资源勘查图件计算机辅助编绘系统的结构分析与开发策略研究

如何站在信息系统的高度来进行资源图件计算机辅助编绘系统的快速开发，是资源信息系统领域计算机应用的一个重要研究课题。在结构分析的基础上，将资源图件计算机辅助编绘系统划分为图形数据库，应用图形系统和基础图形系统3个基本部分。对于复杂资源图件的计算机辅助编绘，需要地矿点源信息系统的支持。在基础图形系统上进行专业编绘程序的二次开发是高效建立图件编绘系统的最佳途径，并需注意GIS集成应用，三维可视化，人工智能和参数化设计等方法的运用，而系统平台开发是复杂应用的高级阶段。该思想可应用于煤及油气勘探，工程开发，矿区管理及区域地质等诸多领域的软件开发中。     

全谱直读等离子发射光谱法直接测定天然矿泉水中的SO2-4

采用全谱直读等离子发射光谱(ICP-AES)直接测定天然矿泉水中SO2-4的方法在优化工作条件下SO42-的测定下限为0.09mgL-1,方法简便,快速,适用于批量生产.     

固溶体矿物的性质对计算河南西峡红柱石矿形成条件的影响

自然界的大多数造岩矿的是由两种或多种组分混合而成的固溶体。因此，在矿物，岩石和矿床等地质体的平衡热力学研究中不可避免地要涉及固溶体矿物热力学地为的问题。河南省西峡县杨乃沟红柱石矿泥质片岩中仅出现红柱石而没有蓝晶石和夕线石，红柱石矿物包裹体测温结果为420－600℃。通过矿物共生平衡p-t曲线所计算出的片岩形成温度和压力条件也应位于上述岩石学和岩相学等特征所限定的范围内。以此为基础，通过构筑矿物共生p-t平衡曲线的方法，对片岩中若干固溶体矿物理想性／非理想性问题进行了研究。其中斜长石必须以非理想固溶体处理，而石榴子石，黑云母和白云母视作理想固溶体的计算结果同片岩的岩石学和岩相学等特征相一致。     

成矿系统与矿化网络研究

成矿系统研究体现了系统观和历史观的结合，是现代矿床学的一个发展趋势，本文简要总结了成矿系统及演化论的几个要点，即（1）按构造动力体制划分成矿系统大类；（2）我因耦合，临界转换的成矿作用机理；（3）矿床系列和异常系列构成的矿化网络；（4）矿床形成-变化-保存的演变过程，在此基础上，作者提出矿化网络是在一个成矿系统中形成的诸类矿床和相关异常的时空分布和结构，它是成矿系统的具体内容的表达。对成矿系统的深入研究应从矿化网络入手，着重在以下几个方面；（1）各类矿床的发育程度；（3）各类矿床的空间关系；（3）各类矿床的时间关系；（4）各类矿床的成因联系；（5）各类矿床被改造的情况。这些都是矿床学和找矿预测研究的基础内容。     

得尔布干成矿带中段八大关——新峰山成矿地质条件分析

得尔布干成矿带中段八大关—新峰山地区断裂构造发育 ,北东向得尔布干深大断裂是重要的控岩控矿构造。与成矿有关岩浆岩主要为燕山期的中性—中酸性侵入岩及次火山岩。赋矿层位主要有上库力组、塔木兰沟组及谢尔塔拉组。在分析典型矿床的成矿地质特征基础上 ,进一步指出找矿方向     

华北板内深部构造

华北板块的形成经历了早前寒武纪、燕山期及喜马拉雅期3个主要构造发展期，由于华北板块自身运动及所受应力场的作用，加之上地幔岩石圈的不均一性等因素，在中、新生代形成许多特殊的板内构造块。综合应用地质、地球物理和地球化学的成果，对华北板内深部结构进行了研究。从深部构造角度划分出6个金及金金属成矿带、4个金刚石成矿带，并对华北地区的地震及地热资源与新生事大陆裂谷的关系进行了探讨。     

模拟酸雨对福建四种山地土壤的淋溶与风化作用

在不同pH值 (2 .0、3.5、5 .0、6 .0及蒸馏水 )的模拟酸雨作用下 ,对福建四种类型山地土壤进行了为期 12 0d的淋溶试验。结果表明 ,盐基离子的淋溶量随模拟酸雨pH值的降低而增加 ,尤其当pH≤ 3.5时 ,增加最明显。 4种类型土壤中各离子淋失量大小均为 :Ca2 >Mg2 >K >Na ,这与原土壤中交换性盐基含量一致。在pH≥ 3.5的酸雨作用下 ,土壤中铝的淋失较少 ,而当酸雨pH =2 .0时 ,铝的淋失骤然升高。pH2 .0的模拟酸雨不仅增加了土壤铝离子的释放 ,而且加速了矿物的风化。在pH2 .0的模拟酸雨淋溶下 ,红壤、黄壤、山地草甸土和紫色土中由矿物风化输入的 5种阳离子总量分别为 2 .94、4 .33、4 .35和 6 .0 4 (cmol kg)土 ,显著大于其它酸雨处理。土壤对酸沉降的缓冲作用由阳离子交换、氢氧化铝水解以及原生矿物风化缓冲作用三部分组成 ,其相对重要性不仅决定于土壤酸度、盐基饱和度和矿物组成 ,而且与酸雨的pH值密切相关     

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.