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.     

贵州雷公山背斜南段锑矿控矿条件浅析

本文根据雷公山背斜南段的几个锑矿床的勘查资料，结合1：5万区调和相应的科研成果，总结该区锑矿床的共同特征，并对其控矿条件进行分析，认为逆冲断层、剪切带控制锑矿田的分布，“褶皱 断层”控制锑矿床分布，单条剪切带及后期脆性断层控制锑矿体的产出。     

细菌浸铜技术在紫金山铜矿的应用

介绍了生物浸矿技术在紫金山铜矿的应用及在这一过程中发现的一些问题，并提出了有待进一步解决的一些问题。     

斑岩铜矿中铂族元素的研究现状及展望

文章对当前国内外斑岩铜矿中铂族元素的研究现状进行了较为详细的阐述，指出研究与铂族元素矿化有关的斑岩铜矿的流体包裹体及其富集成矿过程，以及铂族元素对斑岩铜矿成矿环境，成岩岩浆的起源和成矿物质来源示踪是该领域的发展方向和前沿。     

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。     

土屋-延东斑岩型铜(钼)矿床多源信息找矿模型

为提高东天山铜镍成矿带内的矿产勘查效益，文章综合了区域成矿环境、矿床特征、地球物理场、地球化学场等成矿信息，尝试建立了土屋—延东斑岩型铜钼矿床的多源信息找矿模型。认为成矿带内铜矿资源远景良好，必将成为我国又一个铜矿资源潜力区。     

我国钴矿矿产资源及其成矿作用

钴广泛应用于军事、钢铁、电器、机械等方面，它具有亲铁亲硫的双重性，在各类岩浆岩中的分布不均匀，由超基性岩向酸性岩，钴的含量迅速降低。我国钻矿资源呈现富矿少、贫矿多的特点，并且铬矿主要分布在甘肃、青海、新疆、吉林、陕西、云南、四川等地。按照矿床形成的成矿环境、矿床岩石组合，矿床类型划分为：与镁铁质-超镁铁质岩有关的硫化物钻矿、与海相火山喷发有关的钻多金属矿、与斑岩有关的铜钻矿、与砂岩有关的钻矿、与中酸性侵入岩有关的矽卡岩铁钻矿、与风化作用有关的钴矿等六类。     

层控夕卡岩型矿床成矿系统的元素活动性及质量迁移--以铜陵冬瓜山铜矿床为例

应用改善了的质量平衡方程研究冬瓜山层控夕卡岩型铜矿床成矿系统的元素活动性及质量迁移，结果表明：系统中Zr,Hf,Y和Nb为不活动元素，其余均为活动元素。主量元素中仅CaO为迁出组分，其质量迁移量为84％，其余均为带入组分，带入量最大的SiO2的质量迁移量高达2384％；稀土元素均为带入组分，其中，中稀土较轻稀土和重稀土的活动性更强；其它微量元素中，带入序列由强到弱为：Cr→Ta→Sn→Th，迁出序列由强至弱依次为：U→Sb→Ni—Sr→Ba→Bb→Co→W；成矿元素Cu和Ag为带入组分，其中Cu的带入量最大，质量迁移量高达1200％。本文还得出两点具普适性认识：①稀土元素较其它微量元素对体系条件的变化更为敏感，且往往表现出明显的规律性变化，能更好地示踪地质地球化学过程。②不同元素在不同体系中的行为特征差异极大，仅凭经验判定某一元素为不活动元素往往不可靠。因此，在开展质量平衡研究时，首先采用适当方法确认体系的不活动元素是一项不可或缺的前提工作。     

夏塞地区银、锡多金属矿成矿规律新认识

夏塞地区是西南三江(金沙江、澜仓江、怒江)地区东缘的一个重要成矿带，带内银、锡矿产责源十分丰富。本文认为其成矿物质来源于花岗岩体和地层，构造和岩体是区内的主要控矿因素，地壳演化与成矿作用关系十分密切，矿床类型为分布于岩体内接触带的矽卡岩型矿床和分布于岩体外接触带的热液型矿床。