GPS载波相位三差观测方程的改化

本文根据相关测量平差原理推导出改化ＧＰＳ载波相位三差观测方程的算法，使改化后的协因数呈分块对角体。这种改化算法不但顾及观测值的相关性，而且节省大量的存储单元。使采用严密平差法平差三差观测值成为一种最简单的数据处理方法之一。     

重力场中的完整坐标与非完整坐标

阐明重力场中完整坐标系、非完整坐标系及其之间的转换关系以及完整坐标系的存在对大地测量的意义．指出水准测量中的理论闭合差是由于它所参照的局部笛卡尔坐标系是一个非完整坐标系导致的，并给出了计算理论闭合差的严密公式．     

区域经济联系定量分析初探：以上海与苏锡常地区经济联系为例

针对目前区域联合日益广泛和经济联系量化研究不足的现状，在联系强度等方面提出一些新概念；阐述了经济联系量化指标的选取，建立了经济联系定量分析模型。     

极化分析研究及其在波场分离中的应用

不同的波在空间的质点运动轨迹和极化方向是不同的，利用三分量或二分量记录，在特定的以ｔ０为中心，长为Ｎ△ｔ的滑动时窗中计算出描述波的质点运动轨迹和极化方向的特征参数。利用这些参数可以设计出合适的极化滤波函数，保留那些满足特定要求的质点运动，而衰减那些不满足设计要求的质点运动，利用某些算子可以把弹性波分解成Ｐ波和ＳＶ波，而且衰减面波；利用另外一些算子可以把Ｐ波和ＳＶ波衰减而使面波增强。这些算子甚至可以在嗓音和信号具有相同频率的情况下，成功地提高信嗓比。     

Multifractal modeling and spatial statistics

In general, the multifractal model provides more information about measurements on spatial objects than a fractal model. It also results in mathematical equations for the covariance function and semivariogram in spatial statistics which are determined primarily by the second-order mass exponent. However, these equations can be approximated by power-law relations which are comparable directly to equations based on fractal modeling. The multifractal approach is used to describe the underlying spatial structure of De Wijs 's example of zinc values from a sphalerite-bearing quartz vein near Pulacayo, Bolivia. It is shown that these data are multifractal instead of fractal, and that the second-order mass exponent (=0.979±0.011 for the example) can be used in spatial statistical analysis.     

二维TIM中的传播矩阵与面波相速度

本文利用横向各向同性介质（ＴＩＭ）中的传播矩阵和面波形成条件，导出了面波波慢度方程。对瑞利波给出了各向同性及横向各向同性弹性介质中其相速度的解析解；对于勒夫波，给出了横向各向同性介质中的波慢度方程。     

井间电阻率层析成象的平滑度约束反演

井间电阻率层析成象是一种探测地下浅部精细结构的物探方法，它主要用于解决工程地质问题。本文采用２．５－Ｄ有限元法，针对井间电阻率层析成象中的ＡＭ观测系统进行了反演成象计算。文中首先计算了灵敏度矩阵，然后给出了一种电阻率层析成象反演算法—平滑度约束反演，理论模型的计算和实际资料的处理，都证明了该算法的有效性     

Influence of gas production induced volumetric strain on permeability of coal

Summary The gas permeability of a coalbed, unlike that of conventional gas reservoirs, is influenced during gas production not only by the simultaneous changes in effective stress and gas slippage, but also by the volumetric strain of the coal matrix that is associated with gas desorption. A technique for conducting laboratory experiments to separate these effects and estimate their individual contribution is presented in this paper. The results show that for a pressure decrease from 6.2 to 0.7 MPa, the total permeability of the coal sample increased by more than 17 times. A factor of 12 is due to the volumetric strain effect, and a factor of 5 due to the gas slippage effect. Changes in permeability and porosity with gas depletion were also estimated using the measured volumetric strain and the matchstick reservoir model geometry for flow of gas in coalbeds. The resulting variations were compared with results obtained experimentally. Furthermore, the results show that when gas pressure is above 1.7 MPa, the effect of volumetric strain due to matrix shrinkage dominates. As gas pressure falls below 1.7 MPa, both the gas slippage and matrix shrinkage effects play important roles in influencing the permeability. Finally, the change in permeability associated with matrix shrinkage was found to be linearly proportional to the volumetric strain. Since volumetric strain is linearly proportional to the amount of gas desorbed, the change in permeability is a linear function of the amount of desorbing gas.     

Simulation of normal distributed smooth fields by Karhunen-Loéve expansion in combination with kriging

Simulation of multigaussian stochastic fields can be made after a Karhunen-Loéve expansion of a given covariance function. This method is also called simulation by Empirical Orthogonal Functions. The simulations are made by drawing stochastic coefficients from a random generator. These numbers are multiplied with eigenfunctions and eigenvalues derived from the predefined covariance model. The number of eigenfunctions necessary to reproduce the stochastic process within a predefined variance error, turns out to be a cardinal question. Some ordinary analytical covariance functions are used to evaluate how quickly the series of eigenfunctions can be truncated. This analysis demonstrates extremely quick convergence to 99.5% of total variance for the 2nd order exponential (‘gaussian’) covariance function, while the opposite is true for the 1st order exponential covariance function. Due to these convergence characteristics, the Karhunen-Loéve method is most suitable for simulating smooth fields with ‘gaussian’ shaped covariance functions. Practical applications of Karhunen-Loéve simulations can be improved by spatial interpolation of the eigenfunctions. In this paper, we suggest interpolation by kriging and limits for reproduction of the predefined covariance functions are evaluated.     

Simulation of normal distributed smooth fields by Karhunen-Loéve expansion in combination with kriging

Simulation of multigaussian stochastic fields can be made after a Karhunen-Loéve expansion of a given covariance function. This method is also called simulation by Empirical Orthogonal Functions. The simulations are made by drawing stochastic coefficients from a random generator. These numbers are multiplied with eigenfunctions and eigenvalues derived from the predefined covariance model. The number of eigenfunctions necessary to reproduce the stochastic process within a predefined variance error, turns out to be a cardinal question. Some ordinary analytical covariance functions are used to evaluate how quickly the series of eigenfunctions can be truncated. This analysis demonstrates extremely quick convergence to 99.5% of total variance for the 2nd order exponential (‘gaussian’) covariance function, while the opposite is true for the 1st order exponential covariance function. Due to these convergence characteristics, the Karhunen-Loéve method is most suitable for simulating smooth fields with ‘gaussian’ shaped covariance functions. Practical applications of Karhunen-Loéve simulations can be improved by spatial interpolation of the eigenfunctions. In this paper, we suggest interpolation by kriging and limits for reproduction of the predefined covariance functions are evaluated.