三峡库区“阶跃”型滑坡变形机理与预警模型

自2003年蓄水以来,三峡库区已查明的滑坡或潜在滑坡高达5000余处,这些灾害对三峡水库的持续运营、大坝、航道及库区居民的安全造成了严重的威胁。通过研究滑坡的变形特征、诱发因素及失稳机制,有助于开展滑坡的稳定性评价,并构建预警预报模型。以三峡库区八字门滑坡为研究对象,综合分析降雨、库水位、人工和自动GNSS监测等数据,结合勘查资料及野外宏观巡查,研究了滑坡的变形特征及失稳机理,并确定合理的预警判据及阈值。研究表明:①八字门滑坡整体变形明显,处于蠕动变形阶段,滑坡变形主要集中于每年5—9月,滑坡累积曲线呈现典型的“阶跃”状变形特性。②滑坡的变形受斜坡结构、岩性等因素的控制,水库水位下降是滑坡变形的主要驱动因素,并与库水下降速率正相关。另外,特大暴雨和持续降雨在水位下降阶段、水库低水位运行期及水位上升期会促进滑坡变形,是滑坡的次要驱动因素。③通过精细化数据分析以及改进切线角法获取的八字门滑坡出现“阶跃”变形的位移速率阈值为4.6 mm/d,7 d累积降雨量阈值为60 mm,库水位阈值为159 m,库水位下降速率阈值0.4 m/d。     

三峡库区龙头山滑坡变形机制研究

以三峡库区龙头山滑坡作为研究对象,研究库水位及降雨综合作用下滑坡体的变形机理及演化趋势。研究表明,滑坡体变形在空间上存在差异性,滑坡体中部、左侧前缘变形较大,中后部变形逐渐减小,表现出牵引滑动特征;降雨和库水位波动是滑坡体变形的主要影响因素,2次较大变形均与其叠加作用相关;从演化趋势上来看,滑坡体内不平衡应力得到不断释放,滑坡体变形趋势逐渐减缓,前期滑坡体变形受库水波动作用强烈,后期变形主要受降雨作用影响。     

一种V/S和LSTM结合的滑坡变形分析方法

滑坡变形的产生是坡体自身地质条件和外部诱发条件共同作用的结果,滑坡变形定量预测是滑坡监测预警的关键。传统的基于滑坡累计位移-时间曲线分析滑坡变形的方法,忽略了滑坡变形演化的影响因素,难以对滑坡变形进行准确预测。三峡库区滑坡研究多集中在滑坡时空分布特征和滑坡整体稳定性分析方面,亟需开展单体滑坡综合变形分析。以三峡库区白水河滑坡为例,基于滑坡宏观变形和位移监测数据,利用重标方差（rescaled variance statistic,V/S）分析法对滑坡整体和局部变形趋势进行分析,进而构建考虑库水位波动和降雨滞后性影响因素的可有效利用长期依赖信息的长短记忆（long short-term memory,LSTM）神经网络模型,定量预测滑坡位移。研究结果表明,滑坡体属牵引式滑坡,北东部稳定性较差,西部和后缘相对稳定,预测值的均方根误差为8.95 mm,证明该模型是一种高性能的滑坡变形分析方法。     

基于机器学习的汶川震区滑坡灾害气象预警模型研究

近年来,降雨诱发的滑坡灾害日益频繁,给人民生命财产安全造成了严重的威胁。因此,深入开展滑坡灾害气象预警研究具有重要的理论意义和实用价值。为了解决传统滑坡灾害气象预警方法在计算性能和预警精度等方面的不足,本文立足于滑坡灾害气象预警工作,选取汶川M s 8.0级强烈地震重灾区的62县市为研究区,深入分析研究区滑坡灾害与地质环境、降雨之间的关联关系,构建适用于研究区的滑坡因子指标体系,运用机器学习理论和方法,建立了基于机器学习的滑坡灾害气象预警模型,并利用研究区历史监测数据进行试验,验证了该方法的准确性和可靠性。     

