太阳风引起的地磁扰动的参数辨识

作为太阳风引起的地磁扰动的系统辨识一文的姊妹篇,本文利用参数系统辨识方法,对太阳风-磁层耦合系统做了进一步的探讨.根据地磁扰动机制的理论研究,地磁扰动被认为是太阳风能量耦合进入磁层以及由此引起的一系列磁层内能量的输运和耗散的结果.在此基础上,对太阳风-磁层系统的非线性情况进行了讨论,建立了D_st指数与太阳风参量的非线性函数关系,给出了由此得到的地磁扰动计算结果.同时还利用1967年2月3-12日和1980年12月16-23日的两次事件,对已得到的函数关系进行了验证和讨论.验证结果不仅对模型的合理性、普适性做了很好的说明,也表明这种非线性函数关系作为定量的地磁预报模型,具有一定的可信度和实用性.     

矿床规模频率法在铀资源评价中的应用和效果初探

矿床规模频率法(DSF)是由美国数学地质博士R.B.McCammon和地质专家W.J.Finch等开发并新近引入我国的矿产资源评价方法。该方法具有快速而更加准确地估算未发现矿产资源量的特点。作者在我国西北某铀成矿区采用该方法进行铀资源评价的具体实践,介绍了方法应用中有关计算方程各种因子的选用等问题,同时,对方法的应用效果、各种因子取值对计算结果的影响等进行了初步分析和探讨。     

Weibull分布参数估计方法及其应用

Ｗｅｉｂｕｌｌ分布对于年最大风速、波高、降水量等气候极值具有很高的拟合精度和很强的适应性。本文给出四种Ｗｅｉｂｕｌｌ分布参数估计方法：概率权重矩法（ＰＷＭ）、最小二乘法（ＬＳＭ）、最大似然法（Ｍ－Ｌ）和适线法（ＦＩＴ），并通过统计检验进行拟会精度比较。结果表明：ＰＷＭ、ＬＳＭ和Ｍ－Ｌ法对样本的拟合精度都比较高，其中ＰＷＭ法计算最为简便，ＬＳＭ和Ｍ－Ｌ法采取ａ０的试设法，大大简化了计算过程；经验累积频率公式更适合于Ｗｅｉｂｕｌｌ分布；三参数计算精度较二参数高得多。     

具有时变参数的门限自回归模型及其在气候预报中的应用

将时变参数模型引入门限自回归模型中，提出具有时变参数的门限自回归模型，并对昆明，蒙自，河口等地区３月温度序列进行预报，结果表明：这种模型比门限自回归模型的预报准确度有明显提高，这是因为用这种模型进行预报时，可以随时更新预报模式。     

东海宝云亭三井热解录井方法应用研究的探讨

有机地化(热解)录井是一种新的录井方法,在东海的油气勘探工作中已得到较好的应用效果。在试油前能将录井资料进行综合整理与解释,随钻预测钻井中的含油气层段,为油气层位的确定提供依据。     

边坡工程的爆破效应分析

本文以四川省某中型水电站坝肩边地皮工程为例，讨论了边坡稳定性评价中的爆破效应，论证了目前边坡工程中采用的经验方法。并在此基础上。提出了“动态反演参数优定法”，对边坡工程的有关参数作了合理的调整，所采用优定参数在边坡稳定性评价预测及工程处理中取得了良好的效果。     

人工神经网络及其在地震预报中的应用

文概述了人工神经网络的原理和算法，利用１９８５－１９９２年全国年度趋势会商报告的资料来训练的检验神经网络。结果表明，网络经训练后具有较高的识别能力，在地震预报中有深入研究和进一步应用的价值。最后讨论了神经网络中几个重要参数的取值问题。     

Inverse analysis techniques for parameter identification in simulation of excavation support systems

Two numerical procedures are described that quantitatively identify a set of constitutive parameters that best represents observed ground movement data associated with deep excavations in urban environments. This inverse problem is solved by minimizing an objective (or error) function of the weighted least-squares type that contains the difference between observed and calculated ground displacements. The problem is solved with two different minimization algorithms, one based on a gradient method and the other on a genetic algorithm. The objective function is shown to be smooth with a unique solution. Both methods are applied to lateral movements from synthetic and real excavations to illustrate various aspects of the implementation of the methods. The advantages and disadvantages of each method applied to excavation problems are discussed.     

Melt Pond Scheme Parameter Estimation Using an Adjoint Model

Melt ponds significantly affect Arctic sea ice thermodynamic processes. The melt pond parameterization scheme in the Los Alamos sea ice model(CICE6.0) can predict the volume, area fraction(the ratio between melt pond area to sea ice area in a model grid), and depth of melt ponds. However, this scheme has some uncertain parameters that affect melt pond simulations. These parameters could be determined through a conventional parameter estimation method, which requires a large number of sensitivity simulations. The adjoint model can calculate the parameter sensitivity efficiently. In the present research, an adjoint model was developed for the CESM(Community Earth System Model) melt pond scheme. A melt pond parameter estimation algorithm was then developed based on the CICE6.0 sea ice model, melt pond adjoint model,and L-BFGS(Limited-memory Broyden-Fletcher-Goldfard-Shanno) minimization algorithm. The parameter estimation algorithm was verified under idealized conditions. By using MODIS(Moderate Resolution Imaging Spectroradiometer)melt pond fraction observation as a constraint and the developed parameter estimation algorithm, the melt pond aspect ratio parameter in CESM scheme, which is defined as the ratio between pond depth and pond area fraction, was estimated every eight days during summertime for two different regions in the Arctic. One region was covered by multi-year ice(MYI) and the other by first-year ice(FYI). The estimated parameter was then used in simulations and the results show that:(1) the estimated parameter varies over time and is quite different for MYI and FYI;(2) the estimated parameter improved the simulation of the melt pond fraction.