The paleogeographic implications of loess mounds: Laizhou Bay plain

The loess mounds are a newly discovered type of landforms on the Laizhou Bay plain south of the Bohai Sea. Research shows that they were formed in the late period of the late Pleistocene when the Bohai Sea was exposed in the period of late Wümu glacial age and changed into plain. As the exposed area is not protected by vegetation, the sediments at the sea bottom are blown and transported southward by strong north winds, and deposit on the coastal plains. As thick loess is accumulated, the loess mound landforms are formed in the direction of down wind.     

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.     

Sectoral Shifts and Occupational Migration in the United States

This research is grounded in notions of differential economic restructuring across employment sectors and geographic space, as well as migration selectivity by occupation. A series of unconstrained competing‐destinations models were employed to analyze the response by workers in thirteen occupational categories to sectoral employment change, average wages, and distance. As was hypothesized, workers in occupations that require high levels of education and skills are more responsive, in terms of migration, to economic opportunities in alternative labor‐market areas. However, the results do not support the hypothesis that highly educated and skilled workers migrate longer distances. Further investigation suggests that opportunities for highly educated and skilled workers may be clustering in relatively few areas that are in relatively close proximity.     

贵州雷公山背斜南段锑矿控矿条件浅析

本文根据雷公山背斜南段的几个锑矿床的勘查资料，结合1：5万区调和相应的科研成果，总结该区锑矿床的共同特征，并对其控矿条件进行分析，认为逆冲断层、剪切带控制锑矿田的分布，“褶皱 断层”控制锑矿床分布，单条剪切带及后期脆性断层控制锑矿体的产出。     

斑岩铜矿中铂族元素的研究现状及展望

文章对当前国内外斑岩铜矿中铂族元素的研究现状进行了较为详细的阐述，指出研究与铂族元素矿化有关的斑岩铜矿的流体包裹体及其富集成矿过程，以及铂族元素对斑岩铜矿成矿环境，成岩岩浆的起源和成矿物质来源示踪是该领域的发展方向和前沿。     

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。