东昆仑东段布青山地区上二叠统格曲组物源分析及其构造意义

对东昆仑东段布青山得力斯坦地区出露的上二叠统格曲组砾岩层进行砾石成分、砾度统计及系统的LA-ICP-MS锆石UPb年龄谱分析。结果表明,格曲组砾岩层砾石成分以石英岩和花岗岩为主,硅质岩和基性岩次之,砂岩和灰岩较少,砾石的分散系数为1.54~2.02,该套砾岩为近源快速堆积的产物。砾岩碎屑锆石U-Pb年龄可分为3组:1早奥陶世—晚志留世年龄组为499~409Ma,峰值年龄为426Ma,对应早古生代末期原特提斯洋向北俯冲碰撞产生的一系列构造岩浆事件;2新元古代年龄组为744~619Ma,峰值年龄为744Ma,对应于全球Rodinia超大陆裂解事件;3古元古代年龄组为2443Ma,对应东昆仑地区古元古代构造岩浆热事件。结合碎屑锆石年龄及沉积学特征综合分析,花岗质砾石来源于北侧东昆仑造山带加里东期岩浆弧,沉积岩砾石则可能来自造山带早期的沉积地层,石英岩及其他变质岩砾石则多来自东昆仑基底变质岩系。综合判别,格曲组为一套沉积于活动大陆边缘环境的滨浅海相磨拉石建造,代表南侧古特提斯洋向北俯冲开始的构造阶段,是初始俯冲的沉积构造响应。     

Contribution of phytoliths to total biogenic silica volumes in the tropical rivers of Malaysia and associated implications for the marine biogeochemical cycle

The contribution of phytoliths to total biogenic silica (BSi) volumes in rivers worldwide, and the associated implications for the biogeochemical cycle, require in-depth study. Based on samples from rivers in Peninsular Malaysia, this project investigated the source and characteristics of BSi found in Asian tropical rivers, as well as the process of reverse weathering taking place in these fluvial systems. Results indicated that BSi samples collected in sediments consisted of phytolith, diatom and sponge spicules. Phytoliths, predominantly of the elongate form, comprised 92.8%–98.3% of BSi in the Pahang River. Diatom BSi in this river consisted mainly of pennatae diatoms, but represented a relatively small proportion of the total BSi volume. However, diatom BSi (predominantly of the Centricae form) was more prevalent in the Pontian and Endau Rivers with shares of 68.8% and 79.3% of the total BSi volumes, respectively, than Pahang River. Carbon contents of the BSi particulates ranged from 1.85% to 10.8% with an average of 4.79%. These values are higher than those recorded in other studies to date, and indicate that BSi plays a major role in controlling permanent carbon burial. This study suggests that phytoliths from terrestrial plants are the primary constituents of BSi in the rivers of Peninsular Malaysia, and therefore represent a significant proportion of the coastal silica budget.     

钻探-物探测井技术在覆盖区填图中的应用研究

钻探作为获取深部地质信息的最直接手段,可查明第四纪地层序列,确定基岩顶面埋深与起伏、验证推测模型、研究地质构造、圈定地质体间的重要接触关系,服务于覆盖区填图。物探测井是一种科学有效的井内探测手段,利用特定的岩性或岩性组合对应着特定的测井参数组合的特点,利用测井参数组合可识别地层的岩性、确定地层的深度与厚度,可进行地层划分对比等。钻探-物探测井技术的综合利用,将提高覆盖区勘探效率,降低勘探成本,为下一步开展的覆盖区填图工作提供技术方法支撑。     

油铀兼探的找矿思路在松辽盆地的应用——以中央拗陷区南部为例

以"油铀兼探"的找矿思路,带动松辽盆地中南部开展砂岩型铀矿调查选区及钻探验证工作.将异常强度250 API和450 API确定为划分潜在铀矿孔、潜在铀矿化孔和正常孔的关键技术指标,对中央拗陷区南部1572个钻孔进行异常筛查工作.排查发现潜在铀矿孔362个,潜在铀矿化孔223个.对191号钻孔和36号钻孔进行钻探验证,发现1个工业孔、1个异常孔.认为区内寻找砂岩型铀矿的有利地段为油田区反转构造形成的隆起区边部四方台组河流相砂体,并总结了中央拗陷区南部6种主要控矿因素.     

Hybrid Gaussian-cubic radial basis functions for scattered data interpolation

Scattered data interpolation schemes using kriging and radial basis functions (RBFs) have the advantage of being meshless and dimensional independent; however, for the datasets having insufficient observations, RBFs have the advantage over geostatistical methods as the latter requires variogram study and statistical expertise. Moreover, RBFs can be used for scattered data interpolation with very good convergence, which makes them desirable for shape function interpolation in meshless methods for numerical solution of partial differential equations. For interpolation of large datasets, however, RBFs in their usual form, lead to solving an ill-conditioned system of equations, for which, a small error in the data can cause a significantly large error in the interpolated solution. In order to reduce this limitation, we propose a hybrid kernel by using the conventional Gaussian and a shape parameter independent cubic kernel. Global particle swarm optimization method has been used to analyze the optimal values of the shape parameter as well as the weight coefficients controlling the Gaussian and the cubic part in the hybridization. Through a series of numerical tests, we demonstrate that such hybridization stabilizes the interpolation scheme by yielding a far superior implementation compared to those obtained by using only the Gaussian or cubic kernels. The proposed kernel maintains the accuracy and stability at small shape parameter as well as relatively large degrees of freedom, which exhibit its potential for scattered data interpolation and intrigues its application in global as well as local meshless methods for numerical solution of PDEs.     

