1605年琼山大地震深部构造和应力状态研究

利用1605年琼山地震区已有的浅部地质、物探和钻井等资料,采用空间域三维重力正反演计算,消除了地壳浅部影响,依次得到由浅到深的各剩余深部重力异常值。然后由基底剩余重力异常值反演得到莫霍界面深度及起伏形态。分析上述结果可知:1605年的琼山大地震是一个以垂直向差异运动为主的地震,位于琼北莫霍面隆起区东北部、隆起向外凸出部位的陡坡上;北东东向的马袅-铺前断裂(中东段)呈反扭(左旋)状态,而北西向的铺前-清澜断裂(北段)呈顺扭(右旋)状态;云龙地区地下地幔物质上涌,造成云龙地块的上隆;如果1605年地震区出现NE30°左右的主压应力场作用和云龙地块异常上隆时,应考虑地震的复发问题。另外,分析各剩余深部重力异常等值线分布,还可以判定地质学给出的各断裂构造展布和延伸情况。     

2003年中国及邻区地磁场模型的计算与分析

根据2003年中国地区的135个测点和35个台站的地磁数据, 建立了2003年中国及邻区地磁场泰勒多项式模型和球冠谐模型. 在模型计算过程中, 细致地分析了模型的截断阶数和边界约束对模型计算的影响. 结果表明, 所使用的地磁观测资料是准确可靠的, 模型截断阶数的选取是合理的. 本文所建立的中国区域地磁场球冠谐模型与泰勒多项式模型具有良好的一致性.      

Modelling tidally induced sediment-transport paths over the northwest European shelf: the influence of sea-level reduction

A three-dimensional model covering the northwest European Shelf and part of the adjacent Atlantic Ocean is used to examine the influence of water depth change upon the distribution of maximum tidal bed stress. The direction of bed stress is an indicator of sediment movement as bed load and various regions of convergence and divergence in good agreement with observations are identified. Calculations are performed with water depths reduced by 35 m, corresponding to 10 000 years before present (B.P.). Initially, the model is forced by only the M₂ tide, although subsequently five constituents, namely M₂, S₂, N₂, K₁ and O_1, are used for tidal forcing. Although the distribution of extreme bed stresses computed with only M₂ tidal forcing is comparable to that computed with five tides, the additional tidal constituents modify the magnitude of the bed stress. In particular the diurnal tides show regions of local enhanced current amplitude in the shelf-edge region with corresponding changes in bed stress. When water depths are reduced such that the North Sea and English Channel are separated, then there is a significant change in the tidal distribution in the shallow Southern Bight which influences bed-stress distributions and hence bed-load sediment transport in the area. Besides changes in shallow regions, the distribution of tides at the shelf edge is affected. A discussion of the limitations of the present coarse-grid model in shelf-edge regions and how it can be used to provide boundary conditions for limited-area three-dimensional models that can include stratification is presented. Also the importance of stratification for sediment movement at the shelf edge is briefly discussed.Responsible Editor: Phil Dyke     

工程场地分类中等效剪切波速计算深度问题的讨论

根据中国华北、华东、华南、东北和西北等地918个实测钻孔资料的计算统计,探讨了工程场地分类中等效剪切波速计算深度取值20m和30m的实际差别,并对中国、美国、欧洲现有规范利用等效剪切波速进行场地类别划分的方法特点和具体指标进行了对比讨论。结果表明:1)计算深度由20m增加至30m时,钻孔等效剪切波速值的增大范围约为15～50m/s,平均增加值为25m/s;2)与欧美规范相比,中国现行规范(GB50011-2001)在划分场地类别时要求同时考虑20m计算深度的等效剪切波速值和覆盖层厚度,而在许多实际工程中,因较准确的覆盖层厚度不易获取而难以具体进行场地分类。因此,有必要借鉴欧美规范,通过增大等效剪切波速的计算深度至30m来强化该指标在场地类别判定中的作用     

地表水水环境容量计算方法回顾与展望

为厘清中国地表水水环境容量计算方法演变历史,探讨计算方法发展趋势,在系统调研大量水环境容量研究文献基础上,详细梳理水环境容量从概念引入到研究至今的过程,归纳出中国地表水水环境容量研究过程中产生的五大类计算方法:公式法、模型试错法、系统最优化法（线性规划法和随机规划法）、概率稀释模型法和未确知数学法。解析了各类方法的基本思路、产生过程及应用进展,评述了各类方法的优缺点及适用范围。通过与国外水环境容量计算方法的比较,基于水环境系统复杂性及中国水资源管理特点与应用需求,认为中国应强化对概率稀释模型法、未确知数学法及随机规划法等3种方法的研究和改进。     