诱发因素影响下的滑坡参数优化预测模型研究

在降雨等外界诱发因素的综合作用下,滑坡位移预测是一个复杂的动力系统问题。利用三峡库区白家包滑坡综合监测数据,分析滑坡演化实时特征,提取影响滑坡变形的最相关因素,研究发现白家包滑坡为降雨主导型堆积层滑坡;采用自回归综合移动模型（ARIMA）模型进行拟合及预测,引入月累积降雨量对模型季节性趋势参数进行评估优化,对白家包滑坡72期月相对位移数据进行拟合及预测研究,最终模型结果和实测值的平均绝对误差和相关系数分别为2.873和0.983。研究结果表明,与传统经验法相比,优化参数模型更符合滑坡变形的一般规律。     

高精度GNSS实时滑坡变形监测技术及环境建模分析研究

滑坡变形监测不仅是科学问题,也是实践问题。对地质滑坡灾害进行高精度变形监测是防灾减灾至关重要的命题。GNSS作为滑坡监测预警的重要手段之一,不仅具有连续高精度、全天候监测能力,而且是目前唯一可以直接获取滑坡地表三维矢量变形的监测手段。然而,滑坡灾害往往多发于山地丘陵等复杂环境下,加上地震、降雨等因素的诱发作用,极易影响GNSS技术的实时预处理质量和监测定位精度;此外,高价位的GNSS监测终端开销也使得大规模、高密度滑坡监测难以大规模推广应用。针对上述滑坡复杂环境影响和设备成本问题,本研究分别从高精度GNSS实时数据预处理算法、空间地形环境建模和低成本云平台监测系统3个方面进行了探讨。     

滑坡危险性预测模型的设计与实现

滑坡灾害的发生严重影响人类社会的生产生活,因此对滑坡灾害的预警预测工作一直是地质灾害监测和防治的重点,国内外众多学者都对此进行了相应研究。本文在前人的研究基础上把信息量模型和降雨诱发指数应用在滑坡预测研究中,充分考虑两者的耦合影响,建立了滑坡危险性预测模型,并完成了模型的开发工作。该模型实现了滑坡灾害危险性预测的快速化、自动化,并且已集成到江西省地质灾害预警预报分析决策系统中,得到了成功应用。     

边坡稳定倾角预警值研究分析

以某简易均质边坡为背景,在数值模拟分析软件Midas-GTS中进行建模、计算、倾角位移的记录与分析。通过计算分析得出位移与倾角变化曲线,可用于滑坡的预警监测。针对边坡模型不同部位纵向和横向的倾角变化分析,以及倾角变化和安全系数之间的关系曲线,确定了边坡预警的参考倾角报警值。     

顾及坡体赋存环境的概率地震滑坡危险性制图EI北大核心CSCD

位移模型是评估地震滑坡危险性的一种重要方法。作为位移模型主要输入的岩体参数,一般仅考虑岩体的岩性差异,而对岩体所在坡体赋存环境不同而产生的岩体强度空间差异性缺乏必要考虑,影响地震滑坡危险性评估的可靠性。本文基于汶川同震滑坡分析与影响因子定权研究,提出了一种基于位移模型且顾及坡体赋存环境的概率地震滑坡危险性评估方法,研发了基于ArcGIS的概率地震滑坡危险性制图模块,并以康定为例进行了应用试验。根据第五代中国地震动参数区划图提供的地震动峰值加速度,进行了常遇、罕遇地震场景下康定地震滑坡危险性制图。结果显示,相比于未顾及坡体赋存环境影响,本文方法能够识别出更多潜在地震滑坡危险区;在常遇地震场景下,康定部分区域为地震滑坡高危险区域,其分布主要受构造环境与降雨影响;而在罕遇地震场景下,康定受地震影响的范围显著增加,且呈现明显的集聚现象,尤其是康定城区附近及G318国道康定-泸定路段两侧存在大量地震滑坡高危险区。此外,对因岩体强度差异导致的地震滑坡危险性制图的不确定性进行了量化分析,结果显示,岩体强度参数是导致评估结果存在不确定性的重要因素;实践中,应针对具体应用场景选择合适的岩体强度开展概率地震滑坡危险性评估与制图。本文方法扩展了GIS的地灾风险制图应用,具有普适性,可持续改进;试验成果可为康定未来土地利用规划、地震地质灾害防治及川藏铁路安全提供参考依据。     

