泸定Ms 6.8地震诱发滑坡应急评价研究

2022-09-05,四川省甘孜州泸定县发生Ms 6.8地震。地震在山区诱发了大量的地质灾害,造成了严重的人员伤亡。快速准确地获取地震诱发地质灾害的空间分布范围对震后应急决策和救援抢险至关重要。基于全球同震滑坡数据库与深度学习算法,构建了地震诱发滑坡空间分布概率近实时预测模型,在震后2 h内获取了泸定地震诱发地质灾害的预测结果。通过震后无人机与卫星遥感影像,采用机器学习与深度学习算法实现了震后大范围地质灾害的智能识别,共解译地震诱发滑坡3 633处,总面积13.78 km2。利用遥感解译的泸定地震滑坡数据,对地震诱发地质灾害预测模型进行了优化,获得了震区范围更广、准确性更高的同震滑坡预测结果。结果表明,同震滑坡预测模型能够快速获取震后地质灾害的空间分布情况,填补震后遥感影像获取前的空窗期,为灾后应急救援提供支撑;基于无人机与卫星遥感影像的智能识别技术是快速获取大范围地质灾害信息的有效手段。所取得的研究成果在泸定地震震后应急救援工作中发挥了重要作用。     

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。     

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:①2019年1月-2021年4月,研究区的形变速率为-155.6～92.4mm/a,13个超过-30mm/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。     

澜沧──耿马地震灾情的航空遥感调查

利用航空遥感方法对云南省澜沧-耿马地震灾情进行调查,对震区的震害特征、房屋破坏、山体崩落和滑坡等进行定量量测、统计和分析。在统计房屋倒塌率的基础上绘制了澜沧-耿马地震区房屋倒塌程度分区图、澜沧-耿马地震区山区崩落体分布图。据航片统计量测,在摄影区范围内房屋倒塌6500余栋；崩落体392处,其中最大的石佛洞至金列段崩落体土石方量达164万m3.调查结果表明,用航空遥感方法进行地震灾情调查是十分有效的。     

基于地基真实孔径雷达的边坡动态监测研究与应用

滑坡是仅次于地震的发生最频繁、造成损失最严重的地质灾害之一，给人民群众生命和财产安全带来了巨大威胁。为了能够对危险滑坡进行动态监测，采用GPRI-Ⅱ地基雷达系统，研究了滑坡形变时序分析关键技术，制定了地基雷达数据时序处理流程，并应用于浙江绍兴某道路边坡稳定性监测。实验结果表明，该边坡存在1处明显的形变区域，位于施工现场堆积区，最大形变量达到3.5 mm/d。目前在观测条件满足的情况下，地基雷达可以达到亚毫米级的形变监测能力，已经成为灾害预警和治理的重要手段之一。     

滑坡隐患类型与对应识别方法

基于"三查"体系的滑坡隐患综合遥感识别工作已在全国范围推广应用并取得一定成效,但随着工作的推进,也暴露出一些亟待解决的问题,比如隐患究竟如何定义、有哪些类型、有何针对性的识别技术和方法,以及下一步工作重点是什么等.总结大量实践案例,进一步明确了滑坡隐患的定义,并将滑坡隐患分为正在变形区、历史变形破坏区和潜在不稳定斜坡3...     

GIS-based landslide susceptibility model considering effective contributing area for drainage time

This study employed GIS modelling to ascertain landslide susceptibility on Mt. Umyeon, south of Seoul, South Korea. In this study, an effective contributing area (ECA) for certain drainage time was purposed as a temporal causative factor and then used for modelling in combination with spatial causative factors such as elevation, slope, plan curvature, drainage proximity, forest type, soil type and geology. Landslide inventory map of 163 landslide locations was prepared using aerial photographic interpretation and field verifications after that digitized using GIS environment in 1:5000 scale. A presence-only-based maximum entropy model was used to establish and analyse the relationship between landslides and causative factors. Before final modelling, a jackknife test was performed to measure the variable contributions, which showed that the slope was the most significant spatial causative factor, and ECA with a drainage time of 12 h was the most significant temporal causative factor. The performances of the final models, with and without significant ECA, were assessed by plotting a receiver operating characteristic curve to be 75.5 and 81.2%, respectively.     

