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…     

High-pressure eclogites in northern Jiangsu – southern Shandong province, eastern China

Abstract Eclogites are distributed for more than 500 km along a major tectonic boundary between the Sino-Korean and Yangtze cratons in central and eastern China. These eclogites usually have high-P assemblages including omphacite + kyanite and/or coesite (or its pseudomorph), and form a high-P eclogite terrane. They occur as isolated lenses or blocks 10 cm to 300 m long in gneisses (Type I), serpentinized garnet peridotites (Type II) and marbles (Type III). Type I eclogites were formed by prograde metamorphism, and their primary metamorphic mineral assemblage consists mainly of garnet [pyrope (Prp) = 15–40 mol%], omphacite [jadeite (Jd) = 34–64 mol%], pargasitic amphibole, kyanite, phengitic muscovite, zoisite, an SiO₂ phase, apatite, rutile and zircon. Type II eclogites characteristically contain no SiO₂ phase, and are divided into prograde eclogites and mantle-derived eclogites. The prograde eclogites of Type II are petrographically similar to Type I eclogites. The mantle-derived eclogites have high MgO/(FeO + Fe₂O₃) and Cr₂O₃ compositions in bulk rock and minerals, and consist mainly of pyrope-rich garnet (Prp = 48–60 mol%), sodic augite (Jd = 10–27 mol%) and rutile. Type III eclogites have an unusual mineral assemblage of grossular-rich (Grs = 57 mol%) garnet + omphacite (Jd = 30–34 mol%) + pargasite + rutile. Pargasitic and taramitic amphiboles, calcic plagioclase (An₆₈), epidote, zoisite, K-feldspar and paragonite occur as inclusions in garnet and omphacite in the prograde eclogites. This suggests that the prograde eclogites were formed by recrystallization of epidote amphibolite and/or amphibolite facies rocks with near-isothermal compression reflecting crustal thickening during continent–continent collision of late Proterozoic age. Equilibrium conditions of the prograde eclogites range from P > 26 kbar and T= 500–750°C in the western part to P > 28 kbar and T= 810–880°C in the eastern part of the high-P eclogite terrane. The prograde eclogites in the eastern part are considered to have been derived from a deeper position than those in the western part. Subsequent reactions, manifested by (1) narrow rims of sodic plagioclase or paragonite on kyanite and (2) symplectites between omphacite and quartz are interpreted as an effect of near-isothermal decompression during the retrograde stage. The conditions at which symplectites re-equilibrated tend to increase from west (P < 10 kbar and T < 580°C) to east (P > 9 kbar and T > 680°C). Equilibrium temperatures of Type II mantle-derived eclogites and Type III eclogite are 730–750°C and 680°C, respectively.     

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。     

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.     

EN photographic Perseids

Many Perseid meteors were photographed in the Czech part of the European Fireball Network during the activity of the new strong and sharp maximum on August 12, 1993. Basic data on many of them were evaluated and radiants, atmospheric trajectories and some orbital elements are presented here and compared with atmospheric and orbital data of regular Perseids, which were photographed outside the new activity in 1993 and preceding years. No substantial difference between these two groups of Perseid meteors was found.     

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.     

Optimization of nonconventional wells under uncertainty using statistical proxies

The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the optimization. In this study, a genetic algorithm is applied to optimize the deployment of nonconventional wells. Geological uncertainty is accounted for by optimizing over multiple reservoir models (realizations) subject to a prescribed risk attitude. To reduce the excessive computational requirements of the base method, a new statistical proxy (which provides fast estimates of the objective function) based on cluster analysis is introduced into the optimization process. This proxy provides an estimate of the cumulative distribution function (CDF) of the scenario performance, which enables the quantification of proxy uncertainty. Knowledge of the proxy-based performance estimate in conjunction with the proxy CDF enables the systematic selection of the most appropriate scenarios for full simulation. Application of the overall method for the optimization of monobore and dual-lateral well placement demonstrates the performance of the hybrid optimization procedure. Specifically, it is shown that by simulating only 10% or 20% of the scenarios (as determined by application of the proxy), optimization results very close to those achieved by simulating all cases are obtained.     

Evaluation of the Reduction in Uncertainty Obtained by Conditioning a 3D Stochastic Channel to Multiwell Pressure Data

A stochastic channel embedded in a background facies is conditioned to data observed at wells. The background facies is a fixed rectangular box. The model parameters consist of geometric parameters that describe the shape, size, and location of the channel, and permeability and porosity in the channel and nonchannel facies. We extend methodology previously developed to condition a stochastic channel to well-test pressure data, and well observations of the channel thickness and the depth of the top of the channel. The main objective of this work is to characterize the reduction in uncertainty in channel model parameters and predicted reservoir performance that can be achieved by conditioning to well-test pressure data at one or more wells. Multiple conditional realizations of the geometric parameters and rock properties are generated to evaluate the uncertainty in model parameters. The ensemble of predictions of reservoir performance generated from the suite of realizations provides a Monte Carlo estimate of the uncertainty in future performance predictions. In addition, we provide some insight on how prior variances, data measurement errors, and sensitivity coefficients interact to determine the reduction in model parameters obtained by conditioning to pressure data and examine the value of active and observation well data in resolving model parameters.