斜坡失稳时间的协同预测模型

斜坡岩体由小变形到大变形乃至滑坡的发生，实质上是由组成斜坡的各子系统协同作用的结果．将协同学引入斜坡的稳定性预测评价中，并提出了一种新的斜坡失稳时间预测模型──协同预测模型．经实例检验，该模型预测精度较高，可用于滑坡的短期或临滑预报．     

基于TLS的滑坡形变分析方法研究

以四川理县一地质滑坡为例,分析扫描点空间位置的理论精度,探讨点云数据获取以及点云过滤、点云拼接、地理参考等数据预处理的关键技术,给出一套比较直观、全面的滑坡形变分析方法,包括基于点云比较的形变分析、基于TIN比较的形变分析、基于特征点的形变分析以及基于DEM的形变分析。为了充分利用非地面点云数据,提出一种提取滑坡区域电线杆、树干的几何特征来分析滑坡水平变化的新方法。通过分析得到的滑坡形变量与GNSS远程实时监测结果基本一致。     

末次冰盛期-全新世东海陆坡滑塌沉积的地质年代与沉积学证据

基于东海陆坡区OT12-01孔长度为5.35 m沉积物AMS 14C测年、高分辨率粒度分析和XRF岩芯元素扫描数据,识别出了末次冰盛期(LGM)至全新世期间发生的多次滑塌事件。研究发现,OT12-01孔全新世晚期沉积层缺失,LGM至全新世期间呈现AMS 14C年龄模式频繁倒转、沉积物粒度、元素比值垂向上多处突变或"错动"等特征,保存了LGM至全新世非连续的沉积记录。OT12-01孔沉积物主要来源于低海平面时期的长江/黄河物质,OT12-01孔是由中国大陆陆源物质在东海陆架经水动力分选,细颗粒被搬运至东海陆坡后,发生多次滑塌形成。LGM时期物源供给是OT12-01孔形成滑塌沉积的重要因素,末次冰消期海平面快速上升可能是高频滑塌沉积的触发原因,而低海平面时期甲烷水合物溢出、频繁的地震和火山喷发可能是海底滑坡作用发生的诱因。     

Early Identification of the Jiangdingya Landslide of Zhouqu Based on SBAS-InSAR Technology

SBAS-InSAR technology is characterized by the advantages of reducing the influence of terrain-simulation error, time-space decorrelation, atmospheric error, thereby improving the reliability of surface-deformation monitoring. This paper studies the early landslide identification method based on SBAS-InSAR technology. Selecting the Jiangdingya landslide area in Zhouqu County, Gansu Province as the research area, 84 ascending-orbit Sentinel-1A SAR images from 2015 to 2019 are collected. In addition, using SBAS-InSAR technology, the rate and time-series results of surface deformation of the landslide area in Jiangdingya during this period are extracted, and potential landslides are identified. The results show that the early landslide identification method based on SBAS-InSAR technology is highly feasible and is a better tool for identifying potential landslides in large areas.     

川藏公路“102”滑坡群的基本特征

“102”滑坡群位于川藏公路西藏波密县境内的通麦“102”地段.该地段滑坡成群分布,在约3km长的范围内,共有大小滑坡22处,其中直接危害川藏公路且规模较大的滑坡有6处.     

滑坡变形立体监测网建设

论文简要介绍了滑坡监测工作内容和监测方法，对现有技术方法在实际工作中的优缺点进行了简要评述。认为在滑坡稳定性监测工作中应该建立全方位、多种手段的立体监测网，仅仅运用某一种或者某一单方面的监测方法是不行的，是片面的，无法准确了解滑坡的动态。所说的立体监测网既有水平位移监测又有垂直位移监测，既有滑坡地表表层变形监测又有滑体深部变形监测，既有专业监测又有群测群防体系。同时监测有可能准确地掌握使滑坡失稳的各种因素（自然因素和人为因素）。因此建议在滑坡监测中建立全方位、多种手段的立体监测网，并提出了立体监测网建设的主要内容。     

基于高精度DEM和RS技术的滑坡环境指标参数快速提取方法——以陕西省宝鸡市金台区长乐塬特大型滑坡为例

DEM和RS技术是研究滑坡地质灾害的重要资料和手段。近年来,随着高空间分辨率遥感卫星和高精度雷达卫星的上天,可以获取现时性高精度的DEM,使滑坡地质灾害的研究由二维向三维提升。利用IRS-P5数据生成的5m精度的DEM,借鉴GOOGLE的三维可视性原理,将其和高空间分辨率QuickBird(0.61m)数据叠置到数字地球之上,制作成三维可视性图像,进行滑坡环境指标参数提取方法研究。研究结果表明,该方法可直接读取滑坡环境指标的三维参数,具有客观、准确、快速的特点,可为滑坡灾害评估和区域地质灾害危险性评价提供定量化资料。     

