209国道许家湾滑坡的成因及治理对策

结合许家湾滑坡灾害点的地质背景资料和现场勘察结果，从滑坡区的地貌、区域地质构造、地层岩性、土壤特性、水文地质特征以及人类工程活动等方面分析滑坡成因机制。指出同向多层次结构的边坡类型、山高坡陡及地表水汇集走廊的地貌特征、发育多组密集节理的区域地质构造、软硬互层的地层岩性、土体的残余强度特性等是滑坡形成的内因；而人为削坡、强降雨和破坏植被营造水耕梯田等人类活动所造成的流水浸蚀，是滑坡形成的诱发空子。基于滑坡稳定性的定量计算，确定了采用抗滑挡土墙、格构式地梁和垫梁组成抗滑系统，并布置了由树枝笼渗沟构成的排水系统等工程治理措施。现场监测结果表明滑坡治理措施卓有成效。     

三峡库岸城区滑坡治理与沿江公路建设一体化模式--以重庆万州清泉路滑坡为例

三峡库岸城区滑坡90%属于由第四纪松散土体组成的散体滑坡,滑坡区属于城区重要组成部分,降雨及三峡库水位降落是此类滑坡孕发的动力因素。本文以重庆万州清泉路滑坡为例,基于库岸城区滑坡防治原则,构建了实用的滑坡治理与沿江路建设一体化模式,推导了抗滑桩的内力计算方法;详细分析了一体化模式的结构组成及受力特性。研究成果对于三峡库区城区滑坡的治理与开发利用具有较强的指导借鉴作用。     

深圳市地质灾害及防治技术

深圳市是一座新兴的海滨城市，市区东部与西部以花岗岩为主，中部以古老的变质岩为主，部分地区分布有砂岩、泥岩及变质的板岩、粉砂岩，深圳大断裂是区内主要的构造形迹。滑坡地质灾害主要分布在中部。在花岗岩地区主要变形为岩质高边坡崩塌、落石、坍塌等坡面变形；在变质岩、砂岩、泥岩地区发生多处滑坡。地质构造与地质灾害关系密切，深圳大断裂两侧为地质灾害多发区。防治边坡变形的工程措施即桩排、连续墙、基础为桩，锚杆框架；预应力锚索、预应力锚索框架护坡、预应力锚索抗滑桩，设置多级平台减载及琉干排水措施。深圳采用的治理地质灾害的工程措施经过多年的使用达到预期效果，但在上世纪90年代初完成的工程很少考虑工程措施与城市的景观协调。90年代后期已经考虑了工程完成后对城市环境的影响。因此采用防护措施以锚杆框架、预应力锚索框架工程为主，框架间植草，既有利于边坡稳定又有利于美化环境。     

黄土滑坡临滑预报的应变控制方法

我国西北部地区黄土沉积巨厚，地质构造复杂，土体强度低，水敏感性强，各种类型的黄土滑坡广泛分布。黄土滑坡具有滑动规模大、滑动速度快、灾害损失严重等特点。黄土滑坡滑动时间预报，特别是临滑预报在地质灾害防治领域具重要意义。根据黄土力学性质试验与典型黄土滑坡变形分析研究结果，在对比分析黄土剪切应变特性与滑坡滑动破坏机制的基础上，论文提出了一种黄土滑坡临滑预报的应变控制方法。建立了中、浅层与厚层黄土滑坡的应变破坏标准。经多处滑坡实例验证，结果基本合理，可应用于黄土滑坡临滑预报。     

金沙江某水电站库区泥石流地质灾害研究

金沙江是我国水能资源最为丰富的江河之一，但在金沙江上修建水库，泥石流是经常遇到的地质灾害问题。文章在详细调查了库区地层岩性、构造、滑坡的前提下，结合金沙江某水库区泥石流发育与降雨、地层岩性、构造、滑坡及地形地貌的关系深入探讨了库区泥石流的类型、规模、特征及发育阶段。研究表明：(1)本库区泥石流活动历史久远，泥石流地质灾害活跃，在部分泥石流地质灾害衰退的同时，更多的泥石流沟在逐步形成：(2)水库蓄水，减小了泥石流沟的比降，会在一定程度上降低泥石流的活动性及破坏能力；(3)泥石流对水库的影响主要为输送泥沙，造成水库淤积。     

