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.     

Re-placing the Pilbara's mining unions

This paper examines the attempt by mining management in Western Australia's Pilbara to replace mining unions—quite literally—by removing them from the processes of representation and bargaining. It analyses the way in which those unions have tried to re-place themselves, in the senses of transforming themselves in those spaces in which they were already operating and reviving themselves where they were not. Where unionists have succeeded in these engagements, it has been by working at a range of geographical scales, using the ‘power of place’ in the Pilbara and reshaping traditional geographies of union organisation. It is suggested here that many of these emergent outcomes are the result of the embeddedness of geographically specific historical structures along with new intersections of nationally and locally scaled labour politics.     

试述福建顺昌南舟萤石矿地质特征

顺昌南舟萤石矿位于顺昌县埔上镇南舟村境内，1996年福建省区域地质调查队开展1：5万埔上幅区调时发现矿体，后开展普查工作表明该矿床规模大、交通方便，易于开采，现已投入开发利用。南舟萤石矿成矿地质特征在闽北丰富的萤石资源中具有一定代表性。     

福建建宁黄埠地区金矿化基本特征

通过对建宁黄埠地区金矿地质特征、矿化特点、成矿地质规律的研究，认为该区成矿年龄为91Ma，提出了北西西向碎裂带为主要的含矿构造，并指出该区具有较好的找金远景。     

从江县翁浪金矿床容矿岩石与围岩蚀变及其找矿标志

文章简单介绍了构造蚀变岩型金矿翁浪金矿床的容矿岩石和围岩蚀变特征，指出该类矿床的找矿标志，对进一步寻找此类矿床有一定的意义。     

从流体包裹体研究探讨金属矿床成矿条件

根据作者多年来对流体包裹体的研究成果，参考了国内外的研究结论，提出了一个新的成矿流体分类方案，并评述了我国若干典型金属矿床的研究状况，由此探讨了金属矿床的形成机制。     

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。     

醋酸丁酯萃取-无火焰原子吸收法测定化探样品中痕量金

在KBr存在下,用小体积醋酸丁酯萃取金后,用无火焰原子吸收法测定化探样品中痕量金的分析方法较化学富集光谱法具灵敏度高(检出下限可达0.05×10-9)、精度好、简便快速、成本低等优点.通过批量化探样的检验,证明该方法足已满足化探样品对金的测定要求.     

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。