雨滴谱数据在S波段双偏振天气雷达数据质量评估中的应用

结合T-Matrix方法和雨滴谱仪数据反演双偏振参量,设计了一种雨滴谱反演评估方法(雨滴谱法),利用其反演结果可对双偏振天气雷达数据进行评估。将此方法应用于不同类型降水过程的雷达偏振参量数据质量评估,结果表明:满足微雨条件时,雨滴谱法与传统的微雨滴法均能有效监测由雷达系统误差导致的观测偏差,且评估结果十分接近;不满足微雨条件时,雨滴谱法评估的不同类型降水过程的雷达数据质量与满足微雨条件下的评估结果十分接近,客观证明了系统调整后雷达运行良好、数据质量稳定;不同雨强(R)情况下,雨滴谱法的评估结果具有一致性。10 mm·h^-1≤R<20 mm·h^-1时,雷达观测的数据质量最为稳定。该雨滴谱法适用范围广,可动态评估双偏振雷达偏振参量质量,有助于雷达调整和数据校正。     

临涣矿区水化学特征及在矿井水源判别中的意义

通过分析、比较临涣矿区三个地下水子系统的水文地球化学特征得出，这三个子系统在水溶标型发及少、微量元素的含量上无明显差别，表明各子系统间存在水力联系，而环境同位素氘和＾１８Ｏ分析则是解决矿井水来源判别的有效方法。     

GPS在1:20万区域重力测量中的应用

介绍了利用GPS开展1：20万石棉幅区域重力测量的情况，对正确掌握GPS的工作方法作了研究，并总结了应用该项技术的成功经验和遗留问题。     

赣南烟草化学组成及岩土地球化学特征

通过赣州地区14个典型烟草产区的岩石、土壤和烟叶地球化学调查和研究,初步评价研究区烟草产地的土壤营养状况,探讨烟草内在品质与基岩、土壤元素地球化学特征间的关系,为研究区烟草施肥及种植规划提供科学依据。     

莱芜煤矿区矿井水资源综合利用研究

山东省莱芜煤矿区矿坑水排放量大，仅四处国有煤矿排放量即达1700×10     

澜沧江某电站右坝肩复杂岩体质量分类

笔者通过对澜沧江某电站右岸坝肩复杂岩体的成因进行分析,采用岩体质量指数Z并结合水利水电工程围岩分级和RMR分级等岩体分类方法对电站右岸坝肩的复杂岩体进行了综合分类。     

长清井田环境水文地质特征分析与评价

在分析井田环境水文地质特征的基础上，利用浅层地下水的分析测试资料，依据水环境污染综合评价指数方法对浅层地下水环境质量的现状进行了评价。简要阐述了矿区开发对地下水资源可能造成的影响，矿区开发后地下水环境的一些变化趋势及初步预防措施，为将来矿井设计、建设和生产过程中浅层地下水环境的保护和利用提供了参考资料。     

设计变更统计分析为质量改进指出方向

持续提高设计质量,是工程设计企业永恒的目标。设计变更是工程建设过程中的客观存在,是设计的延伸,也是保证工程实体质量的一个重要环节。本文介绍如何利用数理统计方法,对设计变更进行统计和分析,为设计质量改进指出方向。     

基于三点击实法的压实度评估方法

基于三点击实法的基本原理,提出了压实度的评估方法。该方法考虑了击实试样初始含水率与碾压现场含水率的误差,得到不同误差组合下的真实压实度,并由此得到真实压实度的取值范围、真实压实度大于某个临界值或设计指标的概率及近似压实度相比于真实压实度偏大或偏小的概率。该方法可获得压实度更加详细的信息,有利于对施工质量作出准确判断。     

The introduction of neural network system and its applications in rock engineering

The neural network system has been developing very fast recently. It has been widely used in many industries such as automation, nuclear power plant, chemical industry, etc. Neural network systems have a great advantage in dealing with problems in which many factors influence the process and result, and the understanding of this process is poor, and there are experimental data or field data. In rock engineering, many problems are of this nature. In this paper, a brief introduction to neural network systems is given. Problems such as what is a neural network, how it works and what kind of advantages it has are discussed. After this, several applications in rock engineering, made by us, are presented. Case 1 is ore boundary delineation. In this case, the rock are divided into three classes, i.e.: (1) waste rock; (2) semi-ore; and (3) ore for mining purposes. The neural network system built can judge whether it is ore, semi-ore or waste rock along the borehole according its corresponding geophysical logging data, such as gamma-ray, gamma-gamma, neutron and resistivity. Case 2 is aggregate quality prediction. In this case, the quality parameters: (1) impact value; (2) abrasion value I; and (3) abrasion value II are predicted by using a neural network system based on density, point load, content of quarts and content of brittle minerals. Case 3 is rock indentation depth prediction. In this case, the rock indentation depth under indentation load is predicted by the established neural network system based on the indentation load on rock, indenter type and rock mechanical properties, such as uniaxial compressive strength, Young's modulus. Poisson's ratio, critical energy release rate and density. In all these cases, the neural network systems have been applied successfully. The testing results are satisfactory and better than the existing techniques.