西藏西部班公湖铁铜多金属矿带的成矿特征与远景评估

班公湖铁铜矿带位于班公湖－怒江构造-岩浆-成矿带西段,带内广泛出露的二叠纪海相碳酸盐岩是对成矿极为有利的地层,沿带侵入于二叠纪至三叠纪地层中的晚侏罗世—早白垩世花岗闪长岩、花岗岩则是成矿有利的岩浆岩。横贯区内的班－怒结合带（北西西向）及与之伴生的次级北东向、北西向、南北向断裂,以及接触带构造、层间破碎带构造为成矿物质迁移富集提供了良好的容储空间。航磁异常显示为一级找矿远景区,成矿条件有利,铁铜矿资源潜力较大。在分析1∶25万喀纳幅、日土县幅、羌多幅区调工作成果的基础上,结合近年来的地质勘查新成果,初步分析了班公湖地区的成矿地质特征,并在班公湖矿带内划分出6个成矿远景区。     

广西河池市坡平锑锌多金属矿床地质特征及找矿远景分析

广西河池市坡平锑锌多金属矿床位于南华活动带北侧.桂北台隆(江南古陆)西南缘.通过野外地质找矿工作,现已圈定7个矿(化)体.矿体呈脉状,走向近SN向.沿走向或倾向有分支复合,尖灭再现或尖灭侧现等现象:赋矿岩性主要为白云质泥岩、绢云母泥岩、泥灰岩.该矿在箭猪坡锑多金属矿床南东侧,结合区域矿床对比,认为矿床属中低温热液裂隙充填型矿床.文章在普查成果基础上,通过成矿地质背景、矿区地质概况和矿床地质特征的阐述,着重对矿床成因、控矿因素、矿化富集规律及找矿标志进行分析,认为该区具有较好的锑锌成矿地质条件和找矿前景,是寻找大中型锑锌矿的有利地区.     

宁夏灵武面子山山前区地下水中氟的富集及分布规律探讨

研究区东部属低山丘陵区及山前侵蚀堆积阶地,西部为黄河冲、洪积平原区,地下水资源丰富．是灵武矿区的主要水源地,但由于地下水中氟离子含量较高,给地下水的利用带来了影响。通过对该区水文地质条件和地下水中氟的水文地球化学特征的研究,认为研究区地下水中氟的含量与水化学类型关系不明显,与PH值及钠／钙值呈正相关关系。初步探讨了影响地下水中氟含量的富集因素及分布规律,研究发现：区内潜水含水层氟含量变化大,常发生突变;承压水含水层则多呈渐变形式;地下水中氟含量的高低主要与补给源中氟含量的高低有关．     

反滤回灌井的结构设计理论和方法

反滤回灌井是地下水库中一种有效的回灌设施,它由回灌井和回灌池组成,回灌池位于井口、具有反滤功能。介绍了反滤回灌井单井回灌量的计算方法,提出反滤回灌井结构设计的原则和方法。回灌池反滤结构应有两层,上部反滤层应能过滤回灌水中的杂质,选择较细的砂粒;下部反滤层应能防止上层反滤料通过井盖的孔口落入井内,选择较粗的砾类。从过流能力和水流流态的角度。对回灌池的外型进行了优化,探讨了回灌池池口和池底横截面面积的比例,提出应选择抛物线型竖向剖面,同时介绍了回灌池防淤和抗冲设计的方法。     

油气层录井的精细解释方法 ——以吉林探区为例

采用“四大参数法” (峰值、峰基比、甲烷和重烃) 、全烃曲线形态法和全烃储层灌满系数法, 解决了吉林探区油气层气测低异常和组分发育不全等问题。进一步以地化录井为核心, 结合核磁孔隙度和色谱油性, 建立了地化含油饱和度储层评价体系, 解决了油气层荧光、气测显示异常和电测低电阻的油层评价难题。     

水泥粉煤灰碎石桩工程实例

通过某高层住宅地基处理的设计实例,分析了水泥粉煤灰碎石桩（CFG桩）的特性,指出了水泥粉煤灰碎石桩使用时须注意的要点。     

东营凹陷沙四段碳酸盐岩稀土元素地球化学特征

东营凹陷古近系沙河街组碳酸盐岩的成因一直存在湖相与海相之争。沙四段碳酸盐岩呈厚薄不等的夹层赋于陆相碎屑岩中。本文对研究区代表性碳酸盐岩样品稀土元素地球化学特征的分析表明：东营凹陷古近系沙四段碳酸盐岩的∑LREE〉∑MREE〉∑HREE,MREE相对于HREE富集;不具备Ce／Ce＊负异常特征;Y／Ho值为25．5～34．1,La／La＊值为0．99～1．21;Y／Ho—La／La＊呈负相关关系。所有这些特征均与湖相成因碳酸盐岩一致,沙四段碳酸盐岩的形成应为湖相成因。     

土壤样品中稀土元素的高分辨电感耦合等离子质谱分析及其不确定度的评定探讨

在敞开体系中,用HF、HNO_3和HClO_4溶解电感耦合等离子体质谱法同时测定土壤样品中的15种稀土元素.在高分辨等离子体质谱仪(Element2)上建立了土壤样品中稀土元素含量的ICP-MS分析方法,经土壤国家一级标准物质分析验证,结果与标准值相符,测定的15种稀土元素的相对标准偏差均小于10%,加标回收率为96.5%~114.7%.实验表明,该方法不但操作简便快速,而且具有灵敏度高、检出限低,重现性好等优点,举例说明了测量不确定度的评定程序.     

A Review of: “Applications of Spatial Data Structures: Computer Graphics,Image Processing,and GIS”. By H. SAMET. (Addison-Wesley Publishing Company,Inc, 1989.) [Pp. xv + 507; figures,tables, appendix,references, name and credit index,subject index.] Price US $49.95. ISBN 020150300 X (hardback).

The introduction of automated generalisation procedures in map production systems requires that generalisation systems are capable of processing large amounts of map data in acceptable time and that cartographic quality is similar to traditional map products. With respect to these requirements, we examine two complementary approaches that should improve generalisation systems currently in use by national topographic mapping agencies. Our focus is particularly on self‐evaluating systems, taking as an example those systems that build on the multi‐agent paradigm. The first approach aims to improve the cartographic quality by utilising cartographic expert knowledge relating to spatial context. More specifically, we introduce expert rules for the selection of generalisation operations based on a classification of buildings into five urban structure types, including inner city, urban, suburban, rural, and industrial and commercial areas. The second approach aims to utilise machine learning techniques to extract heuristics that allow us to reduce the search space and hence the time in which a good cartographical solution is reached. Both approaches are tested individually and in combination for the generalisation of buildings from map scale 1:5000 to the target map scale of 1:25 000. Our experiments show improvements in terms of efficiency and effectiveness. We provide evidence that both approaches complement each other and that a combination of expert and machine learnt rules give better results than the individual approaches. Both approaches are sufficiently general to be applicable to other forms of self‐evaluating, constraint‐based systems than multi‐agent systems, and to other feature classes than buildings. Problems have been identified resulting from difficulties to formalise cartographic quality by means of constraints for the control of the generalisation process.