京沈高速公路迁-滦连线龙山滑坡成因机理分析及治理

对龙山滑坡层状滑体的破坏模式进行了分析，采用Sarma法和直线滑面型边坡分析法对各个滑块进行了稳定性分析计算，结果准确。根据不同的工程地质条件，采用不同的工程措施进行治理，效果良好。     

深圳市1 km高分辨率厘米级高精度大地水准面的确定

利用65个精度优于2 cm的GPS水准数据、5 213个实测重力点数据、100 m分辨率的数字地形模型和WDM94地球重力场模型,采用移去-恢复技术计算了深圳市1 km分辨率的大地水准面模型.将该模型大地水准面高与由29个GPS水准得到的大地水准面高进行比较,其差值的标准差为±1.4 cm.     

利用GPS组合观测值建立区域电离层模型研究

介绍了VTEC模型的基本原理，给出了三种利用载波相位观测值改善伪距观测值精度的方法，利用三种组合观测值分别建立VTEC模型，并与利用伪距观测值计算的VTEC模型的精度进行比较。     

无验潮模式下的GPS水下地形测量技术

传统的水下地形测量模式定型于利用GPS测定水底点的平面位置,利用测深仪测定水底点的深度,附之以瞬时潮位资料,获得点位的高程。这种模式在上述条件具备的情况下,可取得完满的结果。但当验潮条件不具备时,该模式将不能获得测点的高程。为了弥补这一缺陷,简化工作流程,提高水下地形测量的精度,本文提出了一种无验潮模式下的水下地形测量思想,该思想不用专门测定潮位,而直接利用GPS的RTK测量技术,辅之以姿态测量和补偿,从而获得高精度的水底点高程。该方法被验证是正确的,希望进一步推广应用。     

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.     

Disjunctive Kriging with Hard and Imprecise Data

This paper presents a methodology for assessing local probability distributions by disjunctive kriging when the available data set contains some imprecise measurements, like noisy or soft information or interval constraints. The basic idea consists in replacing the set of imprecise data by a set of pseudohard data simulated from their posterior distribution; an iterative algorithm based on the Gibbs sampler is proposed to achieve such a simulation step. The whole procedure is repeated many times and the final result is the average of the disjunctive kriging estimates computed from each simulated data set. Being data-independent, the kriging weights need to be calculated only once, which enables fast computing. The simulation procedure requires encoding each datum as a pre-posterior distribution and assuming a Markov property to allow the updating of pre-posterior distributions into posterior ones. Although it suffers some imperfections, disjunctive kriging turns out to be a much more flexible approach than conditional expectation, because of the vast class of models that allows its computation, namely isofactorial models.     

Spectral and Multiscale Signal-to-Noise Thresholding of Spherical Vector Fields

The basic concepts of spectral and multiscale selective reconstruction of (geophysically relevant) vector fields on the sphere from error-affected data is outlined in detail. The reconstruction mechanism is formulated under the assumption that spectral as well as multiscale approximation is well-representable in terms of only a certain number of expansion coefficients at the various resolution levels. It is shown that spectral denoising by means of orthogonal expansions in terms of vector spherical harmonics reflects global a priori information of the noise (e.g., in form of a covariance tensor field), whereas multiscale signal-to-noise thresholding can be performed under locally dependent noise information within a multiresolution analysis in terms of spherical vector wavelets. An application of the multiscale formalism to Earth's magnetic field determination is presented.     

测量平差函数模型的若干讨论

讨论了测量平差函数模型的几种形式———参数模型、非参数模型及半参数模型 ,分别论述了各种模型的优缺点及其适用范围。半参数模型对于客观实际具有极强的解释能力 ,应给予充分的重视和深入的研究。     

月降水量的神经网络混合预报模型研究

以均生函数表征预报量自身周期变化，结合500hPa月平均高度场和月平均海温场预报因子，采用神经网络方法建立了一种新的短期气候预报模型。分别以广西桂北、桂中和桂南6月降水量作为预报对象进行预报试验，结果表明，这种新的预报方法比均生函数回归预报模型及高度场、海温场预报因子的回归预报模型，具有更好的物理基础和预报能力。     

Comparison of the Langrangian Footprint

We compare flux and concentration footprint estimates of athree-dimensional Lagrangian stochastic dispersion modelapplying backward trajectories with the results of ananalytical footprint model by Kormann and Meixner.The comparison is performed for varying stability regimesof the surface layer as well as for different measurementheights. In general, excellent correspondence is found.