关于角平分线法建立线状缓冲区算法的改进

分析了用对角平分线法建立缓冲区的算法,提出了一种新的求角平分线和缓冲点的方法,实践证明其在算法实现方面是可行的,而且效果比较理想。     

航空影像分割的最小二乘支持向量机方法

将最小支持向量机LS-SVM用于航空影像的分割,讨论了不同核函数对分割结果的影响和稀疏化处理对决策函数的影响。试验表明了LS-SVM方法用于航空影像分割的可行性。     

内蒙古大青山地区寒武系与奥陶系之间的一个重要的层序界面

大青山地区寒武纪－奥陶纪地层分布零星，是由中生代逆冲推覆构造就位于此的一系列近东西向延展的构造岩片组成。下寒武统色麻沟组直接覆盖在太古界变质岩之上，缺失中元古界什那干群，寒武系与奥陶系之间呈假整合接触，标志着区内寒武纪与奥陶纪之间发生过一次抬升构造运动，其形成机制与古亚洲洋内的稳定陆快向华北板块拼贴事件有关，是早奥陶世末期华北板块整体抬升的序幕。这个层序界面的认识，对于恢复华北板块北部边缘构造演化历史具有重要意义。     

Combination of Dependent Realizations Within the Gradual Deformation Method

Gradual deformation is a parameterization method that reduces considerably the unknown parameter space of stochastic models. This method can be used in an iterative optimization procedure for constraining stochastic simulations to data that are complex, nonanalytical functions of the simulated variables. This method is based on the fact that linear combinations of multi-Gaussian random functions remain multi-Gaussian random functions. During the past few years, we developed the gradual deformation method by combining independent realizations. This paper investigates another alternative: the combination of dependent realizations. One of our motivations for combining dependent realizations was to improve the numerical stability of the gradual deformation method. Because of limitations both in the size of simulation grids and in the precision of simulation algorithms, numerical realizations of a stochastic model are never perfectly independent. It was shown that the accumulation of very small dependence between realizations might result in significant structural drift from the initial stochastic model. From the combination of random functions whose covariance and cross-covariance are proportional to each other, we derived a new formulation of the gradual deformation method that can explicitly take into account the numerical dependence between realizations. This new formulation allows us to reduce the structural deterioration during the iterative optimization. The problem of combining dependent realizations also arises when deforming conditional realizations of a stochastic model. As opposed to the combination of independent realizations, combining conditional realizations avoids the additional conditioning step during the optimization process. However, this procedure is limited to global deformations with fixed structural parameters.     

Sensitivity Analysis of a Feedforward Neural Network for Considering Genetic Mechanisms of Kuroko Deposits

Exploration for volcanogenic massive sulfide deposits of the kuroko-type is underway in many places. Clarifying the spatial patterns of the metals in kuroko deposits will be useful for understanding their genetic mechanisms and for future exploration of such types of deposits. This study represents a spatial distribution analysis on the contents of principal metals of kuroko deposits: Cu, Pb, and Zn, in the Hokuroku district, northern Japan, by a feedforward neural network and 1917 sample data at 143 drillhole sites. The network, which consists of three layers, was trained by the principle of SLANS in which the numbers of neurons in the middle layer and training data are changed to improve estimation accuracy. Using the weight coefficients connecting adjacent neurons, sensitivity analysis of the neural network was carried out to identify factors influencing spatial distributions of the three metals. The coordinates depth (z) direction, Bouguer gravity, and specific lithology such as dacite were determined to be influencing factors. The high frequency of the z coordinate signifies that the metal contents differ to a large extent by depth. The sensitivity vector was defined using sensitivity coefficients for x, y, and z coordinates of an estimation point. We determined that the directions of large vectors were different inside and outside of the Hanawa-Ohdate area. This characteristic is considered to originate from the differences in the permeability of fractures that became the paths for rising ore solutions, and the depths that the solutions mixed with sea water.     

基于信息块法的矢量符号库的建立和符号化实现

基于试验成果，详细介绍了基于信息块法的矢量地图符号库的建立和空间信息的符号化。     

无拓扑矢量数据快速压缩算法的研究与实现

首先论述传统矢量数据压缩算法：道格拉斯—普克法，分析将其应用于多边形边界数据压缩所造成的图形失真现象，在此基础上提出一种针对无拓扑矢量数据的快速压缩算法，并在MapInfo环境中实现该算法。     

Ion acoustic instability of HPT particles,FAC density,anomalous resistivity and parallel electric field in the auroral region

During the magnetic storm of 21st March 1990, the DE-1 spacecraft encountered the auroral region at high invariant latitude at altitudes ranging from a few thousand kilometers in the ionosphere to many earth radii in the magnetosphere. The magnetic field perturbations interpretable as field aligned current (FAC) layers and the electrostatic turbulence possibly due to electrostatic ion acoustic instability driven by these currents are shown. The critical drift velocity of Hot Plasma Torus (HPT) electrons and the growth rate of ion acoustic wave as a function of electron to ion temperature ratio (T _e/T_i) for low and high current densities and energy of HPT electrons are found out. The intense FAC destabilizes the ion acoustic wave and the resultant electrostatic turbulence creates an anomalous resistivity. The current driven resistivity produces parallel electric field and high power dissipation. The anomalous resistivityη, potential difference along the auroral field lines V_t|, intensity of electric field turbulenceE _t| and power produced per unit volumeP are computed. It is found that the change in westward magnetic perturbation increasesJ _t|, η, V_t|, E_t| andP. Hence HPT electrons are heated and accelerated due to power dissipation during magnetically active periods in the auroral region. Concerning, applications, such HPT electrons can be used in particle accelerators like electron ring accelerator, smokatron etc.     

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.