Optimal Filtering Algorithm for Stochastic 2-D FMM Ⅱ with Multiplicative Noise

1　Introduction　Inrecentyearstherehasbeengrowingresearchin terestinthetwo dimensional (2 D )systemtheory .The 2 Dsystemsmayfindapplicationsinareassuchasmarineseismicdataprocessingandimageprocessing .Althoughmoreandmorevaluableresultshavebeengained ,mos…     

长江中下游金属矿找矿前景与找矿方法

本文论述了长江中下游地区金属矿床的地质、地球物理和地球化学特点,提出今后深部找矿的主要对象是：找隐伏含矿岩体及有关的各类矿体,找岩体中的大型斑岩型矿床,找受岩体和五通砂岩联合控制的层状矿;讨论了地球物理和地球化学方法应用中要注意解决的几个主要问题,强调应发挥包括重力、磁法、激发极化法、电磁法、岩性探测仪及多种天然地震法和化探方法等综合方法的作用,并与地质矿床成矿规律研究密切结合;讨论了方法应用试验中要注意的几个问题。文章最后一部分是从本区大地构造的演化探讨了区域构造岩浆活动与成矿前景,认为这一地区是扬子板块与华北板块陆-陆碰撞挤压造山带及其前陆区,在岩石圈强烈挤压下形成地壳增厚和深部物质的挤出折返,使深部的高压和超高压变质岩层推到地壳浅部;深部生成的柯石英等高压超高压矿物及地幔熔融岩浆同时上侵;生成大量钙碱性岩浆并存储在中地壳部位,通过长期与中下地壳金属物质进行交换,形成矿液的集中优势,通过后期出现的张性断裂构造而进入地壳表层,再经过与围岩发生物理化学成矿作用后沉淀成矿。归纳出扬子板块与华北板块陆-陆碰撞造山带一种新的构造模式。     

作用于平台桩腿的海冰屈曲破坏冰压力随机过程模型及其参数确定

冰对结构的作用过程是典型的随机过程。本文在冰压力过程为平稳过程的假设下，从渤海海冰对平台桩腿作用的大量实测冰压力数据中，选取了２１条冰屈曲破坏时的冰压力时程曲线，对它们进行谱分析后，得到了单点冰屈曲破坏的压力随机过程的谱密度，并确定了谱参数及其跟环境要素的关系，依据文献［１］中冰压力沿圆柱面的空间分布，建立了绕桩腿的冰压力随机场模型，并得到了作用于桩腿的总冰力随机过程及其谱密度。本文的研究成果为平台结构冰激随机振动和疲劳累积损伤分析提供了荷载基础     

随机Dirichlet问题解的调和展开

针对重力学随机Dirichlet问题,通过适当地对边界检验函数的分解,并在随机边界样本空间中提取确定性部分的对偶基,本文将随机Dirichlet问题的一般解展开为一随机系数的调和级数形式。     

GPS signal acquisition using the repeatability of successive code phase measurements

This paper introduces a new method for GPS signal acquisition, which is based on the repeatability of successive code phase measurements and the M-of-N search algorithm. The performance of the proposed method in terms of probability of signal detection is similar to that of traditional methods, except that the calculation of the probability of detection does not rely on the noise distribution or the Carrier-to-Noise ratio (C/N₀). The code phase repeatability-based method is presented along with equations for probability of detection and probability of false detection. If the distribution of the noise is known, it also provides an estimate of the C/N₀. The proposed method is illustrated for coherent and non-coherent acquisition and C/N₀ estimation.     

Modelling Positional Uncertainty of Line Features by Accounting for Stochastic Deviations from Straight Line Segments

The assessment of positional uncertainty in line and area features is often based on uncertainty in the coordinates of their elementary vertices which are assumed to be connected by straight lines. Such an approach disregards uncertainty caused by sampling and approximation of a curvilinear feature by a sequence of straight line segments. In this article, a method is proposed that also allows for the latter type of uncertainty by modelling random rectangular deviations from the conventional straight line segments. Using the model on a dense network of sub‐vertices, the contribution of uncertainty due to approximation is emphasised; the sampling effect can be assessed by applying it on a small set of randomly inserted sub‐vertices. A case study demonstrates a feasible way of parameterisation based on assumptions of joint normal distributions for positional errors of the vertices and the rectangular deviations and a uniform distribution of missed sub‐vertices along line segments. Depending on the magnitudes of the different sources of uncertainty, not accounting for potential deviations from straight line segments may drastically underestimate the positional uncertainty of line features.     

Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

This paper proposes a novel history-matching method where reservoir structure is inverted from dynamic fluid flow response. The proposed workflow consists of searching for models that match production history from a large set of prior structural model realizations. This prior set represents the reservoir structural uncertainty because of interpretation uncertainty on seismic sections. To make such a search effective, we introduce a parameter space defined with a “similarity distance” for accommodating this large set of realizations. The inverse solutions are found using a stochastic search method. Realistic reservoir examples are presented to prove the applicability of the proposed method.     

Developing a SPASE Query Language

The advent of the Virtual Observatory has begun an evolution in the space physics data environment. A number of nascent and discipline specific Virtual Observatories have started to emerge with an emphasis on data search and retrieval. As this new data environment takes shape an emphasis will be placed on interdisciplinary communication in attempts to address large scale and global problems. To this end we formulate the development of a query language to facilitate Virtual Observatory to Virtual Observatory communication. Furthermore, we outline the goals of such a language, how it would work and how existing community efforts can be leveraged to speed the development of this query language.

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.