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比利亚谷铅锌银矿位于海拉尔-根河中生代火山盆地北西缘。该矿床赋存于上侏罗统满克头鄂博组酸性火山岩中,受NW向断裂构造控制,主矿体呈脉状产出。火山岩围岩及主要矿石矿物的微量稀土特征相似:具有富集Rb、Th等大离子亲石元素,而亏损Sr、Nb、Ta等高场强元素的特点,表明其均产于板内构造环境。不同之处为:1矿石矿物的稀土微量元素总量明显小于火山岩围岩;2火山岩围岩呈现出明显的负Eu异常,而矿石矿物则具有强弱不等的正Eu异常。综合分析认为比利亚谷铅锌银矿为火山-次火山热液型矿床,其主要成矿作用与晚侏罗世-早白垩世(140Ma左右)火山活动有着密切联系,区域地质背景上对应于构造体制大转折的晚期,但是成矿期后矿体受到了120Ma大规模岩浆活动的影响,本期岩浆活动是区域岩石圈快速减薄引起,产生了一期深变质作用以及壳幔相互作用有关的深部流体活动,受其强烈改造,引起了矿石矿物微量稀土元素含量的改变。 相似文献
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This paper presents slope stability evaluation and prediction with the approach of a fast robust neural network named the extreme learning machine (ELM). The circular failure mechanism of a slope is formulated based on its material, geometrical and environmental parameters such as the unit weight, the cohesion, the internal friction angle, the slope inclination, slope height and the pore water ratio. The ELM is proposed to evaluate the stability of slopes subjected to potential circular failures by means of prediction of the factor of safety (FS). Substantial slope cases collected worldwide are utilized to illustrate and assess the capability and predictability of the ELM on slope stability analysis. Based on the mean absolute percentage errors and the correlation coefficients between the original and predicted FS values, comparisons are demonstrated between the ELM and the generalized regression neural network (GRNN) as well as the prediction models generated from the genetic algorithms. Moreover, sensitivity analysis of the slope parameters and the ELM model parameters is carried out based on the two utilized evaluation functions. The time expense of the ELM on slope stability analysis is also investigated. The results prove that the ELM is advantageous to the GRNN and the genetic algorithm based models in the analysis of slope stability. Hence, the ELM can be a promising technique for approaching the problems in geotechnical engineering. 相似文献
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Prediction of rock burst classification using the technique of cloud models with attribution weight 总被引：4，自引：2，他引：2
Rock burst is one of the common failures in hard rock mining and civil construction. This study focuses on the prediction of rock burst classification with case instances using cloud models and attribution weight. First, cloud models are introduced briefly related to the rock burst classification problem. Then, the attribution weight method is presented to quantify the contribution of each rock burst indicator for classification. The approach is implemented to predict the classes of rock burst intensity for the 164 rock burst instances collected. The clustering figures are generated by cloud models for each rock burst class. The computed weight values of the indicators show that the stress ratio $ Ts = \sigma_{\theta } /\sigma_{c} $ Ts = σ θ / σ c is the most vulnerable parameter and the elastic strain energy storage index W et and the brittleness factor $ B = \sigma_{c} /\sigma_{t} $ B = σ c / σ t take the second and third place, respectively, contributing to the rock burst classification. Besides, the predictive performance of the strategy introduced in this study is compared with that of some empirical methods, the regression analysis, the neural networks and support vector machines. The results turn out that cloud models perform better than the empirical methods and regression analysis and have superior generalization ability than the neural networks in modelling the rock burst cases. Hence, cloud models are feasible and applicable for prediction of rock burst classification. Finally, different models with varying indicators are investigated to validate the parameter sensitivity results obtained by cloud clustering analysis and regression analysis in context to rock burst classification. 相似文献
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内蒙古是我国非常重要的后备煤炭资源基地,煤层大多聚集在白垩纪断陷盆地中,其中,五间房含煤盆地煤炭资源丰富。通过对该盆地东南部3个钻孔57件煤样的煤岩学和煤化学分析,探讨了煤层的煤质特征、煤相类型及其演化规律。研究结果表明:本区煤层以低—中高灰、高挥发分产率和低—特低硫为特征;具有较高的镜/惰比和结构保存指数;煤相类型主要为潮湿森林沼泽相,自下而上,成煤泥炭沼泽覆水程度总体有所加深,上部泥炭沼泽具有水体逐渐加深的水进型特征,下部泥炭沼泽具有水体逐渐变浅的水退型特征。 相似文献
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成矿区带是具有丰富矿产资源及其潜力的成矿地质单元。全国重要固体矿产重点成矿区带数据集是在以往矿产区划研究工作经验的基础上,结合矿产资源潜力评价成果,建立的一套完整的数据集。本数据集借助23个矿种圈定的成矿靶区、重要矿产地、物化遥异常等信息,基于最新的90个三级成矿区带,划分了26个全国固体矿产勘查重点成矿区带,其中主要包括：主要拐点坐标、面积、典型矿床、重要矿产资源潜力（矿种、累计查明资源量、预测资源量）、成矿远景区（远景区名称、主攻矿种、主攻矿种类型）、新发现（或有重大进展）矿产地等方面数据。该数据集不仅是对已经取得的矿产资源区划和资源潜力评价工作的整理和总结,也为科学地引导国家地质找矿部署工作提供理论基础。 相似文献
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全国矿产资源潜力评价（2006-2013）工作历时8年,是建国以来规模最大的矿情调查工作之一,形成了海量（TB级）的成果数据。如何高效的管理该数据集,实现数据的广泛应用,成为数据共享服务的关键和难点。文章以全国矿产潜力评价成果数据为基础,运用GIS技术,分析了地质大数据存储管理、基于元数据的查询检索、空间数据可视化等关键技术,提出了一种针对海量、多源、异构的地质数据的统一管理思路。通过对成果数据的分析整理,构建元数据库作为存储不同类型数据的索引,完成数据的统一集成管理,同时实现数据的快速查询访问;借助强大成熟的Mapgis k9功能模块和开源的NASA World Wind三维数字地球引擎,进行二次开发,搭建适合于矿产资源潜力评价成果数据信息管理系统平台,为矿产资源潜力评价成果数据推广应用提供信息技术支撑,提高潜力评价数据的信息化服务能力。 相似文献
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