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.     

智能交通系统中的公交车辆指挥调度体系结构

该文阐述公交智能指挥调度系统总体框架，结合智能交通系统ITS(Intelligent Transport Systems)对公交智能化调度的逻辑结构和物理结构要求，分析公交车辆智能化调度系统结构体系及其模型化，探讨智能调度策略和调度机理。根据该系统总体设计框架构思，了解系统各功能间的相互关系，优化公交车辆运营管理模式，以改善现有公交系统的管理水平以及运营效率，为公交指挥调度系统的完善、提高提供可靠技术基础。     

基于GTP的地下工程与围岩一体化真三维空间构模

针对地下工程开挖体的基本特征及其真三维建模要求，在广义三棱柱(GTP)模型的基础上进行扩展，提出采用E—GTP模型来构建地下工程开挖体的真三维模型。通过与其围岩地质体的G—GTP模型的耦合，实现了基于GTP的工程开挖体与围岩地质体的一体化建模。该模型将地下三维空间实体抽象为点、线、面、体四类对象，通过结点、TIN边、侧边、TIN面、侧面和GTP体元共6个基本几何元素进行统一表达。该文重点讨论了E—GTP的建模原理与方法，并介绍了基于面向对象思想的一体化空间数据结构。     

多维植被信息系统及其应用前景

3S(地理信息系统GIS、遥感RS、全球定位系统GPS)及其相关技术的发展，使得植被信息系统的构建和应用成为可能。早期的植被图没有涉及到高度维和时间维，其应用受到很大的限制。因此有必要建立一种基于时空的多维植被信息系统，它的组成包括地理信息系统GIS、数据处理模块DPM、数据存取模块DAM、植被分析模块VAM和交互显示模块IDM，可用于植被的时空分析、三维景观显示、植被—地形—气候关系研究以及植被综合管理等方面，特别适合于山地植被的研究。多维植被信息系统具备立体直观的用户友好界面和强大的时空数据处理能力，并可通过数据库和网络进行资源共享，使植被研究和管理自动化、无纸化，其应用前景广阔。     

A2O·3MoO3·3H2O(A＝Na、K)晶须的合成与表征

在常温条件下合成了两种钾、钠的钼酸盐晶须 ,用FT -IR和XRD对其结构进行了分析 ,SEM照片清晰表明这两种化合物为针状晶须。     

用废聚苯乙烯泡沫制备胶粘剂

本文研究了回收废旧聚苯乙烯(PS)泡沫塑料制备PS胶粘剂的方法。以废旧聚苯乙烯泡沫塑料为主要原料,选择低毒性、相溶性好的有机溶剂,使其溶解,再与聚乙烯醇(PVA)水溶液共混,制得水包油乳液。加入不同改性材料,制得改性胶粘剂。     

蒙甘新相邻(北山)地区金铜矿床时空分布特征及成矿作用

文章首次对蒙甘新相邻(北山)地区各类金、铜和铜—镍矿床(点)地质特征、成因类型和空间分布特点进行了系统总结，论证了金、铜和铜—镍成矿作用与古生代岩浆活动的关系，对区域地壳演化过程中金、铜和铜—镍成矿的动力学机制进行了深入讨论。研究结果表明，该区的金矿床(点)大体可划分为变质岩型、火山岩型、斑岩型和深成侵入岩型；铜矿床(点)有斑岩型、夕卡岩型和铜—镍硫化物型。金和铜矿床(点)大都沿古板块汇聚带分布，与海西期火成岩具密切的时空分布关系，它们是古板块对接碰撞期和碰撞期后大规模构造—岩浆活动的产物。