高分三号卫星全极化SAR影像九寨沟地震滑坡普查

基于光学遥感影像的区域滑坡普查易受云雾天气的影响,存在滑坡体调查不全面的问题,无法满足震后应急调查与恢复重建的需求。本文提出了一种极化SAR卫星数据滑坡普查方法,采用高分三号全极化SAR卫星影像数据,以九寨沟地震震区为实验区,在深入分析滑坡体和其他地物类型散射特征的基础上,融合极化特征、纹理特征和地形特征等多维特征信息,结合高分二号影像获取的训练样本,构建基于BP神经网络的全极化SAR数据滑坡自动识别模型,实现滑坡体的自动快速识别。与高分辨率光学影像与无人机航空影像目视解译结果相比较,总体识别精度为92.8%,Kappa系数为0.715,识别准确度满足地震应急实际应用的需求。研究成果可用于震区大区域滑坡体的普查,为后续开展无人机高分辨率影像滑坡体详查、灾后应急与景区恢复提供辅助信息支撑,并促进国产高分SAR卫星数据在防震减灾中的应用。     

The spatial response pattern of coseismic landslides induced by the 2008 Wenchuan earthquake to the surface deformation and Coulomb stress change revealed from InSAR observations

The 2008 Mw 7.9 Wenchuan earthquake triggered plenty of coseismic giant landslides, which resulted in almost one third of total fatalities and economic losses during the event. Previous studies investigated the spatial relations between landslide distribution and topographic and seismic factors such as elevation, slope aspect, distance from rupture trace and seismic intensity. However, few studies are performed exploring the effects of coseismic surface deformation and Coulomb stress change on triggering landslides due to lack of adequate deformation observation data and stress calculation model for slope failure. In this study, we develop an envelope method to map an entire coseismic deformation field in both near- and far-field areas of seismic faults through the data fusion from InSAR and pixel offset-tracking (POT) techniques. The change in static Coulomb stress (SCS) acting on coseismic landsliding surface caused by the event is determined using the faulting model derived from the joint inversion of InSAR and GPS data, and also with the use of the elastic half-space dislocation theory and the generalized Hook’s law. The analysis suggests the spatial response pattern of seismic landslides to the coseismic ground motion and stress change, especially in the vicinity of fault rupture trace. The landslide density dramatically rises with the stress increase within the range from Yingxiu to Beichuan areas along the major surface rupture. Moving further and eastward along the fault strike, most of large landslides are triggered as the zone of positive SCS change narrows. Moreover, the high-magnitude surface displacements are possibly responsible for the giant landsliding events in the easternmost section. From the analysis of the stress transfer, the occurrence of landslides in the study area is largely controlled by the Yingxiu-Beichuan fault with overwhelming rupture length and fault slip, yet the Pengguan fault indeed shows dominance in the area between the two faults. The results show that coseismic surface deformation (derived from InSAR data in this study) and static Coulomb stress change can serve as two significant controlling factors on seismic landslide distribution and that the stress factor seems more significant in the vicinity of surface rupture.     

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。     

卡尔曼滤波在滑坡监测中的应用

针对滑波变形的特点,将滑坡体上的监测点视为一运动点场,提出一种以运动方程为核心、以卡尔曼滤波技术为工具的变形观测数据处理方法,使滑坡的预报成为可能.     

地面激光点云与航空影像相结合的滑坡监测

面向云南某水电站滑坡监测的实际需求，提出了将地面激光点云数据和航空影像相结合的滑坡监测方法。首先，通过数据配准，实现地面激光点云数据和航空影像数据的融合统一；然后，通过数据去噪与地表模型重建建立监测的数据基准；最后，通过基于地表模型的整体变形分析和基于剖面的采样分析，对滑坡区域的变形规律进行研究。通过对已经获取的5期监测数据的分析表明，本文提出的监测方法具有较高的监测精度和很好的适用性。     

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.     

