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.     

我国磁法勘探的研究与进展

简要概述了我国磁法勘探50年来的研究与进展，主要包括：地面、航空与海洋磁测工作，磁力仪研制与生产，磁异常处理与转换技术，磁异常解释理论与方法，岩石磁性研究，磁法勘探在基础地质研究、固体矿产勘查、油气勘查和其他领域中的应用．     

克服低纬度化磁极困难的有效方法

低纬度地区（尤其是磁赤道附近）磁场化极是长期以来没有很好解决的问题。这里提出的方法只时常规化极稍加改进，通过时化极因子的频点偏移，避开无界点，使化极结果改善很大，甚至在赤道上也可以化极。它既有很高的精度，又因仍是在频率域内进行，故计算方便、快速，非常适合大面积的资料计算     

马山金（硫）矿床地球物理地球化学找矿模式

根据矿床的地球物理地球化学异常特征，磁异常与金矿体的关系，地球化学异常及其垂向分带规律，归纳出矿床地球物理地球化学找矿模式和找矿标志。     

古埃磁天平法研究磁流体的磁性及稳定性

本文根据古埃磁天平的测量原理和磁流体的超顺磁性质，首次使用古埃磁天平研究了磁流体的磁性，利用磁流体的磁增重随时间的变化，研究磁流体的稳定性，并用实验证明方法的可行性。     

生物兼容水基磁流体的制备和性质

用天冬氨酸和谷氨酸化学吸附在纳米四氧化三铁粒子表面，制备得到强生物亲合性的水基磁流体。这种磁流体在磁性药物载体、磁共振造影剂和磁高热治疗肿瘤等领域有良好的应用前景。用体系的电导和旋光度参数表征了两种氨基酸化学吸附在磁粒子表面的过程，研究了酸度、氧化钠浓度、天冬氨酸和谷氨酸的吸附等因素对磁流体的磁性和稳定性的影响。磁测量表明，该磁流体对空气较敏感，60天后饱和磁化强度衰减17％；若保存在磨口瓶中，60天后饱和磁化强度基本不变，可应用于生物医药中。     

综合物探方法在汤上屯地热勘探中的应用研究

本文通过联剖、电测深、磁法和测温法等综合工作成果,分析、推断出汤上屯热水构造的产状、性质和热水出露条件,圈定出了热水分布范围.