功预报了两次局部滑坡。本文首先整合牛顿力监测数据和降雨量监测数据,再将监测曲线与滑坡演化过程进行对比分析,揭示滑坡过程中的力学演化规律,对降雨条件下诱发滑坡的原因进行了分析。然后对牛顿力监测预警成功的案例其临滑预警时长与滑坡体量间的关系进行拟合,发现存在明显的正相关关系。最后讨论了牛顿力监测方法与斋藤模型之间的关联性以及优劣势,并根据各自的特点提出了由面到点的监测预警思路。通过分析,牛顿力监测曲线与滑坡演化过程能较好对应,并可将土质滑坡分为3阶段:(1)牛顿力上升阶段; (2)牛顿力突降阶段; (3)滑坡阶段。本文为牛顿力监测系统的推广提供了实践经验,并为力学监测与位移监测结合的研究提供一个新的思路。     

Accuracy assessment of InSAR derived input maps for landslide susceptibility analysis: a case study from the Swiss Alps

In recent years SAR interferometry has become a widely used technique for measuring altitude and displacement of the surface of the earth. Both these capabilities are highly relevant for landslide susceptibility studies. Although there are many problems that make the use of SAR interferometry less suitable for landslide inventory mapping, it’s use in landslide monitoring and in the generation of input maps for landslide susceptibility assessment looks very promising. The present work attempts to evaluate the usefulness and limitations of this technique based on a case study in the Swiss Alps. Input maps were generated from ERS repeat pass data using SAR interferometry. A land cover map has been generated by image classification of multi-temporal SAR intensity images. An InSAR DEM was generated and a number of maps were derived from it, such as slope-, aspect, altitude- and slope form classes. These maps were used to generate landslide and rockfall susceptibility maps, which give fairly well acceptable results. However, a comparison of the InSAR DEM with the conventional Swisstopo DEM, indicated significant errors in the absolute height and slope angles derived from InSAR, especially along the ridges and in the valleys. These errors are caused by low coherence mostly due to layover and shadow effects. Visual comparison of stereo images created from hillshading maps and corresponding DEMs demonstrate that a considerable amount of topographic details have been lost in the InSAR-derived DEM. It is concluded that InSAR derived input maps are not ideal for landslide susceptibility assessment, but could be used if more accurate data is lacking.     

数字网格摄影测量法及其在滑坡模型试验中的应用

综合考虑自动网格法和摄影测量技术各自的优缺点,提出了数字网格摄影测量法。介绍了该方法的原理与步骤,并将其应用到室内某滑坡模型试验的位移测试中。试验证明：数字网格摄影测量法可以快速、准确对模型体观测点位移进行量测,精度达到次毫米级,是一种比较理想的滑坡模型位移测试方法。     

The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data

Post-event Interferometric Synthetic Aperture Radar (InSAR) analysis on a stack of 45 C-band SAR images acquired by the ESA Sentinel-1 satellites from 9 October 2014 to 19 June 2017 allowed the identification of a clear precursory deformation signal for the Maoxian landslide (Mao County, Sichuan Province, China). The landslide occurred in the early morning of 24 June 2017 and killed more than 100 people in the village of Xinmo. Sentinel-1 images have been processed through an advanced multi-interferogram analysis capable of maximising the density of measurement points, generating ground deformation maps and displacement time series for an area of 460 km² straddling the Minjiang River and the Songping Gully. InSAR data clearly show the precursors of the slope failure in the source area of the Maoxian landslide, with a maximum displacement rate detected of 27 mm/year along the line of sight of the satellite. Deformation time series of measurement points identified within the main scarp of the landslide exhibit an acceleration starting from April 2017. A detailed time series analysis leads to the classification of different deformation behaviours. The Fukuzono method for forecasting the time of failure appear to be applicable to the displacement data exhibiting progressive acceleration. Results suggest that satellite radar data, systematically acquired over large areas with short revisiting time, could be used not only as a tool for mapping unstable areas, but also for landslide monitoring, at least for some typologies of sliding phenomena.     

藏东冻土区滑坡形变时序InSAR监测分析

受气候暖湿化和冻融作用的影响,近年来西藏东部地区的山体滑坡多发频发,对人民生命财产安全造成严重威胁,制约了当地经济社会发展,因此,迫切需要利用有效手段对滑坡灾害隐患开展大范围调查与早期识别。以藏东317国道矮拉山地区为例,利用小基线集时序InSAR（Interferometric Synthetic Aperture Radar）技术,分别对2017年3月—2019年7月期间Sentinel-1A SAR升、降轨数据集进行地表形变监测分析,获取了该地区滑坡体隐患的分布情况,并讨论了滑坡历史形变演化特征及成因。结果表明：大部分区域较为稳定,滑坡隐患主要集中在山谷两侧,升降轨InSAR提高了滑坡监测识别的准确性和覆盖度;冻融滑坡形变过程与降雨型滑坡存在差异,呈现平稳期和失稳期交替出现的季节性变化特征;形变过程主要受冻融和降雨影响,两者共同作用加速坡体变形。实验结果验证了InSAR技术能够有效弥补传统监测手段的不足,可在高山冻土区滑坡隐患早期识别与监测防治中发挥重要作用。     

