武汉市区第四系含水层地下水有机污染敏感性研究

在详细调查武汉市水文地质条件和地下水污染现状的基础上，获得了高精度的武汉市水环境中微量有机污染物的组成数据。所检测出的有机组分达30余种，以苯及相关苯系物为主，污染程度较高的地下水主要分布在人口密集区和工业，商业区，应用改进的DRASTIC模型－地下水污染敏感性评价模型，在GIS平台上，编制了武汉市区地下水污染敏感性分区图。根据其评价结果，建议集中对那些敏感性相对较高的区域采取有效的环保措施，开发利用时应作出风险评价。     

青藏高原各拉丹冬地区冰川变化的遥感监测

以位于青藏高原长江源头的各拉丹冬地区冰川为例, 利用2000年的TM数字遥感影像资料、 1969年的航空相片遥感资料、地形图及数字地形模型, 通过遥感图像处理和分析提取研究区小冰期最盛期(LIA)、 1969年和2000年的冰川范围, 并在地理信息系统技术支持下分析该地区冰川的进退情况. 研究结果表明, 该地区1969年冰川面积比小冰期最盛期的冰川面积减少了5.2%, 2000年的冰川面积比1969年的冰川面积减少了1.7%. 从1969年到2000年最大冰川退缩速度为-41.5 m*a-1, 最大冰川前进速度为+21.9 m*a-1. 本区的冰川基本处于稳定状态, 冰川退缩的速度不是太大, 并有前进的冰川存在.     

MAPGIS管网开发平台的设计

设计了一个通用的管线信息系统开发平台。该开发平台构筑于大型GIS基础软件MAPGIS之上，具有完备的数据模型，强大的数据包容能力。采用多层次体系结构，包含网络数据管理与网络分析、管网工作区管理、专业管网工作区管理、管网工程管理、管网实用服务工具包、管网管理类库和控件库等多个模块。该开发平台同时支持API和组件接口，二次开发便利灵活，可以较好地满足管网应用系统的。在该平台基础上已开发出多个成功的管网信息系统，证明它具有广泛的应用前景。     

Understanding Perturbation on the Simplex: A Simple Method to Better Visualize and Interpret Compositional Data in Ternary Diagrams

Perturbation is an operation defined on the simplex and can be used for centering compositional data in a ternary diagram, applying objective criteria. Because a straight line in the original diagram is still astraight line in the perturbed diagram, gridlines or compositional fields defined by straight lines can easily be included in the operation. Simultaneous perturbation of data, gridlines, and/or compositional fields is shown to improve both visualization and graphical interpretation of compositions in ternary diagrams. This is illustrated by some examples using simulated as well as real data.     

新疆数字地震前兆台网的数据传输功能分析

为了进一步提高地震前兆数据的传输效率和准确性 ,实现前兆数据采集和管理的自动化 ,对新疆地震局数字化地震前兆台网中心的功能、数据采集原理、数据传输特征进行了分析 ,认为数字化观测技术为地震前兆数据的采集、传输提供了一种高效、可靠的手段     

GIS支持下青海湖地区草地蝗虫发生的地形分析

草地蝗虫发生的地形分析是建立草地蝗虫发生预报模型的基础，以青海湖地区为实验区，在Arc/Info和ArcView地理信息系统的支持下，进行草地蝗虫发生与各地形变量（包括海拔高度、坡度与坡向）的叠置分析，提取草地蝗虫发生的地形信息数据库，然后，分别采用T－检验（对连续变量－海拔高度和坡度）和卡方（Chisquare)检验（对类变量－坡向类型）进行各地形变量对草地蝗虫发生的差异显著性检验，结果表明，海拔高度和坡度对草地蝗虫发生的影响极为显著（显著性水平p=0.000)，而坡向地草地蝗虫发生的影响不是特别明显（显著性水平p=0.039)。     

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

油气化探综合异常模糊划分动态聚类分级评价

通过选取描述油气化探综合异常的各种特征参量，借助模糊数学工具，利用最优聚类中心所具有的代表特征，建立分级指标，对油气化探综合进行分级评价。鄂尔多斯盆地油气化探综合异常的评价实例结果表明，利用油气化探综合异常各种特征参量进行动态聚类分级评价是可行的。     

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.