GIS在地震地质灾害信息管理中的应用--滑坡稳定性分析技术

地震及降雨等因素对于滑坡发生有明显的控制作用。为了有效地将地表地形与滑坡运动特征结合起来,进行了滑坡的稳定性分析。在GIS中了实现对滑坡快速有效的分析、预测,并建立了滑坡稳定的综合判定标准。     

顾及降水因子的卡尔曼滤波在滑坡变形数据处理中的应用

滑坡体在受到内部及外部相关因素作用后逐渐失稳,促使滑坡体发生变形。本文利用卡尔曼滤波对滑坡体上的监测点进行变形分析,估计出监测点的状态参数以全面地反映滑坡体的运动状态。普通卡尔曼滤波没有考虑其他控制因子的影响,如降水量、河流的水位等,但这些因素对某些滑坡的变形有时起了决定性作用。本文探讨了将降水因子作为卡尔曼滤波的控制输入及控制输入系数确定的方法,利用该方法可以初步判断出滑坡体对降水是否敏感。     

基于水文-气象阈值的区域降雨型滑坡预测研究

目前区域降雨型滑坡预测主要依赖降雨阈值开展,然而从降雨诱发滑坡机理可知,除降雨入渗导致的土壤含水量变化外,降雨入渗前的土壤含水量也是影响边坡失稳的重要因素,无法考虑降雨入渗前的土壤湿度情况,被认为是降雨阈值在滑坡预测中表现差的主要原因。针对这一问题,本文以四川省都江堰地区作为试验区域,提出考虑前期土壤湿度的区域降雨型滑坡预测思路,通过统计分析历史滑坡数据,构建了基于前期土壤湿度和近期降雨情况的水文-气象阈值模型,其中前期土壤湿度情况由改进的前期有效降雨指数刻画,近期降雨情况由最近的累积降雨量表示。试验结果表明:在试验区域的降雨型滑坡预测中,水文-气象阈值模型表现出较好的命中率和较低的误报率。本文构建的水文-气象阈值模型,可同时考虑前期土壤湿度和近期降雨对滑坡发生的影响,模型所需数据少、所用方法简单易操作且预测性能较优,适合在区域降雨型滑坡预测中推广应用。     

2017年“8.8”九寨沟地震滑坡自动识别与空间分布特征

2017年8月8日发生的7.0级九寨沟地震诱发九寨沟熊猫海附近产生大量的滑坡体,造成道路阻塞,严重影响地震应急救援进度。为快速准确地识别滑坡分布范围,本文在深入分析滑坡遥感影像特征的基础上,引入面向对象分析方法,实现了基于无人机影像的震后滑坡体的自动识别。通过多尺度分割算法获取滑坡多层次影像对象,利用SEaTH算法自动构建每一层次特征规则集,实现基于不同层次分析的滑坡体自动识别。分析滑坡体在地形、活动断层等因子中的空间分布特征,为地震滑坡预测与危险性评价奠定基础。与人工目视解译结果相比较,基于面向对象的滑坡自动识别方法提取精度可达94.8%,Kappa系数为0.827,在电脑配置相同的情况下,自动识别方法的效率是人工目视解译效率的一倍。空间分布特征分析表明,地震滑坡的空间分布与斜坡坡度、地形起伏度呈正相关关系,与地表粗糙度存在负相关关系,研究区滑坡体分布存在明显的断层效应。     

Assessment of Seismically Induced Landslide Hazard for the State of Karnataka Using GIS Technique

