相空间反演方法在表层水温预报中的应用

利用相空间理论及方法对渤、黄、东海共4个站位近十几年的旬平均SST进行分析。结果表明：表层水温具有混饨特性，其吸引子关联维数平均约为1．23、嵌入相空间维数为6（渤、黄海）和7（东海178号站位）、二阶Renyi熵平均约为3．7×10-4（1／d）及平均可预报时间尺度平均为27个点；基于以上分析结果运用相空间反演方法建立了旬平均SST的反演模型，并且在试预报的前5旬的最大相对误差约为4．2％。     

Surface Elevation Distribution of Sea Waves Based on the Maximum Entropy Principle

A probability density function of surface elevation is obtained through improvement of the method introduced by Cieslikiewicz who employed the maximum entropy principle to investigate the surface elevation distribution. The density function can be easily extended to higher order according to demand and is non-negative everywhere, satisfying the basic behavior of the probability. Moreover because the distribution is derived without any assumption about sea waves, it is found from comparison with several accepted distributions that the new form of distribution can be applied in a wider range of wave conditions. In addition, the density function can be used to fit some observed distributions of surface vertical acceleration although something remains unsolved.     

长江口枯季悬沙粒度与浓度之间的关系

2003和2004年枯季在长江口采集水样并作水文观测，对所获水样进行过滤和粒度分析，以计算悬沙浓度和悬沙粒度分布。结果表明，2003年11月小潮期间，悬沙中值粒径与悬沙浓度存在着显著的指数关系，在大潮期间没有显著关系；在2004年2月小湖期间，两者之间没有显著关系，但在大潮期间存在着显著的指数关系。枯季水体悬沙以粉砂组份为主，并且随着向口外的推移，细颗粒组份逐渐增加，但在拦门沙最大浑浊带附近，由于絮凝作用，沉积物粒度变幅较大，可产生粒径粗化的现象。小潮期间，砂含量较低，但与悬沙浓度之间有显著相关关系；大潮期间，悬沙粒径粗化，但砂含量与悬沙浓度之间的关系不显著。上述分布趋势与沉积物来源、当地的水动力条件和絮凝作用等因素有密切关系。     

太平洋波高熵的分布变化规律研究

使用1992年IO月到1998年12月连续共75个月、230个重复周期的Topex／Poseidon卫星高度计有效波高资料，对南、北太平洋波高熵的空间分布特征和时间变化规律进行了研究。统计分析了太平洋波高熵的多年的空间分布特征和多年各月的时间变化规律。结果表明，太平洋波高熵呈现出中间低、南北高的马鞍形空间分布特征和明显季节变化的规律，与太平洋的平均有效波高和气候分布特征和变化规律相一致。给出了南北太平洋模拟波高熵的计算公式及计算稳定性检验。     

等高线图信息定量度量研究

在分析现有的地图信息定量度量方法的基础上,提出了一组测量等高线地形图的几何信息和拓扑信息的定量度量方法,并进行了实例分析.     

Seismic hazard of Egypt

Earthquake hazard parameters such as maximum expected magnitude,M _max, annual activity rate,, andb value of the Gutenberg-Richter relation have been evaluated for two regions of Egypt. The applied maximum likelihood method permits the combination of both historical and instrumental data. The catalogue used covers earthquakes with magnitude 3 from the time interval 320–1987. The uncertainties in magnitude estimates and threshold of completeness were taken into account. The hazard parameter determination is performed for two study areas. The first area, Gulf of Suez, has higher seismicity level than the second, all other active zones in Egypt.b-values of 1.2 ± 0.1 and 1.0 ± 0.1 are obtained for the two areas, respectively. The number of annually expected earthquakes with magnitude 3 is much larger in the Gulf of Suez, 39 ± 2 than in the other areas, 6.1 ± 0.5. The maximum expected magnitude is calculated to be 6.5 ± 0.4 for a time span of 209 years for the Gulf of Suez and 6.1 ± 0.3 for a time span of 1667 years for the remaining active areas in Egypt. Respective periods of 10 and 20 years were reported for earthquakes of magnitude 5.0 for the two subareas.     

Estimation of covariance matrix from geochemical data with observations below detection limits

Multivariate statistical analyses have been extensively applied to geochemical measurements to analyze and aid interpretation of the data. Estimation of the covariance matrix of multivariate observations is the first task in multivariate analysis. However, geochemical data for the rare elements, especially Ag, Au, and platinum-group elements, usually contain observations the below detection limits. In particular, Instrumental Neutron Activation Analysis (INAA) for the rare elements produces multilevel and possibly extremely high detection limits depending on the sample weight. Traditionally, in applying multivariate analysis to such incomplete data, the observations below detection limits are first substituted, for example, each observation below the detection limit is replaced by a certain percentage of that limit, and then the standard statistical computer packages or techniques are used to obtain the analysis of the data. If a number of samples with observations below detection limits is small, or the detection limits are relatively near zero, the results may be reasonable and most geological interpretations or conclusions are probably valid. In this paper, a new method is proposed to estimate the covariance matrix from a dataset containing observations below multilevel detection limits by using the marginal maximum likelihood estimation (MMLE) method. For each pair of variables, sayY andZ whose observations containing below detection limits, the proposed method consists of three steps: (i) for each variable separately obtaining the marginal MLE for the means and the variances, , , , and forY andZ: (ii) defining new variables by and and lettingA=C+D andB=C–D, and obtaining MLE for variances, and forA andB; (iii) estimating the correlation coefficient _YZ by and the covariance _YZ by . The procedure is illustrated by using a precious metal geochemical data set from the Fox River Sill, Manitoba, Canada.     

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.