二维地震观测系统合理性的论证方法

已知勘查区深度范围前提下,最大炮检X和道间距ΔX,不可以太大也不可以太小,施工前能够计算出它的允许值范围。     

图像平滑算法比较研究及改进策略

图像平滑作为去除图像中含有噪声的图像增强处理技术，是各种与图像有关软件中必不可少的功能模块。文中在分析噪声模型的基础上，对均值平滑、中值平滑、边缘保持平滑等技术从图像处理空间域中的算法原理、实现方法及效率等方面进行了详细比较。针对图像中含有的不同噪声模型，通过多次试验，得出了值得从事图像处理研究者参考的结论。这将有助于提高图像处理软件开发的效率。     

基于预测的边缘检测方法

提出了一种基于预测的、有一定自适应性的边缘检测方法。介绍了其原理和算法实现过程。通过计算两个相邻像素的灰度值的均值、均方差和梯度来预测下一个像素的灰度值，比较预测值和真实值来判断下一个像素是否是边界点。通过与现有同类算法的比较，证明了该算法的优越性。     

基于小波变换系数自适应阈值法在语音去噪中的应用

在软阈值去噪算法基础上，根据小波变换后信号与噪音具有的奇异性，提出无偏风险自适应阈值估计算法，并应用于语音信号的去噪处理，实验结果表明该算法的最小均方误差低于其他的去噪算法。     

基于立体补偿的遥感立体像对压缩方法

提出研究遥感立体像对的压缩问题。主要讨论了左右影像的视差补偿和辐射补偿。针对遥感立体像对视差分布不均以及左右影像存在辐射差的特点,提出了一种基于立体补偿的遥感立体像对压缩算法。该算法以左片为基准图像,采用自适应视差估计计算出右片的视差矢量,结合辐射校正和重叠块视差补偿技术得到平滑的右片的预测图像,以右片减去预测图像得到残差图像,然后采用小波压缩算法对残差图像进行压缩。实验结果表明,该算法能显著提高遥感立体像对的压缩性能。     

水资源复杂适应配置系统的理论与模型

在对水资源配置系统的复杂性及其复杂适应机理分析的基础上，应用复杂适应系统理论的基本原理和方法，构架出了全新的水资源配置系统分析模型，同时对系统的动力机制、主体行为特性和系统状态的评价方法进行了描述，形成了一个较为完整的研究体系，用于分析水资源配置系统的演化规律，并应用这种分析方法对南水北调工程对受水区水资源配置的影响进行了简要的研究。     

二维有限元网格的自适应剖分及程序实现

有限单元法在计算岩土力学领域已得到广泛应用。在有限元的分析计算中，前处理工作即准备计算数据耗时费力。如何简化前处理工作、实现网络自动生成对于推广有限元分析至关重要。作者在综合考虑多种算法原理的基础上，对非规则几何图形、复连通域的二维模型提出了一套新的自适应剖分算法。该算法可以同时生成三角形单元和四边形单元，还能对单元形状进行规则性调整。给出了详细的实施步骤，并编写了相应的应用程序，应用程序同时提供了对单元节点进行优化排序的功能。实际应用表明，能够高效快速地给出良好的剖分网格。     

Generalized Coarse Graining Procedures for Flow in Porous Media

This paper focuses on heterogeneous soil conductivities and on the impact their resolution has on a solution of the piezometric head equation: owing to spatial variations of the conductivity, the flow properties at larger scales differ from those found for experiments performed at smaller scales. The method of coarse graining is proposed in order to upscale the piezometric head equation on arbitrary intermediate scales. At intermediate scales large scale fluctuations of the conductivities are resolved, whereas small scale fluctuations are smoothed by a partialy spatial filtering procedure. The filtering procedure is performed in Fourier space with the aid of a low-frequency cut-off function. We derive the partially upscaled head equations. In these equations, the impact of the small scale variability is modeled by scale dependent effective conductivities which are determined by additional differential equations. Explicit results for the scale dependent conductivity values are presented in lowest order perturbation theory. The perturbation theory contributions are summed up with using a renormalisation group analysis yielding explicit results for the effective conductivity in isotropic media. Therefore, the results are also valid for highly heterogeneous media. The results are compared with numerical simulations performed by Dykaar and Kitanidis (1992). The method of coarse graining combined by a renormalisation group analysis offers a tool to derive exact and explicit expressions for resolution dependent conductivity values. It is, e.g., relevant for the interpretation of measurement data on different scales and for reduction of grid-block resolution in numerical modeling. This revised version was published online in July 2006 with corrections to the Cover Date.     

Adaptive Multiscale Permeability Estimation

With multiscale permeability estimation one does not select parameterization prior to the estimation. Instead, one performs a hierarchical search for the right parameterization while solving a sequence of estimation problems with an increasing parameterization dimension. In some previous works on the subject, the same refinement is applied all over the porous medium. This may lead to over-parameterization, and subsequently, to unrealistic permeability estimates and excessive computational work. With adaptive multiscale permeability estimation, the new parameterization at an arbitrary stage in the estimation sequence is such that new degrees of freedom are not necessarily introduced all over the porous medium. The aim is to introduce new degrees of freedom only where it is warranted by the data. In this paper, we introduce a novel adaptive multiscale estimation. The approach is used to estimate absolute permeability from two-phase pressure data in several numerical examples.