Landslide hazards are a major natural disaster that affects most of the hilly regions around the world. In India, significant damages due to earthquake induced landslides have been reported in the Himalayan region and also in the Western Ghat region. Thus there is a requirement of a quantitative macro-level landslide hazard assessment within the Indian subcontinent in order to identify the regions with high hazard. In the present study, the seismic landslide hazard for the entire state of Karnataka, India was assessed using topographic slope map, derived from the Digital Elevation Model (DEM) data. The available ASTER DEM data, resampled to 50 m resolution, was used for deriving the slope map of the entire state. Considering linear source model, deterministic seismic hazard analysis was carried out to estimate peak horizontal acceleration (PHA) at bedrock, for each of the grid points having terrain angle 10° and above. The surface level PHA was estimated using nonlinear site amplification technique, considering B-type NEHRP site class. Based on the surface level PHA and slope angle, the seismic landslide hazard for each grid point was estimated in terms of the static factor of safety required to resist landslide, using Newmark’s analysis. The analysis was carried out at the district level and the landslide hazard map for all the districts in the Karnataka state was developed first. These were then merged together to obtain a quantitative seismic landslide hazard map of the entire state of Karnataka. Spatial variations in the landslide hazard for all districts as well as for the entire state Karnataka is presented in this paper. The present study shows that the Western Ghat region of the Karnataka state is found to have high landslide hazard where the static factor of safety required to resist landslide is very high.     

Satellite-derived rainfall thresholds for landslide early warning in Bogowonto Catchment,Central Java,Indonesia

Satellite rainfall products for landslide early warning prediction have been spotlighted by several researchers, in the last couple of decades. This study investigates the use of TRMM and ERA-Interim data, for the determination of rainfall thresholds and the prediction of precipitation, respectively, to be used for landslide early warning purposes at the Bogowonto catchment, Central Java, Indonesia. A landslide inventory of 218 landslides for the period of 2003–2016 was compiled, and rainfall data were retrieved for the landslide locations, as given by 6 ground stations, TRMM, and ERA-Interim data. First, rainfall data from the three different sources was compared in terms of correlation and extreme precipitation indices. Second, a procedure for the calculation of rainfall thresholds for landslide occurrence was followed consisting of four steps: i) the TRMM-based rainfall data was reconstructed for selected dates and locations characterized by landslide occurrence and non-occurrence; ii) the antecedent daily rainfall was calculated for 3, 5, 10, 15, 20 and 30 days for the selected dates and locations; iii) two-parameter daily rainfall-antecedent rainfall thresholds were calculated for the aforementioned dates; after analysis of the curves the optimum number of antecedent rainfall days was selected; and (iv) empirical rainfall thresholds for landslide occurrence were determined. The procedure was repeated for the entire landslide dataset, differentiating between forested and built-up areas, and between landslide occurrence in four temporal periods, in relation to the monsoon. The results indicated that TRMM performs well for the detection of very heavy precipitation and can be used to indicate the extreme rainfall events that trigger landslides. On the contrary, as ERA-Interim failed to detect those events, its applicability for LEWS remains limited. The 15-day antecedent rainfall was indicated to mostly affect the landslide occurrence in the area. The rainfall thresholds vary for forested and built-up areas, as well as for the beginning, middle and end of the rainy season.     

雷达干涉测量对甘肃南峪乡滑坡灾前二维形变追溯

利用存档光学遥感影像对灾前演变情况进行分析是目前常用的方法,但往往受限于获取时间密度、云量等因素。随着雷达遥感卫星数据质量的不断提升,合成孔径雷达干涉测量（interferometric syntheticaperture radar,InSAR）技术可以为滑坡灾前形变探测提供新的技术途径。基于欧洲空间局哨兵一号（Sentinel-1）雷达卫星数据,同时结合升轨与降轨视线向形变结果提取沿坡向与垂直向二维形变,对2018年7月12日甘肃南峪乡滑坡灾前二维形变进行追溯分析。时序结果显示,该滑坡自2017年6月起便已经开始缓慢的变形,至滑坡发生前13个月时最大累积形变量达77 mm。结合降雨量数据对比分析,发现该滑坡灾前变形与降雨量变化高度吻合,说明降雨是该滑坡发生的主要诱因之一。该InSAR追溯结果展示了星载雷达干涉测量技术在滑坡探测方面的应用潜力,为滑坡诱因分析、防灾减灾乃至滑坡监测预警工作提供了新的思路与参考。     

