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。     

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。     

天山1号冰川厚度和冰下地形探测与冰储量分析

通过对天山乌鲁木齐河源 1号冰川的雷达回波探测 ,清晰地揭示出冰川底部冰 /岩界面的位置及其起伏变化特征 ,显示出雷达波对山地冰川良好的穿透能力和对冰下地形的高分辨能力 ,冰川雷达测厚的误差小于 1 .2 %。研究结果显示 ,1号冰川东支冰川平均厚度为 5 8.77m ,西支冰川平均厚度为 44.84m ,冰体厚度最大值发育于冰川中部趋于主流线位置。冰川冰储量计算表明 ,东支冰储量为 0 .0 5 1 868km3,西支冰储量为 0 .0 2 0 2 1 0km3。表面和底部地形有明显差异 ,主要因冰川动力过程对基岩强烈的地貌作用所致 ,意味着冰床的起伏地形对冰川浅层冰体的运动过程影响不显著。     

LiCl-NiCl2熔盐体系相图

用原子参数 模式识别方法研究AX BX2 型卤化物熔盐体系形成固溶体的规律性 ,同时预报了LiCl NiCl2 熔盐体系能形成以LiCl为基的广泛固溶体。采用热分析法和X 射线衍射分析等实验测定了LiCl NiCl2熔盐体系相图 ;结果与计算预报相符。     

TEM和纵向四极测深探测管网研究

各种地球物理探测方法均有其应用上的优势。联合应用脉冲瞬变电磁法（TEM）和纵向四极测深法各自的方法技术特点，解决工程勘察中超浅层的管网分布与定位问题：TEM进行场地普查，而纵向四极测深进行准确定位。实践证明，TEM和纵向四极测深联合应用是管网探测快速、经济且有效的手段，在工程勘察领域中具有广阔的应用前景。     

多右端非对称位移方程组的GMRES种子投影方法

考虑用GMRES方法求解多右端非对称位移方程组(A-σjI)x^(j)=b^(j)，1≤j≤p。基于Smith的求解多右端方程组的种子投影思想，提出了求解上述位移方程组的GMRES种子投影方法，利用种子方程组产生的Krylov子空间来求近似解。本文给出了近似解的误差界，最后数值结果显示了该方法的有效性。     

由电法勘探资料计算地下水矿化度的软件系统

地下水矿化度是划分地下咸淡水的重要参数，过去人们常用手工计算。但由于涉及的参数众多，计算过程烦琐，人工计算不仅速度慢，而且容易出错。一种利用Visual Fortrain和C^ 编制的批量计算地下水矿化度的软件系统，可有效地解决这类烦恼。该系统根据事先由电(磁)测深资料反演出的真电阻率数据，借助水文地质资料划分出不同区块的孔隙度、水质类型及温度等参数，自行判别计算公式，在数秒内可算完一条电(磁)测深剖面下的地下水矿化度。经实际试算，效果令人满意。