模拟退火算法在控制网平差中的应用

线性最小二乘估计在对非线性函数进行线性近似的过程中会产生模型误差,而一些非线性参数估计方法可能因为函数复杂而难以求导,法方程系数矩阵秩亏或呈病态矩阵时难以求解,非线性迭代解法有时对初始值的选择存在依赖性,不恰当的初始值会导致迭代无法收敛。针对这些问题,引入了模拟退火算法,介绍了该算法的基本原理、计算步骤和收敛性,并以3个控制网平差应用为例,说明该算法具有无需求导求逆,简洁实用,易于编程等优势,并能实现全局优化,获得高精度的平差结果。     

最简单路径寻找方法研究

许多认知研究表明,路径指令的复杂性和形式在人类导航中和路径的长度一样重要。在很多情形下,我们所需要的不是到达目的地的最短路径,而是一条最简单路径,也就是容易描述、理解、记忆或者执行路线的导航指令。大多数自动导航系统都依赖于计算最短路径问题的解,而不是找到最简单路径。本文在分析人们对道路转向的认知研究基础上,采用了适合道路转向的路段-链数据模型,并从权重函数选择、算法具体实现和算法分析三个方面具体论述了最简单路径算法的主要思想和具体实施。     

Numerical simulation of a stratigraphic model

A multi-lithology diffusive stratigraphic model is considered, which simulates at large scales in space and time the infill of sedimentary basins governed by the interaction between tectonics displacements, eustatic variations, sediment supply, and sediment transport laws. The model accounts for the mass conservation of each sediment lithology resulting in a mixed parabolic, hyperbolic system of partial differential equations (PDEs) for the lithology concentrations and the sediment thickness. It also takes into account a limit on the rock alteration velocity modeled as a unilaterality constraint. To obtain a robust, fast, and accurate simulation, fully and semi-implicit finite volume discre tization schemes are derived for which the existence of stable solutions is proved. Then, the set of nonlinear equations is solved using a Newton algorithm adapted to the unilaterality constraint, and preconditioning strategies are defined for the solution of the linear system at each Newton iteration. They are based on an algebraic approximate decoupling of the sediment thickness and the concentration variables as well as on a proper preconditioning of each variable. These algorithms are studied and compared in terms of robustness, scalability, and efficiency on two real basin test cases.     

基于ICCP算法的重力辅助惯性导航

迭代最近等值线算法(ICCP)是一种重要的匹配导航算法,文中首先介绍ICCP算法的基本原理,随后在0.2′×0.2′重力异常数据库的基础上,利用ICCP算法进行仿真计算得到最佳匹配位置。最后为了验证匹配位置是否可用于修正惯导误差,提出将匹配位置误差作为观测量,用卡尔曼滤波对惯导系统误差进行最优估计。由最后的仿真结果可以看出,ICCP算法可有效抑制惯导纬度误差的增长,且最大纬度误差不超过2,′以匹配位置误差作为观测量可以用来估计惯导方位误差角。     

顾及DEM误差自相关的坡度计算模型精度分析

基于DEM的坡度计算,其误差来源于DEM误差、DEM结构和坡度计算模型。在顾及DEM误差自相关的前提下,对四种DEM坡度计算模型进行了分析和评价。研究表明,三阶不带权差分能给出较高的坡度计算精度;在局部窗口中,格网点数量越多,坡度计算越准确;等权比不等权的坡度计算模型更准确;DEM误差自相关结构形式对坡度计算无影响。进一步的理论分析和试验分析还表明：DEM误差自相关性的存在,不仅能够改善地形分析的精度,也能改善DEM自身精度。     

基于规则库和网格算法的土地利用现状图自动数字注记

图形自动注记可极大地提高制图的工作效率。针对土地利用现状图数字注记的特点,本文从地图认知角度出发,建立注记自动配置的规则库,提出面状要素自动注记的一种新的算法——网格法。该算法最大的优点是容易实现、效率高,可方便地解决注记压盖冲突等问题。通过开发一个程序实例,对图斑要素进行了自动数字注记,实验表明95％以上的图形注记效果都较为理想。     

Aspherical correction for free vibration of elastically anisotropic solid by means of theXYZ algorithm

By applying the perturbation theory to theXYZ algorithm (a kind of variational method), the difference f in free vibration frequencies between sphere and ellipsoid was approximated as , where i and _i (i = x,y andz) (i=x, y andz) are aspherical coefficients and asphericities of the ellipsoid, respectively. We developed an analytic method to compute the aspherical coefficients719-4 by using theXYZ algorithm. A numerical example was given for an ellipsoidal olivine, and an attempt was made to estimate the asphericities of the specimen by a least-squares method, based on the relationship between frequency shift and asphericity.     

