Variations in the strength of recurrent geomagnetic activity in solar cycles 11 to 21

From maximum entropy spectral analysis (MESA) of short lengths ofAa indices of geomagnetic activity, the characteristics of the strength of the 27-day and 13.5-day signals for each of the solar cycles 11 to 21 are highlighted. It is shown that the 13.5-day signal is a near permanent feature in geomagnetic activity. The Hale-cycle (22 yr) effect could be seen in the average magnitude of the 27-day signal with greater strengths in the even cycles. No clear annual variation in the strength is noticed, contrary to some earlier known results.     

An extreme value analysis of the flow of Burbage Brook

An extreme value analysis of the flow of Burbage Brook is carried out by modelling peaks over a high threshold. The aims are to illustrate recently developed statistical techniques and to report on interesting features of the flow of the brook over a 58-year period. Peak flows are found to show marked seasonal variation and a downward trend. Then-year return level is estimated for various values ofn, and the reliability of the estimates is assessed.     

水库诱发地震最大震级的预测

水库蓄水诱发地震虽然是水利工程建设伴生的少见现象,但有时会诱发6级以上地震,加之震中距小,震源浅,成为水利工程抗震设防的重要因素。已有震例还证明,水库规模越大,诱震的可能性越大,诱震的最大震级Mmax(以下简写M)也越高。我们曾提出用水库的“综合影响参数E”来表征水库的“规模”,并给出了M～E回归关系式。本文在补充了部分震例资料,并把M和E以及最大库深Hmax(以下简写H)都视为随机量,经模式识别的分类判别后把所用震例分为两组(第一组为M≥4.5的21个震例:第二组为全部37个震例),把变量E取为确定量或取为随机量和把变量E、lnH取为两个随机量与M进行了回归。回归结果:第一组的双随机量(M、E)模型的回归标准差为口σ_M=0.610,变异系数V=0.118,三随机量(M,E,H)σ_M=0.576。V=0.111;第二组的双随机量的σ_M=1.028,V=0.234,三随机量时σ_M=1.032,V=0.235。说明第一组三随机量模型对可能发生M≥4.5级的新建水库的最大诱震震级的预测是可用的。     

参数估计的统一模型——广义Gauss-Markov模型

设计矩阵可以是秩亏阵,观测值的协方差阵可以是奇异阵的广义 Gauss-Markov模型(简称广义G-M模型),它是一种形式简单的统一模型。本文从最小二乘估计V~TQ~-V=min 出发,研究广义G-M 模型的参数估计理论和方法。说明了V~TQV~-=min与 Rao及Bjeharmmar等的平差原则一致。并对广义 G-M模型之解及其性质进行了系统讨论。     

Inverse methods for subsurface flow: A critical review of stochastic techniques

The development of stochastic methods for groundwater flow representation has undergone enormous expansion in recent years. The calibration of groundwater models, the inverse problem, has lately received comparable attention especially and almost exclusively from the stochastic perspective. In this review we trace the evolution of the methods to date with a specific view toward identifying the most important issues involved in the usefulness of the approaches. The methods are critiqued regarding practical usefulness, and future directions for requisite study are discussed.     

Bayes estimate of the probability of exceedance of annual floods

In this paper Lindley's Bayesian approximation procedure is used to obtain the Bayes estimate of the probability of exceedence of a flood discharge. The Bayes estimates of the probability of exceedence has been shown by S.K. Sinha to be equivalent to the estimate of the probability of exceedence from the predictive or Bayesian disribution, of a future flood discharge. The evaluation of complex ratios of multiple integrals common in a Bayesian analysis is not necessary using Lindley's procedure. The Bayes estimates are compared to those obtained by the method of maximum likelihood and the method of moments. The results show that Bayes estimates of the probability of exceedence are larger as expected, but have smaller posterior standard deviations.     

Amplitude domain analysis for the 30 August 1986 Vrancea intermediate earthquake

The amplitude domain analysis applied to the major Vrancea (Romania) earthquake of 30 August 1986 leads to the determination of some parameters characterizing the amplitude and the time duration of the signal.Digital three-component acceleration data, obtained at 10 Romanian seismic stations with epicentral distances between 40 and 200 km, are used. The results are compared with those obtained by Schenk for strong Californian earthquakes.Paper presented in the 21st General Assembly of the European Seismological Commission, Sofia, 1988.     

基于水位流量关系的流量估算不确定性分析

针对现有水位流量关系线型物理机制不强及流量估算不确定性来源考虑不充分问题,以北江流域石角水文站为例,推导该测站水位流量关系,基于BaRatin模型评估流量测量误差及率定样本选取对估算流量不确定性的影响。发现河槽控制宽浅矩形断面水位流量关系为幂函数,其系数可用糙率、河宽、比降表达,指数为定值5/3;考虑流量测量误差后高水估算流量总不确定性减小32%;率定数据增加1倍、3倍,高水估算流量总不确定性减小12%、34%。结果表明:① 水位流量关系模型建立方法可推广至多类型测站;② 高水测量误差对率定精度影响较大,建议提高高水流量测量精度;③ 现有实测水位与流量数据存在信息冗余,主要存在于低水数据中,本方法可减少率定数据使用,降低整编成本。     

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.