自动气象站湿球温度快速计算方法

湿球温度是采暖通风、电厂冷却塔等工程设计中的重要气象参数。随着自动气象站在台站的广泛使用,湿球温度的直接观测资料逐渐停止,这给工程设计中湿球温度的气象参数分析和气象资料的应用造成了困难。对于自动气象站湿球温度的计算,现提出基于地面气象观测的湿度参量公式和牛顿迭代法基本思想,采用简化一般的迭代公式,并利用湿球温度的经验公式计算初始值,采用EXECL电子表格完成湿球温度的迭代计算。结果表明,该方法计算湿球温度,精度较高,计算量较小,计算过程简单可控,可广泛应用于自动气象站的湿球温度计算。     

日平均计算方法对气温统计值的影响

为了弄清不同日平均计算方法对气温统计值的影响,利用陕西6个基准站的定时气温资料分别进行24次观测与4次观测,以及4次与3次观测值计算所得日平均气温计算值的差异分析,并对1961-2010年的3次、4次、24次计算的年平均气温序列进行均一性的惩罚最大F检验(PMFT).结果表明:24次气温定时值计算的日平均气温均值高于4次值,平均差值为0.13℃,标准差为0.39℃,两者差值在秋季较大.4次比3次日平均气温值平均偏低0.14℃,标准差为0.85℃,一年中,夏季差值最大.不同次数的日平均气温计算方法可引起月、年平均气温值0.2℃甚至以上的升降.24次气温值的使用可以使单站的气温增暖速率提高0.03～0.04℃/10a.但日平均气温计算方法的改变不会造成气温序列的非均一.     

民用炸药库防雷地网分析

以一种较为简单可行的计算方法,对炸药库在复杂土壤环境下的防雷地网进行设计计算,并通过对新建炸药库接地工程前期设计和施工资料对比验收实测值,验证该设计计算方法的可行性。     

NOAA／AMSU温度反演在强热带风暴“碧利斯”研究中的应用

基于中国海洋大学卫星地面站提供的数据,通过NOAA／AMSU温度反演资料对2006年第4号强热带风暴“碧利斯”进行路径估算、结构再现、强度分析。结果表明,采用250hPa温度场信息进行强热带风暴中心定位,可以较准确地估算台风路径;在利用卫星数据反演的AMSU—B150GHz通道亮温图像上叠加风场信息,可以清晰地反映台风的螺旋结构,同时利用AMSU数据反演得到的亮温距平剖面图能够很好地反映台风强度。     

Application of Bayesian model and discriminant function analysis to the estimation of sediment source contributions

Bayesian and discriminant function analysis (DFA) models have recently been used as tools to estimate sediment source contributions. Unlike existing multivariate mixing models, the accuracy of these two models remains unclear. In the current study, four well-distinguished source samples were used to create artificial mixtures to test the performance of Bayesian and DFA models. These models were tested against the Walling-Collins model, a credible model used in estimation of sediment source contributions estimation, as a reference. The artificial mixtures were divided into five groups, with each group consisting of five samples with known source percentages. The relative contributions of the sediment sources to the individual and grouped samples were calculated using each of the models. The mean absolute error (MAE) and standard error of (SE) MAE were used to test the accuracy of each model and the robustness of the optimized solutions. For the individual sediment samples, the calculated source contributions obtained with the Bayesian (MAE = 7.4%, SE = 0.6%) and Walling-Collins (MAE = 7.5%, SE = 0.7%) models produced results which were closest to the actual percentages of the source contributions to the sediment mixtures. The DFA model produced the worst estimates (MAE = 18.4%, SE = 1.4%). For the grouped sediment samples, the Walling-Collins model (MAE = 5.4%) was the best predictor, closely followed by the Bayesian model (MAE = 5.9%). The results obtained with the DFA model were similar to the values for the individual sediment samples, with the accuracy of the source contribution value being the poorest obtained with any of the models (MAE = 18.5%). An increase in sample size improved the accuracies of the Walling-Collins and Bayesian models, but the DFA model produced similarly inaccurate results for both the individual and grouped sediment samples. Generally, the accuracy of the Walling-Collins and Bayesian models was similar (p > 0.01), while there were significant differences (p < 0.01) between the DFA model and the other models. This study demonstrated that the Bayesian model could provide a credible estimation of sediment source contributions and has great practical potential, while the accuracy of the DFA model still requires considerable improvement.