北大别饶拔寨石榴辉石岩P-T-t轨迹及其构造意义

P-T-t轨迹作为变质岩的重要研究方法之一，对揭示岩石的构造演化过程具有重要意义。北大别饶拔寨镁铁-超镁铁岩形成的构造环境和就位过程长期以来尚存争议。本文通过岩相学观察、矿物化学研究和温压计计算，揭示出饶拔寨石榴辉石岩经历了四个变质演化阶段：1）超高压变质阶段（M1）：主要根据石榴子石中金红石的出溶，单斜辉石中石英的出溶和磷灰石中不透明矿物的出溶，结合前人研究，认为饶拔寨石榴辉石岩经历过超高压变质阶段（≥2.5GPa）；2）高压麻粒岩相阶段（M2）：矿物组合为石榴子石（变斑晶）和单斜辉石（基质）+斜长石（基质），记录的温压条件为T=648~700℃，P=1.47~1.94GPa；3）中压麻粒岩相阶段（M3）：以石榴子石外围发育的主要由斜方辉石+斜长石组成的内圈"白眼圈"为特征，形成的温压条件为T=781~796℃，P=0.92~0.98GPa；4）角闪岩相阶段（M4）：以石榴子石变斑晶周围发育的外圈"白眼圈"为特征，其矿物组合为角闪石+斜长石的后成合晶，形成的温压条件为T=663~685℃，P=0.50~0.58GPa。石榴辉石岩的锆石SIMS U-Pb定年得到了3组不同的交点年龄，分别为208.1~202.1Ma、227.6Ma和817.7Ma。根据锆石包裹体中角闪石+斜长石的组合，推断208.1~202.1Ma代表了角闪岩相变质年龄的下限，227.6Ma则是高压麻粒岩相的下限年龄，而新元古代年龄（817.7Ma）与区域上的变质岩原岩年龄一致，可能代表了其原岩年龄。结合前人研究，饶拔寨石榴辉石岩记录了顺时针的P-T-t轨迹，揭示了板片俯冲（超高压变质）-碰撞-折返（降压升温过程，~227Ma）-抬升（降压降温过程，208~202Ma）的完整过程。     

Estimation for partially observed Markov processes

Many stochastic process models for environmental data sets assume a process of relatively simple structure which is in some sense partially observed. That is, there is an underlying process (X_n, n 0) or (X_t, t 0) for which the parameters are of interest and physically meaningful, and an observable process (Y_n, n 0) or (Y_t, t 0) which depends on the X process but not otherwise on those parameters. Examples are wide ranging: the Y process may be the X process with missing observations; the Y process may be the X process observed with a noise component; the X process might constitute a random environment for the Y process, as with hidden Markov models; the Y process might be a lower dimensional function or reduction of the X process. In principle, maximum likelihood estimation for the X process parameters can be carried out by some form of the EM algorithm applied to the Y process data. In the paper we review some current methods for exact and approximate maximum likelihood estimation. We illustrate some of the issues by considering how to estimate the parameters of a stochastic Nash cascade model for runoff. In the case of k reservoirs, the outputs of these reservoirs form a k dimensional vector Markov process, of which only the kth coordinate process is observed, usually at a discrete sample of time points.     

Reservoir description using dynamic parameterisation selection with a combined stochastic and gradient search

There is a correspondence between flow in a reservoir and large scale permeability trends. This correspondence can be derived by constraining reservoir models using observed production data. One of the challenges in deriving the permeability distribution of a field using production data involves determination of the scale of resolution of the permeability. The Adaptive Multiscale Estimation (AME) seeks to overcome the problems related to choosing the resolution of the permeability field by a dynamic parameterisation selection. The standard AME uses a gradient algorithm in solving several optimisation problems with increasing permeability resolution. This paper presents a hybrid algorithm which combines a gradient search and a stochastic algorithm to improve the robustness of the dynamic parameterisation selection. At low dimension, we use the stochastic algorithm to generate several optimised models. We use information from all these produced models to find new optimal refinements, and start out new optimisations with several unequally suggested parameterisations. At higher dimensions we change to a gradient-type optimiser, where the initial solution is chosen from the ensemble of models suggested by the stochastic algorithm. The selection is based on a predefined criterion. We demonstrate the robustness of the hybrid algorithm on sample synthetic cases, which most of them were considered insolvable using the standard AME algorithm.