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。     

当前地震台站财务管理工作之浅见

本文简要介绍了台站财务管理的现状 ,针对目前台站管理中出现的一些问题 ,阐明加强台站财务管理与会计核算改革的必要性 ,并粗略提出地震台站财务管理工作的改革方向与对策。     

库尔勒数字地震台台基噪声分析

选取库尔勒地震台FBS—3数字地震仪不同时段的无震记录资料，运用傅里叶变换对其台基噪声进行频谱分析，并计算台基的平均噪声水平及仪器的实际动态范围。库尔勒地震台数字地震仪台基噪声主要来自随机干扰，噪声频段相对稳定，噪声幅度随时段有所变化。观测系统实际动态范围符合数字地震仪架设的要求。     

容易造成误判的几例电源故障及排除

在仪器设备的各种故障中，由电源引起的故障所占比例最高，而表现形式又各不相同。本文通过几例仪器设备的维修过程，说明容易被忽视的一些问题和有关测量手段需要注意的事项。     

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。     

格尔木地磁台观测环境测试与分析

通过实验测试和计算，分析了高压输出电线等干扰源对格尔木地磁台观测环境的影响，进一步对格尔木地磁台观测环境现状，及今后观测环境保护工作的要点进行了综合分析讨论。     

德令哈地震台震级偏差及台站校正值的研究

利用德令哈地震台DD—l记录到的746个近震，对其测定的震级偏差进行统计计算，发现DD—l测定的震级偏小。经过分析，指出了产生偏差的原因，并给出了校正值C= 0．15。     

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.