具有空间分布裂缝岩石的随机介质模型

利用随机过程的谱展开理论以及Hudson等人的裂纹介质模型构造一种裂纹数密度是空间平稳随机过程的随机介质模型。这个模型可以将裂纹的微观参数(裂纹数密度)与裂纹介质的宏观性质(弹性常数)联系起来,能灵活、有效地描述实际非均匀裂纹介质。模型算例表明,弹性常数的空问分布特征与裂纹数密度的有差别,而且对不同的弹性常数影响不同。通过改编自相关长度的大小,可以模拟裂缝在两个坐标轴方向上具有不同分布尺度的情况。最后,利用高阶的交错网格有限差分方法,我们模拟了地震波在具有随机分布裂缝岩石中的传播特征。     

The effect of an interface boundary layer on the resonance properties of a buried structure

This paper presents a model for the analysis of the diffraction of plane waves at a cavity in an infinite homogeneous linear elastic medium supported by a segmented lining. An elastic boundary layer is introduced between the cavity lining and the infinite medium. The boundary layer is simulated by ‘elastic boundary conditions’ in which the stress is proportional to the relative displacement of the lining and of the surrounding medium boundary. A closed‐form analytical solution of the problem was obtained using the Fourier–Bessel series, the convergence of which was proven. It was shown that the number of series terms required to obtain a desired level of accuracy can be determined in advance. Using amplitude–frequency response analysis it was shown that the boundary layer produces additional ‘pseudo‐resonance’ frequencies that depend on the layer properties. These frequencies are almost identical to the eigenvalues obtained from the simple analysis of a segmented elastically supported lining. Copyright © 2003 John Wiley & Sons, Ltd.     

地气测量方法研究及应用

通过对不同介质地气的空白、样品的各种对比分析,基本上可以肯定:含有一定浓度的高纯的液体捕集介质在一定程度上对地气样品的捕集是有效的,并具有元素含量本底低、易操作、分析过程简单等优点;不同介质对地气中不同元素的捕获能力有差异,王水对Au、Ag、Cu、Zn等元素的捕获能力要强于硝酸,泡塑对金元素的吸附也有一定的优势。因此,在今后的实际工作中,可根据具体矿床类型,选择合适的介质或2种介质配合使用。     

第二松花江流域水电厂综合中长期水文预报的分析研究

基于对第二松花江流域上游小山、松山、两江水电站中长期水文预报研究成果，论述了综合中长期水文预报的研究思路和定性预报、定量预报的分析研究方法。该研究通过2002年实践检验，具有较高的预报精度。     

Improving Sequential Simulation with a Structured Path Guided by Information Content

Multiple-point simulation is a newly developed geostatistical method that aims at combining the strengths of two mainstream geostatistical methods: object-based and pixel-based methods. It maintains the flexibility of pixel-based algorithms in data conditioning, while enhancing its capability of reproducing realistic geological shapes, which is traditionally reserved to object-based algorithms. However, the current snesim program for multiple-point simulation has difficulty in reproducing large-scale structures, which have a significant impact on the flow response. To address this problem, we propose to simulate along a structured path based on an information content measure. This structured path accounts for not only the information from the data, but also some prior structural information provided by geological knowledge. Various case studies show a better reproduction of large-scale structures. This concept of simulating along a structured path guided by information content can be applied to any sequential simulation algorithms, including traditional variogram-based two-point geostatistical algorithms.     

Optimisation de la quantification des flux de matière en suspension d'un cours d'eau alpin : l'Isère à Grenoble (France)

This study seeks to determine suspended-matter flux of the Isère at Grenoble, one of the most important rivers of the northern French Alps. Since 1994, and especially 1996, systematic samplings of the left bank make it possible to follow in one point the variations of suspended particles and matter (SPM) of this river. But from a single measurement, sometimes not very representative of the whole observable concentrations within the section, it remains still difficult to estimate precisely the SPM flux. The results of this study show in particular the successive appearance of several turbidity gradients, whose occurrence is explained mainly by the hydrological and hydro-sedimentary dynamics of this river. The estimate of these gradients thus makes it possible to extrapolate specific measurements more easily and consequently to daily define the quantities of suspended matter exported by the Isère. Since 1996, the results show a very high flow, specific of the Isère at Grenoble, variable between 124 and more than 460 t km^?2 yr^?1. To cite this article: D. Dumas, C. R. Geoscience 336 (2004).     

Mise au point d'un milieu de culture pour l'étude de l'altération de silicates en présence de Pseudomonas aeruginosa

The influence of microorganisms on mineral alteration is not easy to determine in environmental conditions, because of the difficulty to raise for comparison purposes an identical but abiotic system. Another problem in this context is the choice of reliable tracers to evaluate the alteration rate of materials during in vitro experiments. To face such difficulties, we elaborated a defined medium allowing both the growth of Pseudomonas aeruginosa and a precise measurement of the elements solubilized from the minerals. Thanks to this medium, we were able to quantitatively determine the amounts of major elements solubilized from the materials in the presence of bacterial growth, compared to a sterile system. Moreover, the analysis by ICP-MS of trace elements was possible after a chromatographic treatment, which selectively eliminated 99% of the sodium content of the medium. To cite this article: G. Aouad et al., C. R. Geoscience 337 (2005).     

Conditional Simulation of Random Fields with Bivariate Gamma Isofactorial Distributions

This work focuses on a random function model with gamma marginal and bivariate isofactorial distributions, which has been applied in mining geostatistics for estimating recoverable reserves by disjunctive kriging. The objective is to widen its use to conditional simulation and further its application to the modeling of continuous attributes in geosciences. First, the main properties of the bivariate gamma isofactorial distributions are analyzed, with emphasis in the destructuring of the extreme values, the presence of a proportional effect (higher variability in high-valued areas), and the asymmetry in the spatial correlation of the indicator variables with respect to the median threshold. Then, we provide examples of stationary random functions with such bivariate distributions, for which the shape parameter of the marginal distribution is half an integer. These are defined as the sum of squared independent Gaussian random fields. An iterative algorithm based on the Gibbs sampler is proposed to perform the simulation conditional to a set of existing data. Such ‘multivariate chi-square’ model generalizes the well-known multigaussian model and is more flexible, since it allows defining a shape parameter which controls the asymmetry of the marginal and bivariate distributions.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.