哈尔滨市厘米级精度似大地水准面精化计算与检核

详细介绍了哈尔滨市厘米级精度似大地水准面精化的计算、精度检核与数据的分析方法.     

空间最近目标查找算法分析及其高性能Web实现

讨论了空间最近目标查找的基本算法和相关的空间索引机制,简单地比较了几种算法和索引机制的优缺点.详细地介绍了在Windows IIS和.Net下,建立多级空间格网索引,实现空间最近目标查找的实现方法.     

海底管线磁场计算模型

为提高海底管线探测的效率,从磁偶极子模型出发,通过磁矩的轴向分解及其磁场的矢量合成,并考虑地磁背景场影响,提出了一种计算水下管线磁场的数学模型,通过仿真计算三种不同走向管线的磁场分布并与实测数据比对,证明所建模型是正确的。     

海洋潜标系统的静力分析和姿态计算

本文介绍了400m和4000m海洋潜标系统的静力分析和姿态计算方法，以供有关人员交流。     

海洋对热带气旋响应的研究 Ⅱ.不同海洋热力结构下的情形

利用中二层非线性原始方程海洋模式，研究海洋在自身不同热力结构下对热带气旋的响应。计算结果表明，初始混合层（ML）深度和层结强度对海表温（SST）和ML深度变化起着十分重要的作用。初始ML深度对海流量值影响较大，层结强度则较小。东海陆架区特殊的海洋热力结构，极易造成SST下降。海洋对7002号台风响应的模拟结果与观测资料较一致。     

跳点法在风暴潮数值预报模型中的应用

将跳点法的基本思想应用于求解二维水位抛物型方程，从而构造了一种用于风暴潮数值预报的高效率差分格式。文中给出了这一格式的稳定性证明，并通过对实际风暴潮过程的模拟，证实了这一格式的稳定性和时效性     

边界元法在地下水补给量计算中的应用

本文是边界元法在地下水补给量计算中应用的一次尝试,它成功地把边界元法应用到考虑有垂向补给或排泄的实际水文地质问题中。文中详细地推导了考虑有垂向补给或排泄均质各向同性承压含水层非稳定流边界元法的计算公式,结合长春市八里堡群井开采试验的水位观测资料计算了河流边界的补给量;分析了目前边界元法在水文地质计算中出现的时间积分和面积积分问题,在这两个问题的解决上做了些改进;结合简单水文地质模型给出了边界元法的精确度。     

An intelligent displacement back-analysis method for earth-rockfill dams

An intelligent method for the effective displacement back-analysis of earth-rockfill dams was proposed by combining artificial neural networks and evolutionary calculation. This method employs artificial neural networks, with optimal architecture trained by the evolutionary calculation and Vogl’s algorithm, instead of the time-consuming finite element analysis. In the back analysis, the soil parameters were optimized by performing evolutionary calculations on the tested neural network. The proposed method was verified by applying it to the displacement back-analysis of two projects in China, and the influence of generation number and set size on the simulation ability of neural networks was investigated.     

关于抗浮设防水位及浮力计算问题的分析讨论

参阅了近10年来国内11篇文献和2本专著中有关抗浮问题和地下水渗流问题的论述,对抗浮设防水位的确定和浮力的计算问题提出看法和建议。     

Hierarchical probabilistic regionalization of volcanism for Sengan region,Japan

A 1 km square regular grid system created on the Universal Transverse Mercator zone 54 projected coordinate system is used to work with volcanism related data for Sengan region. The following geologic variables were determined as the most important for identifying volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater pH value, presence of volcanic rocks and presence of hydrothermal alteration. Data available for each of these important geologic variables were used to perform directional variogram modeling and kriging to estimate geologic variable vectors at each of the 23949 centers of the chosen 1 km cell grid system. Cluster analysis was performed on the 23949 complete variable vectors to classify each center of 1 km cell into one of five different statistically homogeneous groups with respect to potential volcanism spanning from lowest possible volcanism to highest possible volcanism with increasing group number. A discriminant analysis incorporating Bayes’ theorem was performed to construct maps showing the probability of group membership for each of the volcanism groups. The said maps showed good comparisons with the recorded locations of volcanism within the Sengan region. No volcanic data were found to exist in the group 1 region. The high probability areas within group 1 have the chance of being the no volcanism region. Entropy of classification is calculated to assess the uncertainty of the allocation process of each 1 km cell center location based on the calculated probabilities. The recorded volcanism data are also plotted on the entropy map to examine the uncertainty level of the estimations at the locations where volcanism exists. The volcanic data cell locations that are in the high volcanism regions (groups 4 and 5) showed relatively low mapping estimation uncertainty. On the other hand, the volcanic data cell locations that are in the low volcanism region (group 2) showed relatively high mapping estimation uncertainty. The volcanic data cell locations that are in the medium volcanism region (group 3) showed relatively moderate mapping estimation uncertainty. Areas of high uncertainty provide locations where additional site characterization resources can be spent most effectively. The new data collected can be added to the existing database to perform future regionalized mapping and reduce the uncertainty level of the existing estimations.