山岳型旅游区人文建筑环境后效与调控模型

许多山岳型旅游区内或其流域上游，都建有人文建筑，而且在急剧增多，导致自然环境日趋恶化，其中乱建疗养院和旅馆的影响最为严重。通过地监测并全面分析张家界环境演变趋势，发现住宿施对环境的影响，比其他游乐设施更为明显。张家界国家森林公园生态环境的脆弱因子是金鞭溪水质。金鞭溪水质恶化主要表现为蓝藻，绿藻迅速繁殖，感官质量下降。主要原因是磷污染较重，总磷年均值100%超标。本文提出了基于环境脆弱因子的动态阈值调控模型，通过计算得出：在不超出张家界景区最为脆弱的环境因子-金鞭溪水质标准：总磷≤0.02前提下，金鞭溪上游接待区住宿设施生态阈值的动态系列：春季临界床位数为1186，夏季为3057，冬季为545，秋季为333。目前的建筑规模已超过了金鞭溪上游接待区住宿设施生态阈值。     

红外光谱定量比较分析法及其应用

利用红外光谱定量比较分析的原理，从定向及微区的角度研究晶化固态物质的成分、结构等，有效地解决矿物、岩石、矿床乃至构造研究中的诸多问题。     

海拉尔盆地贝尔凹陷的油气运聚分析

海拉尔盆地贝尔凹陷是一个以白垩系沉积为主的陆相断陷区。采用条件分析法,对贝尔凹陷的油气运聚条件及其成藏的基本模式进行了系统研究,认为,贝尔凹陷在剖面上有3套成藏组合,其中中部成藏组合最佳;在平面上可分为5个运聚系统,其中以位于凹陷西北部的3个系统为最佳。该凹陷不同构造部位的运聚条件相差较大,生烃洼陷深部位的油气运移以砂岩储集层和断裂为主要输导通道,构造高部位的油气运移以断裂为主,而两者之间的油气输导则以不整合面和断裂为主。在油气运聚和成藏模式分析的基础上,认为贝尔凹陷油气运聚的有利条件主要包括生油凹陷有充足的油源、生油区内有继承性发育的圈闭构造以及各运聚要素间存在良好的时空配置关系3个方面。     

静力触探中土层界面效应试验研究

根据成层土模型试验结果,对静力触探中土层面效应进行试验研究和机理分析,取得了成层土界面效应的规律性,为利用静力触探划分土层提供了理论和实践依据。     

Lateral tidal mixing in the Antarctic marginal seas

Tidal mixing plays an important role in the modification of dense water masses around the Antarctic continent. In addition to the vertical (diapycnal) mixing in the near-bottom layers, lateral mixing can also be of relevance in some areas. A numerical tide simulation shows that lateral tidal mixing is not uniformly distributed along the shelf break. In particular, strong mixing occurs all along the Ross Sea and Southern Weddell Sea shelf breaks, while other regions (e.g., the western Weddell Sea) are relatively quiet. The latter regions correspond surprisingly well to areas where indications for cross-shelf exchange of dense water masses have been found. The results suggest that lateral tidal mixing may account for the relatively small contribution of Ross Sea dense water masses to Antarctic Bottom Water.     

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。     

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.