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.     

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。     

辽东青城子矿集区金、银成矿时代及地质意义

为确定辽宁青城子矿集区金、银的成矿时代 ,分别以含金硅化岩和含银网脉状石英晶体内流体包裹体为对象 ,应用Rb_Sr法测得金、银成矿的等时线年龄为 (2 33± 31)Ma～ (2 34± 14 )Ma ;作为对Rb_Sr法测年结果的验证 ,又以与贵金属矿石矿物共生的热液石英为对象 ,用40 Ar/3 9Ar快中子活化法测得坪年龄tp=(2 38.78± 0 .74 )Ma～ (2 38.80± 0 .6 0 )Ma,等时线年龄ti=(2 39.4 6± 1.13)Ma～ (2 4 0 .35± 0 .88)Ma。Rb_Sr和40 Ar/3 9Ar两种方法的测年结果基本一致 ,与本区印支期岩浆活动时代〔2 17.6Ma～ (2 30 .7± 5 )Ma〕吻合。     

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。     

陕西凤县八卦庙金矿床成矿地质特征及外围找矿前景分析

阐述了八卦庙金矿床经历了长期、复杂的非线性成矿过程，着重分析了：含矿建造为与同生伸展断裂伴生的海底喷流热水沉积岩、热水浊积碎屑岩；脆-韧性剪切构造控制着主成矿过程；后期幔源岩浆气液叠加富集使其成为超大型金矿床。指出矿床成因为热水同生沉积细碎屑岩-剪切蚀变岩-幔源岩浆气液叠加富集型。八卦庙金矿床外围仍有巨大的找矿前景。     

萃淋树脂吸附法测金

对萃淋树脂在酸性介质中吸附解脱金及相关体系的条件选择，以及萃淋树脂在生产方面的应用前景进行了实验研究。结果表明，萃淋树脂在稀王水介质中能较好地吸附金，用硫脲解脱金简便完全，检测限达0．002μg／g，吸附率达100％，解脱率99．8％。     

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.     

李坝式金矿床成矿作用及矿床类型探讨

李坝式金矿床成矿作用过程分为成矿元素初始富集期和构造叠加热液改造成矿期，岩浆活动主号是通过改变矿床地质体物化条件而促进矿化体的形成，并有部分岩浆物质参与成矿。该矿床与国内外经典卡林型金矿床具有较大的可比性，但是由于缺乏低温矿物组合以及与岩浆活动的亲密性证据，该矿床应属广义上的卡林型金矿床。     

金山金矿热液蚀变粘土矿物特征及水-岩反应环境研究

粘土矿物是流体作用过程中水—岩反应的产物，因此其特征反映了流体活动的特征和水-岩反应的环境。金山金矿蚀变粘土矿物主要由伊利石和绿泥石组成，其中，蚀变糜棱岩中的伊利石含量大于绿泥石含量；而蚀变超糜棱岩中的绿泥石含量大于伊利石含量。蚀变糜棱岩中伊利石的多型为2M1，超糜棱岩中为2M1和1M。金山金矿蚀变绿泥石的成份分析结果表明其为富铁绿泥石，由蠕绿泥石、铁镁绿泥石和密绿泥石组成，绿泥石中Fe、Mg质组分不仅来自围岩，而且也有一部分来自流体。利用地质温度计计算绿泥石的形成温度为206-258℃，流体的f(O2)为10^29.56～10^-31.48。本文认为金山金矿热液蚀变为酸性蚀变，其环境为还原环境，流体作用的水／岩比较高；在水—岩反应过程中，流体中的Fe、Mg、Si为带出组分。粘土矿物的形成机制为溶解—迁移—沉淀。