利用InSAR技术研究黄土地区滑坡分布

InSAR技术能够获取大面积、连续、高精度的地表垂直形变信息,可用来监测地震、火山、滑坡等自然灾害造成的地表形变。文章介绍了InSAR技术在监测陕北黄土地区滑坡中的应用,首先进行野外地质勘察和TM光学遥感影像解译,接着通过EnviSat SAR数据差分干涉处理,获取研究区干涉形变场,提取出滑坡位移量,最后详细分析黄草湾至董家寺沿线一带的滑坡变形范围,并划定出了4个有一定变形的重点监视区。     

Development of a methodological approach for the accurate measurement of slope changes due to landslides,using digital photogrammetry

This paper presents a method for studying recent landscape evolution due to mass movements. The method presented employs digital photogrammetric techniques, combined with global positioning system (GPS) measurements, to analyse landslide features depicted in aerial images taken by ad hoc and historical flights. The method was applied and validated in a 7 -km² area, located on a south-facing slope of a tributary of the Pas River (Cantabrian Range, Spain). In this area many landslide deposits and features are present, dated from 120,000  years BP to the present. The method starts with the design and carrying out of an ad hoc flight to take 1:5,000 photographs to be used as a reference, using different aircraft devices to control the position and geometry of the photograms. Different ground control points (GCPs) were measured using GPS techniques to support the geomorphological and photogrammetric work. Reference and historical photograms were digitised in a photogrammetric scanner and the digital images and GCPs were incorporated into a digital photogrammetric workstation to generate the reference digital stereo models by aerotriangulation. The models generated have a precision of 21 cm for the reference images and 33 cm for the historical images. The obtained landslide maps were compared with traditional geomorphological maps and an increased precision in the volume and area measurements was confirmed. Backward crown displacements show rates for the last 15 years of 15 mm year^?1. Mass involved in landslide mobility rates in recent landslides are 440  tons.     

基于点云密度特征的滑坡位移监测方法

激光扫描仪对同一目标两次采集的点并不重合,无法通过点云的直接比较快速确定滑坡位移。考虑到单个点云位置的不确定性和区域点云密度的稳定性,将点云的密度作为滑坡表面变形的表征,提出了基于点云密度特征的滑坡位移监测方法。将离散的三维点云转化为二维的密度图像,再利用粒子图像测速技术分析位移前后两幅点云密度图像的相关性,从而计算栅格图像中各子集的相对位移值;当各子集的位移全部计算完成后,得到目标区域的平面位移场。室内块体移动试验表明该方法的计算精度受变形梯度的影响,在地表变化剧烈处会产生一定程度的误差,且子集相关性系数无法达到1。在黄藏寺滑坡的位移监测中,利用本方法识别出了边坡的变动区域,计算出了滑坡的平面位移场,直观地反映了滑坡表面变形状况,验证了该方法的实用性。     

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。     

Pre-conditioning factors and susceptibility assessments of Wenchuan earthquake landslide at the Zhouqu segment of Bailongjiang basin, China

In this study, we present a landslide susceptibility assessment carried out after the devastating 2008 Wenchuan earthquake. For the Zhouqu segment in the Bailongjiang basin in north-western China landslide susceptibility was computed by a logistic regression method. This region has been experiencing landslides for a long time, and numerous additional slope failures were triggered by the 2008 Wenchuan earthquake. The data used for this study consists of slope failures attributed to the 2008 earthquake, the 878 post Wenchuan earthquake landslides and collapses inventory build up by combination the field investigation, monoscopic manual interpretation, image classification and texture analysis using SPOT 5 and ALOS remote-sensing image data. All data derived from remote sensing images are validated during field investigations. The landslide pre-disposing factor database was constructed. A digital elevation model (DEM) with a 30 × 30 m resolution, orthophotos, geological and land-use maps and information on peak ground acceleration data from the 2008 earthquake is used. The statistical analysis of the relation between Wencuan earthquake-triggered landslides and pre-disposing factors show the great influence of lithological and topographical conditions for earthquake-triggered slope failures. The quality of susceptibility mapping was validated by splitting the study area into a training and validation set. The prediction capability analysis showed that the landslide susceptibility map could be used for land planning as well as emergency planning by local authorities in this region.