Landslide Mapping and Assessment by Integrating Landsat-8, PALSAR-2 and GIS Techniques: A Case Study from Kelantan State,Peninsular Malaysia

Integration of satellite remote sensing data and GIS techniques is an applicable approach for landslide mapping and assessment in highly vegetated regions with a tropical climate. In recent years, there have been many severe flooding and landslide events with significant damage to livestock, agricultural crop, homes, and businesses in the Kelantan river basin, Peninsular Malaysia. In this investigation, Landsat-8 and phased array type L-band synthetic aperture radar-2 (PALSAR-2) datasets and analytical hierarchy process (AHP) approach were used to map landslide in Kelantan river basin, Peninsular Malaysia. Landslides were determined by tracking changes in vegetation pixel data using Landsat-8 images that acquired before and after flooding. The PALSAR-2 data were used for comprehensive analysis of major geological structures and detailed characterizations of lineaments in the state of Kelantan. AHP approach was used for landslide susceptibility mapping. Several factors such as slope, aspect, soil, lithology, normalized difference vegetation index, land cover, distance to drainage, precipitation, distance to fault, and distance to the road were extracted from remotely sensed data and fieldwork to apply AHP approach. The excessive rainfall during the flood episode is a paramount factor for numerous landslide occurrences at various magnitudes, therefore, rainfall analysis was carried out based on daily precipitation before and during flood episode in the Kelantan state. The main triggering factors for landslides are mainly due to the extreme precipitation rate during the flooding period, apart from the favorable environmental factors such as removal of vegetation within slope areas, and also landscape development near slopes. Two main outputs of this study were landslide inventory occurrences map during 2014 flooding episode and landslide susceptibility map for entire Kelantan state. Modeled/predicted landslides with a susceptible map generated prior and post-flood episode, confirmed that intense rainfall throughout Kelantan has contributed to produce numerous landslides with various sizes. It is concluded that precipitation is the most influential factor for landslide event. According to the landslide susceptibility map, 65% of the river basin of Kelantan is found to be under the category of low landslide susceptibility zone, while 35% class in a high-altitude segment of the south and south-western part of the Kelantan state located within high susceptibility zone. Further actions and caution need to be remarked by the local related authority of the Kelantan state in very high susceptibility zone to avoid further wealth and people loss in the future. Geo-hazard mitigation programs must be conducted in the landslide recurrence regions for reducing natural catastrophes leading to loss of financial investments and death in the Kelantan river basin. This investigation indicates that integration of Landsat-8 and PALSAR-2 remotely sensed data and GIS techniques is an applicable tool for Landslide mapping and assessment in tropical environments.     

结合SBAS-InSAR技术及信息熵的苍山地质滑坡隐患识别

针对西南地区滑坡隐患高位隐蔽,传统技术难以全面识别的问题,本文以大理苍山为研究对象,首先利用SBAS-InSAR技术对苍山2019年1月—2021年4月间的滑坡隐患进行识别;然后结合随机概率信息熵模型,对不同坡度等级与边坡稳定性之间的关联性进行定量分析;最后根据典型隐患区的遥感影像以及采样点的形变时序图,探讨了边坡形变时空演化特征及沉降诱因。试验结果表明：(1)2019年1月—2021年4月,研究区的形变速率为-155.6～92.4 mm/a, 13个超过-30 mm/a的不稳定滑坡隐患被识别;(2)坡度等级为Ⅳ、Ⅴ级时,信息熵大于0.8,边坡稳定性较弱,不均匀形变严重,与已有研究保持高度一致,证实了该模型的可靠性;(3)典型隐患区形变趋势呈明显的季节性变化,降雨和冰雪消融是导致边坡失稳的主要因素。