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.     

Economic Filters Applied to Quantitative Mineral Resource Simulations

The U.S. Geological Survey has developed a technique that allows mineral resource experts to apply economic filters to estimates of undiscovered mineral resources. This technique builds on previous work that developed quantitative methods for mineral resource assessments. A Monte-Carlo calculation uses mineral deposit models to estimate commodity grades and tonnages of undiscovered deposits. The results then are analyzed using simple estimates of capital expenditures and daily operating costs for a mine and associated mill. The daily operating costs and the value of the ore are used to calculate the net present value of the deposit, which is compared to the capital expenditures to determine whether the deposit is economic. Repetition of these calculations for many deposits produces a table that can be interpreted in terms of the probability of there being deposits that have anet present value exceeding some specified amount. Sample calculations indicate that applying economic filters to simulated mineral resources might change the perception of the results compared to presenting the calculations in terms of the expected mean gross-in-place value of the minerals.     

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.     

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。     

沃尔什变换在测井曲线分层中的应用研究

在岩性复杂的变质岩地区实施的大陆科学钻探，是通过采集井下不同深度岩石物理化学性质和物理场的各种数据，研究地壳特征及演化过程的各种科学问题。利用测井资料达到岩性剖面的重建是大陆科学钻探的重要目的之一。章利用沃尔什变换对钻孔岩性剖面进行分层，大重建的岩性剖面结果可以看出，该方法对于变质岩地区不同岩性的划分还是比较有效的。     

Analysis of Karst Tufa from Guangxi, China

The paper presents an analysis of characteristic karst tufa from Guangxi, China, which has not been studied before. A comparison with tufa from Dinaric Karst of Croatia is discussed in view of the C-type climate. The major mineral is calcite. Minor minerals are quartz and dolomite, depending on location. The content of calcium carbonate varies from 65% to 92%, and that of magnesium carbonate from 0.03% to 1.77%. Among other elements, the most abundant are Fe, from 0.02% to 1.50%, and Ti, from 0.15% to 0.27%. Many other trace elements (V, Cr, Mn, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Y, Zr, Hg and Pb) are also present. Specific activity of radionuclides 40K, 232Th, 137Cs, 226Ra and 238U varies from sample to sample. Concentration of U in tufa is close to that reported for sedimentary carbonate. Low concentration of 137Cs indicates that this part of the world was not exposed to nuclear explosions. The concentration of 226Ra is the highest in Mashan County. The ratio 238U/226Ra (0.21-0.71) in tufa from Mashan Co     

贵州峨眉山玄武岩喷发期的岩相古地理研究

贵州峨眉山玄武岩喷发，从动态的角度可以分为茅口期晚期和龙潭期（吴家坪期），龙潭期又可分为三个喷发旋回，对应于四个不同的岩相古地理环境，体现了东吴运动在造成贵州地区地壳抬升、下沉和接受最大海侵之后，又上升、拉张、沉陷带发生地裂（又称峨眉地裂）以及地幔物质喷溢等地质活动，具间歇性和多旋回性的特点。本文从研究海陆变迁入手，揭示峨眉山玄武岩喷发与沉积作用的内在联系，进而探讨其与金、锑等矿产的成因联系，提出该期各相区与成矿区的形成模式。通过对贵州峨眉山玄武岩不同喷发期岩相古地理的研究可以看到，茅口期晚期和龙潭期早期海域的沉积韵律和相带展布格局与玄武岩喷发的间歇性和多旋回性特征完全一致。玄武岩的喷发为成矿提供了物质基础，玄武岩喷发的间歇期又为沉积矿产的富集提供机遇。这种岩浆期后气液以富硅和二氧化碳为特征的玄武岩，本身富含铁、锰、铜、铅、锌、锑、砷、汞、金、银、氟、磷以及一些稀散和放射性元素等成矿组分。在喷发过程中，气液成分有一定变化，各阶段和离岩浆的远近距离不同以及喷发性质和环境差异，形成了火山气液矿床、火山沉积矿床和沉积矿床的不同成矿带。     

大洋多金属结核研究概况

从多金属结核的分布及富集规律、物质成分、内部结构、形成机制等方面论述了目前国内外的一些研究动态,对目前在这一研究领域提出的一些新概念、新方法作了一定的介绍.在分析和总结前人研究成果的基础上,指出了在这一研究领域还存在的一些问题及今后研究的方向.     

测井资料交会图法在火山岩岩性识别中的应用

在火山岩储层研究中，岩性识别显得越来越重要。在评述目前常用的岩性识别方法后，重点以测井资料交会图法为例，以松辽盆地徐家围子断陷升平气田深层白垩系营城组火山岩为对象，优选出密度测井、自然伽玛测井、声波测井、电阻率、钍铀等测井项目的数据进行交会，编制出测井曲线交会图版，并以此为依据识别出该区的火山岩主要岩性有：安山岩、玄武岩、流纹岩和凝灰岩等。识别结果与实际情况相吻合。     

基于MAPGIS的矿产资源管理系统建立

结合Visual C++和Visual Foxpro6.0开发矿产资源GIS系统过程,讨论了在地理信息系统平台MAPGIS下,采用API函数及MFC类库实现矿产资源管理系统的关键技术.