岩体三维不连续裂隙网络及其逆建模方法

通过拟合实测的二维裂隙编录图和钻孔裂隙编录图，优化裂隙三维大小和密度参数，使模型能够准确再现野外所观测到的实际现象，包括开挖面和钻孔上不同裂隙的数量、密度和长度，通过计算实例验证了方法的可行性。     

Seismic risk assessments and GIS technology: applications to infrastructures in the Friuli–Venezia Giulia region (NE Italy)

This paper illustrates the seismic risk preliminary estimates of two different groups of structures located on the territory of the Friuli–Venezia Giulia region (NE Italy) : the first group includes some special industrial plants, and the second group includes bridges and tunnels belonging to the regional highway network. The part of the study on special industrial plants tries to evaluate the degree of expected damage, taking into account their structural typology and ground shaking expressed in terms of macroseismic intensity. The second part of the study is an application of the HAZUS methodology to the tunnels and bridges of a highway network: the combination of expected ground shaking and the construction characteristics lead to very different risk levels, especially when considering the bridges. The resulting damage levels to bridges and tunnels are still only indicative because of the fragility curves used in the evaluations: they were developed for existing bridge and tunnel structural typologies in the U.S.A. Moreover, both examples show the power of GIS technology in storing, elaborating, and mapping spatial data. Copyright © 2003 John Wiley & Sons, Ltd.     

大庆徐家围子地区深部致密砂砾岩气层识别

大庆徐家圈子地区深部致密砂砾岩储层的骨架成分除了含有石英、长石外，还含有许多酸性喷发岩，而且各种骨架成分的含量随深度变化比较大；复杂的岩性使得气的影响被岩性的影响所淹没，从而造成常规气层识别方法失效。针对这一问题，应用神经网络方法准确求取地层颗粒密度和泥质含量，然后对中子和密度测井值进行岩性影响校正和泥质影响校正，使得求出的中子和密度孔隙度仅受地层气的影响，最后将得到的中子和密度孔隙度进行对比，应用中子和密度孔隙度的差异识别气层。     

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.     

A neural network approach for structural identification and diagnosis of a building from seismic response data

This work presents a novel procedure for identifying the dynamic characteristics of a building and diagnosing whether the building has been damaged by earthquakes, using a back‐propagation neural network approach. The dynamic characteristics are directly evaluated from the weighting matrices of the neural network trained by observed acceleration responses and input base excitations. Whether the building is damaged under a large earthquake is assessed by comparing the modal parameters and responses for this large earthquake with those for a small earthquake that has not caused this building any damage. The feasibility of the approach is demonstrated through processing the dynamic responses of a five‐storey steel frame, subjected to different strengths of the Kobe earthquake, in shaking table tests. Copyright © 2002 John Wiley & Sons, Ltd.     

给定内插高程异常值的精度时对GPS水准网格间距的考虑

在已布设GPS水准网的地区，若需内插其中任意一点的高程异常值时，应该了解该内插值的精度。导出了该内插点高程异常值的精度评定方法，并具体给出在我国C级GPS水准网中，该内插点高程异常推估值精度和该地区的地形和栅格重力异常分辨率的数学关系式和实例。在给定内插点高程异常值精度的局域大地水准面时，按不同地形和栅格重力异常分辨率的密度，根据这些数学关系式，可以设计间距合理的B级或C级GPS水准网。     

GPS网数据处理几个问题

本文指出在GPS网平差中有关精度评定、权的确定以及坐标转换所产生的控制点间隙等存在问题，供研究讨论。     

基于人工神经网络的赤潮卫星遥感方法研究

根据赤潮的卫星遥感探测机理，应用人工神经网络技术，建立和利用NOAA AVHRR可见光和热红外波段遥感数据的BP神经网络赤潮信息提取模型。应用实例显示。基于该人工神经网络方法可以提取赤潮发生地点和范围等信息，赤潮探测正确率达到78.5%。研究结果表明，应用人工神经网络方法提取赤潮信息是可行的。本文中建立的BP赤潮信息提取模型适当修改后可移植应用于其它传感器遥感数据进行赤潮信息提取。     

基于RBF神经网络的遥感影像分类算法研究

提出了运用径向基函数神经网络和K均值法进行遥感影像分类的算法，以实际的遥感影像分类为例，通过与传统的最小距离法进行比较，对RBF神经网络分类器的优点进行了归纳，并就算法实施中的一些问题进行了探讨。分析结果表明，RBF神经网络是一种有效的图像分类器。     

千兆网支持下的城市公众GIS

当前GIS技术的发展已经进入到一个新的阶段,呈现出网络化、专业化、智能化、大众化等特点。公众GIS是近年来新兴的GIS应用领域,它主要的目的是面向公众提供信息服务。介绍了城市公众GIS的基本概念、特点和系统功能,在分析了当前公众GIS存在的问题之后,继而提出一种适合宽带网络(千兆以太网)支持下的公众GIS系统结构,并进一步分析了它的功能和用途。