Influence of physico-mechanical properties of Indian dimension stones on cutting rate of diamond wire saw

Cutting performance of diamond wire saw is a key factor influencing mine planning, production scheduling, and equipment selection for dimension stone quarries. It is normally measured in terms of cutting rate. Rock samples collected from various granite and marble quarries in India were tested in laboratory to determine their physico-mechanical properties. Cutting rate of diamond wire saw was measured in the field studies during the actual cutting process in quarries. Using these laboratory determined properties and the cutting rate, a multiple linear regression model has been developed to predict the cutting rate of diamond wire saw. Physico-mechanical properties of rocks determined in laboratory are used as independent variables and cutting rate as predictor variable in the regression model. The study indicates that the cutting rate increases with a decrease in most of the hardness and strength parameters of rock. The final model is tested for its goodness of fit indicating a significant linear relation between cutting rate and physico-mechanical properties, namely tensile strength, slake durability index, and Cerchar hardness index with regression coefficient of 94%. The resulting model can be used suitably for different types of hard to medium hard and soft dimension stones. The generalized model for estimating the cutting rate becomes a handy tool for mining engineers to work out operating efficiency, expenses, planning etc. of the dimension stone block cutting.     

Geochemical Characterization and Controlling Factors of Chemical Composition of Spring Water in a Part of Eastern Himalaya

This paper focuses on the suitability of spring water for drinking and irrigation purposes in a part of eastern Himalaya, south Sikkim. There are many anthropogenic and geogenic factors contributing as a source of major cations and anions in the spring water. The spring water chemistry show a variation in EC, pH, TDS, Temperature, Na⁺, K⁺, Mg²⁺, Ca²⁺, Fe²⁺, Pb, Mn, Cu, HCO₃^-, Cl^-, PO₄^3-, NO₃^-, F^- and SO₄^2-. Mainly two types of water quality indexing has been used, one for suitability of spring water for drinking purposes and the other for irrigation purposes. For drinking purposes, Piper diagram used for determination of water type, water quality index (WQI) for quality monitoring and saturation index for mineral dissolution in water. % Na, RSC (Residual Sodium Carbon) and SAR (sodium absorption ratio) have been used for irrigation suitability. Piper diagram shows that CaHCO₃ type of water was dominant in the study area. The WQI depicted excellent category and SAR, percent sodium and RSC (Residual Sodium Carbon) depict excellent, good and permissible category for irrigation purposes. Principle component analysis (PCA) was used to determine the major influencing factor responsible for the variability in the parameters analysed of spring water.     

福建邵武地区大坪—张厝一带萤石矿地质特征及下一步工作方向

正研究区位于福建省邵武市大坪—张厝一带,区内断裂构造发育,萤石矿化强烈,物化探异常明显,萤石矿床(点)众多。综合区内成矿地质特征及成矿规律,区内萤石矿找矿潜力大,新发现的张厝、新坪萤石矿通过进一步工作,有望达到中大型萤石矿床规模。1 区域地质     

山东淄博金岭杂岩体分布区深部富铁矿资源潜力及找矿方向

深部找矿是目前研究和勘查的热点领域.金岭杂岩体侵入于古生代灰岩中,是燕山运动晚期的产物,与其有关的铁成矿作用是内生铁成矿典型事件之一,形成了金岭富铁矿集区.矿集区目前已发现铁矿体主要分布在-500 m以浅,矿山开采面临资源枯竭的严峻形势,深部找矿亟待突破.区内矿体按赋存介质及空间分布可划分为岩体/地层接触带式、假整合/层间裂隙式、岩体内捕虏体式三种型式.重、磁、电、地综合指示,其深部和外围依然具有优越的矽卡岩型富铁成矿条件,有利找矿深度可延伸至1.3 km,资源潜力大.通过可控源音频大地电磁测深、重磁反演技术对深部和外围成矿地质体的反映,优选出了研究区深部和外围的主要有利找矿区,分别建立了金岭杂岩体两侧矽卡岩型富铁矿地质地球物理综合找矿模型,以期为实现研究区深部和外围找矿发现及相似区域的找矿工作提供理论指导.     

基于小波长短期记忆网络的风电功率超短期概率预测

随着大规模的风电并网,风电所具有的间歇性与随机性对电力系统的稳定性产生了很大的影响,风电功率预测成为当前解决该问题重要的方式之一.本文利用长短期记忆（LSTM）网络良好的时序记忆特性,将小波分解技术与LSTM深度网络结合,提出基于小波长短期记忆网络的风电功率超短期概率预测模型.首先通过小波分解技术将原始时间序列进行平稳化处理,再建立各子序列样本的LSTM网络预测模型,借助最大似然估计法估计预测误差的高斯分布函数,最终实现对未来4 h时刻的风电功率概率区间预测.最后,采用中国东北某风电场数据对所提方法进行算例分析,结果表明,将小波分解与深度学习方法结合可以较好地提高预测的精度,提高概率预测的区间可靠性.