某滑坡坝溃坝可能性以及对下游拟建某电站影响分析

自然形成的滑坡坝很容易发生溃坝。通过对某滑坡坝溃坝情况计算表明：即使在溃坝情况下，也不会对下游人民生命财产和拟建某电站的安全运行构成威胁，也不会诱发别的地质灾害。     

宝鸡簸箕山滑坡地震勘探

宝鸡簸箕山滑坡是宝鸡市区的一个大型滑坡,危及许多市民及建筑的安全。为进一步了解滑坡体的细微结构及其滑面形态,中奥双方于1999年和2000年两次对簸箕山滑坡进行了地震勘探。本文详细介绍了地震勘探测线布设、各条测线的勘探及分析、解释结果,为该滑坡的科学治理提供了背景性参考资料。     

利用GIS进行地震滑坡分析

地震滑坡是由地震引发的一种十分严重的震害，它对人类生命财产所造成的损失是巨大的。地震滑坡事件是一个复杂的过程．影响因素多种多样，滑坡的形成机制十分复杂。为正确认识地震滑坡，以1920年宁夏海原大地震为例，利用GIS强大的空间分析功能，多角度、多层次地对地震滑坡的形成、发生、发展机制进行了研究。指出了用GIS技术研究地震烈度等对地震滑坡的影响作用、合理评价和预测某一地区地震滑坡发生的可能性。     

Exploration and discovery in Yellowstone Lake: results from high-resolution sonar imaging, seismic reflection profiling, and submersible studies

‘No portion of the American continent is perhaps so rich in wonders as the Yellow Stone’ (F.V. Hayden, September 2, 1874)Discoveries from multi-beam sonar mapping and seismic reflection surveys of the northern, central, and West Thumb basins of Yellowstone Lake provide new insight into the extent of post-collapse volcanism and active hydrothermal processes occurring in a large lake environment above a large magma chamber. Yellowstone Lake has an irregular bottom covered with dozens of features directly related to hydrothermal, tectonic, volcanic, and sedimentary processes. Detailed bathymetric, seismic reflection, and magnetic evidence reveals that rhyolitic lava flows underlie much of Yellowstone Lake and exert fundamental control on lake bathymetry and localization of hydrothermal activity. Many previously unknown features have been identified and include over 250 hydrothermal vents, several very large (>500 m diameter) hydrothermal explosion craters, many small hydrothermal vent craters (1–200 m diameter), domed lacustrine sediments related to hydrothermal activity, elongate fissures cutting post-glacial sediments, siliceous hydrothermal spire structures, sublacustrine landslide deposits, submerged former shorelines, and a recently active graben. Sampling and observations with a submersible remotely operated vehicle confirm and extend our understanding of the identified features. Faults, fissures, hydrothermally inflated domal structures, hydrothermal explosion craters, and sublacustrine landslides constitute potentially significant geologic hazards. Toxic elements derived from hydrothermal processes also may significantly affect the Yellowstone ecosystem.     

Landslide susceptibility analysis using GIS and artificial neural network

The purpose of this study is to develop landslide susceptibility analysis techniques using an arti?cial neural network and to apply the newly developed techniques to the study area of Yongin in Korea. Landslide locations were identi?ed in the study area from interpretation of aerial photographs, ?eld survey data, and a spatial database of the topography, soil type and timber cover. The landslide‐related factors (slope, curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter) were extracted from the spatial database. Using those factors, landslide susceptibility was analysed by arti?cial neural network methods. The landslide susceptibility index was calculated by the back‐propagation method, which is a type of arti?cial neural network method, and the susceptibility map was made with a geographic information system (GIS) program. The results of the landslide susceptibility analysis were veri?ed using landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. A GIS was used to ef?ciently analyse the vast amount of data, and an arti?cial neural network to be an effective tool to maintain precision and accuracy. The results can be used to reduce hazards associated with landslides and to plan land use and construction. Copyright © 2003 John Wiley & Sons, Ltd.