广域滑坡灾害隐患InSAR显著性形变区深度学习识别技术

全面识别和发现地质灾害隐患,已成为我国地质灾害防治的重大实际需求。目前,基于InSAR技术和深度学习相结合用于广域尺度下地质灾害隐患智能识别应用效果与适用性还需要进一步探索与研究。本文基于Stacking InSAR技术获得地表形变相位数据,利用深度学习检测识别正在变形的滑坡隐患位置与分布,确定显著性形变区边界,探索将上述技术方法推广到一定的广域范围和动态更新数据集。结果显示,测试数据集显著性形变区平均识别精度为0.69,召回率为0.67,F₁ score为0.67,动态更新数据集识别精度为0.85,召回率为0.58,F₁ score为0.68。研究表明,本文方法在广域地灾隐患识别中具有应用可行性,可为地质灾害监测预警提供理论基础与技术支撑。     

特大规模滑坡遥感调查──巧家县城滑坡

目视解译１：６７万彩红外航空象片发现巧家县城斜坡显示为典型的滑坡地形。经实地验证及结合地质资料、航空象片、卫星象片综合解译分析认为：根据斜坡形态、金沙江异常弯道、斜坡上松散物质分布及所处地质环境与降雨条件确定巧家斜坡为－巨型古滑坡，估算体积约１２０×１０８ｍ３，就我们所查阅到的国内外报道中为一最大规模古滑坡，年龄约１０万年。目前，滑坡整体已经稳定，但后壁、侧壁及滑体上发育的大量次生崩塌、滑坡、泥石流还将长期活动。     

Acquisition of Directional Parameters in Aerial Images Based on DEM Data

1　Extraction of the steep curve mapThe steep curve is defined as connection linefrom zero cross points which are produced fromthe convolution of Laplace ( LOG) filtration toDEM. Firstly, the elevation data from DEM areprocessed with LOG convolution and zero cross.The sampling formula is 2G(i,j) =12πσ6(r2 -2σ2)e-r2/2σ2 (1)where -M≤i≤M,-M≤j≤M,r2=i2+j2; M isthe filtration radius, nearly equaling to 5.1σ2; pre diction errorσis set to 4. Suppose that e[i,j] is asmall …     

基于深度学习的滑坡位移时空预测

滑坡变形监测数据是认识滑坡变形演化规律的直接依据,对该数据深度挖掘是实现滑坡灾害预警预报的有力保障。现有的滑坡位移预测模型多局限于单个监测点的时序预测,且未考虑监测点间的空间相关性。针对上述问题,本文提出了一种基于深度学习的滑坡位移时空预测模型:首先,构建表达所有点间空间相关性的加权邻接矩阵;其次,引入外界影响因素加强属性特征矩阵,以构建图结构数据;最后,采用集合图卷积网络(GCN)和门控循环单元(GRU)的深度学习模型,并通过多组试验寻找最优超参数,实现滑坡位移的时空预测。本文模型结果的均方根误差为4.429 mm,与对比模型相比至少降低了47.3%。而消融试验结果也显示,引入外界影响因素的属性增强可进一步提高模型的预测性能,均方根误差相对于未属性增强结果减少了28.4%。结果表明,该方法可用于滑坡位移或其他地质灾害中同样具有时空关联属性的观测量的时空预测。     

Acquisition of directional parametes in aerial images based on DEM data

This paper develops a method which can be used to assist aerial navigation by determining the spatial position and posture of the aerial photographic plane. After the method, aerial images match known DEM to capture the spatial position and posture. Some aerial images and terrain data are used to testify our method. Compared with those of analytic and stereo mappers, the results by our method are correspondent to real measurements well.     

Acquisition of Directional Parameters in Aerial Images Based on DEM Data

This paper develops a method which can be used to assist aerial navigation by determining the spatial position and posture of the aerial photographic plane. After the method, aerial images match known DEM to capture the spatial position and posture. Some aerial images and terrain data are used to testify our method. Compared with those of analytic and stereo mappers, the results by our method are correspondent to real measurements well.     

THE EXTRACTION OF POSITIONAL DATA FROM HISTORICAL PHOTOGRAPHS AND THEIR APPLICATION TO GEOMORPHOLOGY

A technique which enables spatial data to be obtained from historical photography is outlined. The method is applied to a 40 year sequence of oblique aerial photography of the Black Ven landslide in Dorset, England. Some of the possible data processing options are discussed and illustrated by some preliminary results produced from the Dorset landslide.