基于深度学习的滑坡灾害易发性分析

滑坡灾害成因机理复杂、影响因素众多,深度学习作为当前人工智能领域的热点,能够更好地模拟滑坡灾害的形成并准确预测潜在的斜坡。为了挖掘深度学习在滑坡易发性的应用潜能,本文构建了一维、二维和三维的滑坡数据表达形式,并提出3种基于卷积神经网络模型（Convolutional Neural Networks, CNN）的滑坡易发性分析处理框架：基于CNN分类器、基于CNN与逻辑回归的融合和基于CNN集成,最后以江西省铅山县为研究对象进行验证,结果表明：所有基于CNN的易发性模型都能够获得准确且可靠的滑坡易发性分析结果。其中,基于二维数据的CNN模型在所有单分类器中预测精度最高,为78.95%。此外,二维CNN特征提取能够显著提升逻辑回归的预测精度,其准确率提升7.9%。最后,异质集成策略能够大幅度提升基于CNN分类器的滑坡预测精度,其准确率提升4.35%~8.78%。     

区块链技术在全国地质灾害风险预警系统建设中的应用探索

2019年以来,自然资源部全面推进普适型设备研发与地质灾害自动化监测预警工作,截至2023年6月,已在17个地质灾害重点防治省份的5.5万处隐患点推广应用,全国地质灾害风险预警系统也进入了快速发展阶段。目前,系统每日接收监测数据超千万,在海量数据存储管理、时序数据并行处理、大数据智能分析和多参数风险预警等方面取得积极进展。然而,系统在数据安全方面仍存在诸多挑战,尤其是在数据一致性、数据防篡改和系统可靠性等方面有较大提升空间。文章在系统研究区块链技术特点及其在相关领域典型应用的基础上,提出基于分布式账本技术和共识机制的“可信数据流”建立方法,依此形成“区块链+全国地质灾害风险预警系统”总体框架,同时建立了原型系统。实验数据显示改进系统可以解决省-部数据不一致问题,识别并阻止非法数据篡改,多节点故障情况下仍可保障系统稳定运行,系统总体性能损耗低于20%。研究成果可为区块链技术在各级地质灾害风险预警系统中的应用提供顶层设计思路与技术方法,对于提升全国地质灾害自动化监测网安全运行能力具有重要借鉴意义。     

Evolution of the north flank of Tenerife by recurrent giant landslides

Geomorphologic analysis of submarine and subaerial surface features using a combined topographic/bathymetric digital elevation model coupled with onshore geological and geophysical data constrain the age and geometry of giant landslides affecting the north flank of Tenerife. Shaded relief and contour maps, and topographic profiles of the submarine north flank, permit the identification of two generations of post-shield landslides. Older landslide materials accumulated near the shore (<40-km) and comprise 700 km³ of debris. Thickening towards a prominent axis suggests one major landslide deposit. Younger landslide materials accumulated 40–70 km offshore and comprise the products of three major landslides: the La Orotava landslide complex, the Icod landslide and the East Dorsal landslide complex, each with an onshore scar, a proximal submarine trough, and a distal deposit lobe. Estimated lobe volumes are 80, 80 and 100 km³, respectively. The old post-shield landslide scar is an amphitheatre, 20–25 km wide, partly submarine, now completely filled with younger materials. Age–width relationships for Tenerife's coastal platform plus onshore geological constraints suggest an age of ca. 3 Ma for the old collapse. Young landslides are all less than 560 ka old. The La Orotava and Icod slides involved failures of slabs of subaerial flank to form the subaerial La Orotava and Icod valleys. Offshore, they excavated troughs by sudden loading and basal erosion of older slide debris. The onshore East Dorsal slide also triggered secondary failure of older debris offshore. The slab-like geometry of young failures was controlled by weak layers, deep drainage channels and flank truncation by marine erosion. The (partly) submarine geometry of the older amphitheatre reflects the absence of these features. Relatively low H/L ratios for the young slides are attributed to filling of the slope break at the base of the submarine edifice by old landslide materials, low aspect ratios of the failed slabs and channelling within troughs. Post-shield landslides on Tenerife correlate with major falls in sea level, reflecting increased rates of volcanism and coastal erosion, and reduced support for the flank. Landslide head zones have strongly influenced the pattern of volcanism on Tenerife, providing sites for major volcanic centres.