Crossover from Nonlinearity Controlled to Heterogeneity Controlled Mixing in Two-Phase Porous Media Flows

We use high resolution Monte Carlo simulations to study the dispersive mixing in two-phase, immiscible, porous media flow that results from the interaction of the nonlinearities in the flow equations with geologic heterogeneity. Our numerical experiments show that distinct dispersive regimes occur depending on the relative strength of nonlinearity and heterogeneity. In particular, for a given degree of multiscale heterogeneity, controlled by the Hurst exponent which characterizes the underlying stochastic model for the heterogeneity, linear and nonlinear flows are essentially identical in their degree of dispersion, if the heterogeneity is strong enough. As the heterogeneity weakens, the dispersion rates cross over from those of linear heterogeneous flows to those typical of nonlinear homogeneous flows.     

THE ANALYSIS ON THE STATISTICAL CHARACTER OF QUIKSCAT SCATTEROMETER WINDS AND STRONG WIND FREQUENCY USING REMOTE SENSOR DATA FROM QUIKSCAT

1 INTRODUCTION Most of the conventional sea surface wind data are measurements from ships, buoys and islands, with coverage and spatial resolution far below the requirements of research and application. At present, due to limited understanding of physical processes and efficient exploitation of data, numerical prediction models have not been used as they should be, although routine procedures are able to give sea surface wind fields at the intervals of 6 hours. With the development of sp…     

测氡法快速确定油气田边界及深度

本介绍了运用测氡法快速确定大庆××油气田边界，估算其埋藏深度试验，结果表明，该方法具有可行性和实用性。由于所做工作有限，尚属经验性探讨，还必须从理论和实践上进一步地工作，进而充实、完善它。     

Dynamic stochastic estimation of physical variables

A fundamental problem facing the physical sciences today is analysis of natural variations and mapping of spatiotemporal processes. Detailed maps describing the space/time distribution of groundwater contaminants, atmospheric pollutant deposition processes, rainfall intensity variables, external intermittency functions, etc. are tools whose importance in practical applications cannot be overestimated. Such maps are valuable inputs for numerous applications including, for example, solute transport, storm modeling, turbulent-nonturbulent flow characterization, weather prediction, and human exposure to hazardous substances. The approach considered here uses the spatiotemporal random field theory to study natural space/time variations and derive dynamic stochastic estimates of physical variables. The random field model is constructed in a space/time continuum that explicitly involves both spatial and temporal aspects and provides a rigorous representation of spatiotemporal variabilities and uncertainties. This has considerable advantages as regards analytical investigations of natural processes. The model is used to study natural space/time variations of springwater calcium ion data from the Dyle River catchment area, Belgium. This dataset is characterized by a spatially nonhomogeneous and temporally nonstationary variability that is quantified by random field parameters, such as orders of space/time continuity and random field increments. A rich class of covariance models is determined from the properties of the random field increments. The analysis leads to maps of continuity orders and covariances reflecting space/time calcium ion correlations and trends. Calcium ion estimates and the associated statistical errors are calculated at unmeasured locations/instants over the Dyle region using a space/time kriging algorithm. In practice, the interpretation of the results of the dynamic stochastic analysis should take into consideration the scale effects.     

Flexible spectral methods for the generation of random fields with power-law semivariograms

Random field generators serve as a tool to model heterogeneous media for applications in hydrocarbon recovery and groundwater flow. Random fields with a power-law variogram structure, also termed fractional Brownian motion (fBm) fields, are of interest to study scale dependent heterogeneity effects on one-phase and two-phase flow. We show that such fields generated by the spectral method and the Inverse Fast Fourier Transform (IFFT) have an incorrect variogram structure and variance. To illustrate this we derive the prefactor of the fBm spectral density function, which is required to generate the fBm fields. We propose a new method to generate fBm fields that introduces weighting functions into the spectral method. It leads to a flexible and efficient algorithm. The flexibility permits an optimal choice of summation points (that is points in frequency space at which the weighting function is calculated) specific for the autocovariance structure of the field. As an illustration of the method, comparisons between estimated and expected statistics of fields with an exponential variogram and of fBm fields are presented. For power-law semivariograms, the proposed spectral method with a cylindrical distribution of the summation points gives optimal results.     

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.     

Magnetic domain structures and stray fields of individual elongated magnetite grains revealed by magnetic force microscopy (MFM)

In order to clarify the relationship between microscopic and macroscopic magnetic features of a rock, we applied magnetic force microscopy (MFM) as a local probe on a sample with an intense natural remanent magnetisation, a norite from Heskestad, Norway. We studied in detail seven magnetite (Fe₃O₄) exsolution lamellae, five of them were about 30 μm long and a few micrometers wide, two were significantly shorter; all were located in their natural host, a grain of clinopyroxene. By combining MFM images of surface magnetisation with information about shape observed with optical microscopy (OM), the internal domain structure was determined for individual grains. In general, the lamellae were pseudo-single-domain grains with open-flux domain magnetisations parallel to their long axes. The domain sizes were, in cross-section, on the order of a micrometer for the longer lamellae and about 300 nm for the short lamellae. By increasing the separation between the MFM probe and the exposed end of the lamellae, information could be obtained about the decrease in stray fields for the individual grains. Close to the lamellae, stray fields were significant even for grains with multiple domains. The largest fields were found above the largest domains. A separation of approximately one domain width was found to be a characteristic distance, where stray fields from adjacent domains begin to cancel one another.     

云南省红河地区2004年第一场透雨过程分析

利用2004年5月15～21日第一场透雨过程的高空资料,对产生强降雨天气的主要物理量进行分析,总结主要物理量的变化特征,揭示这次强降雨产生发展变化规律,对今后在预报过程中对主要物理量的变化引起重视,具有一定的参考意义。     

长江三峡GPS处理结果和应变背景场

采用BerneseGPSSoftware 4 .2对长江三峡工程诱发地震监测系统GPS监测网络 1998～ 2 0 0 3年的 6期观测数据进行了处理 ,结果表明 ,三峡库区与华南块体的水平相对运动在 0～ 3mm/a(± 0 .1～± 2 .0mm/a) ;蓄水导致的垂直形变区域主要集中在茅坪 香溪 巴东库段 ,近岸点垂直沉降的量级在 10～ 35mm左右 ,最大峰值区域香溪约 35mm(± 8.6mm) ,垂直形变沿远离库中心方向迅速衰减。采用贝塞尔双三次样条函数模型拟合该地区的应变率 ,推算各类水平应变场 ,结果表明 ,蓄水前各种应变背景在 10 -9/a～ 10 -10 /a量级。作为构造稳定地区 ,三峡库区近期因蓄水导致大规模形变 ,从而诱发中强地震的可能性不大