CT图像存档与管理解决方案

本文针对数字影像设备（如CT、MRI等）日益突出的图像存储和管理问题，进行深入的分析和研究。鉴于不同厂家和时期设备配置不同，提出了4种切实可行的解决方案：胶片扫描，视频采集，光盘或MOD输入，DICOM接口连接，完全解决了图像长期大量存储的问题。同时具有网络联机查询，图文报告，电子病历管理，激光相机接口等丰富的增值功能，是数字化医院的理想选择。     

南向行星际磁场事件与磁暴关系的研究

利用172-182年IMP-8飞船的太阳风观测资料和相应地磁活动性指数Dst和AE,研究了43个南向行星际磁场事件期间太阳风和磁层的耦合问题. 与这43个事件对应的地磁暴是中等的和强的磁暴(Dst＜-50nT). 结果表明:(1) 在43个事件中有11个(约占25.6髎)紧随激波之后,18个处于激波下游流场中(占42髎),其余14个(占33髎)和激波没有关连. 绝大多数事件都伴有太阳风动压和总磁场强度的增加;(2) 当行星际晨昏向电场强度EI＞-4mV/m时,只引起磁亚暴,对Dst指数没有明显影响. 仅当EI＜-5mV/m时,磁亚暴和磁暴才会同时出现;(3) 太阳风动压的增加会增强能量向环电流的输入,但不是密度和速度单独起作用,而是以PK=ρV2的组合形式影响能量的输入;(4) 虽然行星际磁场(IMF)南向分量BZ对太阳风和磁层的耦合起着关键作用,但IMF的BX和BY分量相对于BZ的大小对太阳风向磁层的能量传输也有一定影响. 当BX、BY相对BZ较大时能量耦合加强.     

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.     

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。     

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。