海浪的条件特征周期

在海浪波面高度为正态分布的假定下,导出一种以给定波高为条件的条件周期概率密度函数.与风浪槽中测量数据比较,结果表明,在窄谱情况下此概率密度函数与实验室风浪的实际符合良好.根据此密度函数定义了3种条件特征周期,并导出它们与平均周期的关系式.根据这些关系对有关海洋工程上的一些问题作了解释和讨论.     

浙江西沪港重金属铜的配位容量和形态分析

根据2000年6月10～14日在浙江省西沪港采集的海水样品,利用AA-800石墨炉原子吸收分光光度技术和阳极溶出伏安法测定样品中重金属铜的含量,获得铜在海水中受不同的有机配体控制.不同粒级的铜表观配位容量表明西沪港海水过孔径1.00μm微孔膜的(ACuCC)较高,为144.4nmol/dm3;过0.40和0.20μm滤膜的(ACuCC)分别为103.0和102nmol/dm3;铜的有机配体条件稳定常数的对数值在7.25～9.14之间.铜的总量为21.72nmol/dm3.铜全部为稳定溶解态,其中pH2酸溶态占95.0%,强有机结合态占5.0%.溶解态铜中有机结合态占过滤海水中总铜的61.6%.     

极谱法测定海水介质的铜络合容量

采用新极谱技术(1.5次微分)的阳极溶出伏安滴定方法测定天然海水的铜络合容量和条件稳定常数。对测试条件、EDTA回收率和有关问题进行了探讨。青岛近岸水样的九次平行测定表明,铜的表现络合容量为3.01×10~(-7)mol/dm~3,条件稳定常数为7.34×10~7,相对标准偏差分别为9.2％和13.8％。     

时间序列转折突变点检测的线性函数方法

根据转折突变的定义，在引入线性半截多项式的前提下，提出检测时间序列转折突变点的一种新方法。此方法不仅能找出时间序列中存在的多个转折突变点，而且使得检测到的突变点通过统计显著性检验。应用此方法对北半球1851—1990年年平均气温距平及其11年滑动平均序列，1901—1999年昆明5月降水及其11年滑动平均的降水序列分别进行了检测。发现北半球年平均气温距平在1879、1889、1939和1973年附近出现了年际转折突变，在1878、1888和1941年以及1972年出现了年代际尺度的转折突变；昆明5月雨量不存在年际转折突变点，但昆明5月雨量在1918、1966以及1978年附近出现了年代际尺度转折突变。     

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.     

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.     

应力释放模型的改进及其在研究台湾地区地震预测问题中的应用.

应力释放模型过去主要用于研究大范围历史大地震活动规律．本文对应力释放模型进行了改进，对其能否运用于区域更小、时段更短、震级更低的情况进行了探讨；以台湾地区近百年6级以上地震为例的研究结果表明，应力释放模型仍然适用．在回溯性的地震预测检验中，用改进的应力释放模型计算出台湾地区地震发生的条件概率强度，并用其预测6级以上地震的发震时刻．结果表明，其预测精度优于泊松模型．      

Smoothing and Change Point Detection for Gamma Ray Count Data

Gamma ray detectors are used to measure the natural radioactivity of rocks. For a number of boreholes drilled at a site the gamma ray detector is lowered into each borehole and counts of gamma ray emissions at different depths are recorded as the instrument is gradually raised to ground level. The profile of gamma counts can be informative about the geology at each location. The raw count data are highly variable, and in this paper we describe the use of adaptive smoothing techniques and change point models in order to identify changes in the geology based on the gamma logs. We formulate all our models for the data in the framework of the class of generalized linear models, and describe computational methods for Bayesian inference and model selection for generalized linear models that improve on existing techniques. Application is made to gamma ray data from the Castelreagh Waste Management Centre which served as a hazardous waste disposal facility for the Sydney region between March 1974 and August 1998. Understanding the geological structure of this site is important for further modelling the transport of pollutants beneath the waste disposal area.     

Indicator Simulation Accounting for Multiple-Point Statistics

Geostatistical simulation aims at reproducing the variability of the real underlying phenomena. When nonlinear features or large-range connectivity is present, the traditional variogram-based simulation approaches do not provide good reproduction of those features. Connectivity of high and low values is often critical for grades in a mineral deposit. Multiple-point statistics can help to characterize these features. The use of multiple-point statistics in geostatistical simulation was proposed more than 10 years ago, on the basis of the use of training images to extract the statistics. This paper proposes the use of multiple-point statistics extracted from actual data. A method is developed to simulate continuous variables. The indicator kriging probabilities used in sequential indicator simulation are modified by probabilities extracted from multiple-point configurations. The correction is done under the assumption of conditional independence. The practical implementation of the method is illustrated with data from a porphyry copper mine.     

Transformation of Residuals to Avoid Artifacts in Geostatistical Modelling with a Trend

Trend modelling is an important part of natural resource characterization. A common approach to account for a variable with a trend is to decompose it into a relatively smoothly varying trend and a more variable residual component. Then, the residuals are stochastically modelled independent of the trend. This decomposition can result in values outside the plausible range of variability, such as grades below zero or ratios that exceed 1.0. We transform the residuals conditional to the trend component to explicitly remove these complex features prior to geostatistical modelling. Back transformation of the modelled residual values allows the complex relations to be reproduced. A petroleum-related application shows the robustness of the proposed transformation. Furthermore, a mining application shows that when this conditional transformation is applied to the original variable, instead of the residual, simulated values are assured to be nonnegative.