关于二轴晶矿物光性正负判别式的讨论

作者同意刘智星的论断:旧的二轴晶矿物光性正负判别式是错误的。本文也对刘智星的推论作了更为简明直观的解释,同时提出一个新的测定二轴晶矿物光性正负的判别式:Nm-N_(45)。应用此判别式检查了近470个二轴晶矿物的光性,发现某些文献中所记载或所引用的资料(折射率或光性符号)有错。     

太平洋海温异常对大理雨季开始期的影响

采用大理州雨季开始期资料、1961年6月~2008年5月太平洋5°×5°共286个格点的逐月平均海温资料及NCEP/NCAR的SSTA资料,通过相关分析发现,太平洋海温与大理雨季开始期的关系密切,春季和上年秋季太平洋海温对大理雨季开始的影响较大。雨季开始前若发生厄尔尼诺事件,大理雨季开始期偏晚,反之,若发生拉尼娜事件,大理雨季开始期偏早。N ino3区的海温异常对大理雨季开始期的影响相当明显,当5月N ino3区的海温异常偏高时,大理雨季开始期偏晚;反之,当5月N ino3区的海温异常偏低时,大理雨季开始期偏早,N ino3区海温异常可以作为大理雨季开始期预测的一个强信号因子。     

浅谈水工工程投标中的预算和报价技巧

施工企业在投标时应在充分理解招标文件、施工图及做好投标前的现场考察的基础上,根据企业自身的管理水平及市场行情做好投标书。介绍了预算价编制、施工组织设计编制、定额及费用选取、报价分析及最终报价中的一些技巧性问题。     

西安市浅层地温场垂向分布特征及其影响因素分析

根据西安市内布设的12口长观孔监测的一年内的月平均地温,绘制不同时期地温随深度变化曲线,系统分析西安市浅层地温场垂向上的分布特征及其影响因素。分析得出：西安市垂向上的分布特征有两种类型：渐变升温型和升温降温交替型。影响其分布的主要因素是地质构造、地下水活动以及岩性。在开发利用过程中要针对浅层地温场不同的变化类型因地制宜地进行调查评价工作,合理开发西安市浅层地温能。     

塔里木盆地北部油气运移二维二相数值模拟分析

油气运移和聚集过程实际上是油（气）水饱和度知疏导层的变化过程，盆地中油（气）二相流动问题的研究就是对这一过程的定量描述，本项研究是在前人工作基础上，考察压实作用造成骨架变形来推导新的二维二相流动方程，它容描述异常压力演化与二相流动于一式，在模拟方法上采用有限分析的数值方法，这应用于新疆塔里木盆地北部地区，展示了该区异常压力、含水饱和度弥散状扩散效应以及流速场在地质历史时期动态模拟演化特征；指出了该     

Temporal vP/vS variation characteristics in different zones of China’s Capital Circle area before and after Wen’an earthquake

On July 4, 2006, an earthquake of MS5.1 took place in Wen’an, Hebei Province, just at the south center of China’s Capital Circle area digital seismograph network. It is the strongest event recorded ever since the network went into operation in 2002. We processed the vast amounts of phase data yielded by the 107 digital seismic stations between 2002~2007 using Wadati method. In order to improve the precision and stability of shear and compressional wave velocities (vP/vS) calculation, we impose a number of restrictions on the computation environment and condition, e.g., the earthquakes are densely concentrated, selected stations are limited in range, the number of stations in- volved in the computation is larger than 5 and linear fitting features high precision and small error. Under these restrictions, the study shows that vP/vS in and around Wen’an and Tangshan underwent a normal-low-normal proc- ess one year before Wen’an earthquake, vP/vS became obviously low and the low ratio lasted for about one year, meanwhile, little variation of vP/vS was seen in Xingtai, northwest of Beijing, southwest of Beijing, Beijing-Tianjin and Beijing; after the quake, the vP/vS returned normal in Wen’an and Tangshan. Error and stability analysis of the calculated result for vP/vS shows it is convincible that anomaly appeared in and around Wen’an and Tangshan be- fore Wen’an earthquake.     

深海沉积物分类与命名的参数指标和主成分分析

对于南海中部(118个表层沉积物样,水深82~4 420 m)、东部(106个表层沉积物样,水深700~4 508 m)海域的表层沉积物的粒度资料按小于200 m,200~2 000 m,大于2 000 m水深段对水深、平均粒径、黏土含量进行统计分析,结果表明从陆架到陆坡再到深海,平均粒径和黏土含量随水深增加呈非常有规律的变化;把大于2 000 m水深区域再细分为大于2 500 m,大于3 000 m,大于3 500 m,结果表明平均粒径和黏土含量随水深增加几乎无变化,在南海中部水深大于2 000 m海域平均粒径为3.39~3.54μm,黏土平均含量为54.91%~55.47%;在南海东部水深大于2 000 m海域平均粒径为3.25~3.37μm,黏土平均含量为53.91%~54.56%。研究表明2 000 m水深具有划分深海沉积物的指示意义。南海中部水深大于2 000 m海域黏土平均含量为55.19%,平均粒径为3.39μm;在南海东部水深大于2 000 m海域黏土平均含量为53.91%,平均粒径为3.37μm;在南海中部、东部水深大于2 000 m海域平均粒径均小于4μm,黏土平均含量均大于50%,表明深海沉积物粒度特征是平均粒径小于4μm和黏土平均含量大于50%。黏土含量是非生物组分的代表和划分深海沉积物类型的一个独立参数,钙质生物和硅质生物组分是另外两个独立参数。南海东部海域表层沉积物中55种元素总含量为47.50%,硅、铝、钛、钠、钾、磷、钙、镁、铁、锰十种主元素含量为47.03%,其他45种元素含量为0.47%,虽然沉积物来源复杂、成因不同,但沉积物化学主成分并不复杂,主要由前10种主元素和氧元素组成。沉积物主元素铝、钙、硅分别富集于黏土、钙质沉积、硅质沉积中。通过建立沉积物生源组分与碳酸钙、三氧化二铝、二氧化硅的量化关系,可把碳酸钙、生物二氧化硅作为钙质生物和硅质生物的两个替代参数。     

Stepwise Conditional Transformation for Simulation of Multiple Variables

Most geostatistical studies consider multiple-related variables. These relationships often show complex features such as nonlinearity, heteroscedasticity, and mineralogical or other constraints. These features are not handled by the well-established Gaussian simulation techniques. Earth science variables are rarely Gaussian. Transformation or anamorphosis techniques make each variable univariate Gaussian, but do not enforce bivariate or higher order Gaussianity. The stepwise conditional transformation technique is proposed to transform multiple variables to be univariate Gaussian and multivariate Gaussian with no cross correlation. This makes it remarkably easy to simulate multiple variables with arbitrarily complex relationships: (1) transform the multiple variables, (2) perform independent Gaussian simulation on the transformed variables, and (3) back transform to the original variables. The back transformation enforces reproduction of the original complex features. The methodology and underlying assumptions are explained. Several petroleum and mining examples are used to show features of the transformation and implementation details.