ODP204航次天然气水合物的可能有利储层——浊积层

为了解岩性在天然气水合物分布中的作用, 利用岩矿鉴定、扫描电镜和能谱法对ODP204航次的 4个站位 10个钻孔共计 115个沉积物样品进行分析, 发现沉积物中矿物成分基本相近, 以粘土矿物为主, 含少量以石英为主的陆源碎屑。样品中含有丰富的硅藻和有孔虫等微体化石, 可形成硅藻粘土—硅藻土、有孔虫硅藻土或凝灰质硅质生物粘土。石英碎屑大多呈棱角状, 粒径小、含量少, 同时, 样品中可见透镜体、团块或旋涡状层状结构以及杂乱堆积的斑杂构造。上述特征表明沉积物为含有大量深海或半深海的浊流沉积物或其夹层。由于钻井岩心中有相当部分的天然气水合物样品分布在上述沉积物中, 推断这些浊流沉积物很可能是ODP204航次天然气水合物的有利储层。     

沁水盆地煤层气钻井工艺方法

针对沁水盆地煤层气概况,地层情况及开采条件,介绍了参数井、生产井、多分支地面煤层气水平开发井、丛式井等钻井工艺、钻具组合、井身结构及井身质量要求.     

水气两相流系统K-S-P模型参数反演的最优估计

应用渗流力学和参数反演辨识最优化理论,建立了水气两相流控制方程,给出了控制两相流系统K-S-P模型参数之间的本构关系,并对其中的相关参数进行数值反演辨识,得出待求参数的最优估计值。对不同质地土壤的实验测试值进行对比分析,其结果吻合较好,验证了模型的适定性和可靠性,解决了实验数据处理以及结果对初值的敏感性问题,并且该数值方法可通过多组初值参数进行数值实验,结合实验结果来满足实际工程的需求。这不仅对非饱和带内污染物传输、气藏工程试井计算、填埋气体的释放以及挥发性有机污染物的去污分析中两相流数值计算提供基础,而且对K-S-P本构关系之间参数的确定提供了可靠的理论依据。     

平差系统的模型误差及其识别方法研究

论述了模型误差影响参数估值的一些理论问题,指出了随机模型误差和函数模型误差之间的相互作用和转化。为讨论平差系统最优模型的选取,给出了与现有文献将模型误差纳入平差系统的思路不同的一个估计和识别模型误差的理论基础公式,由此导出了相应的实用公式,给出了平差系统模型的优选方法。     

附加系统参数平差法在导线网中的应用

本文探讨了附加系统参数平差法在带有测距系统误差的导线网中的应用，说明了当系统误差比较显著时．采用附加系统参数平差法的可行性、必要性及其优越性。     

A new method for indentifying the model error of adjustment system

Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given. Project supported by the Open Research Fund Program of the Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (No. 905276031-04-10).     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

Parameter identification of inelastic structures under dynamic loads

The generalized model of differential hysteresis contains 13 control parameters with which it can curve‐fit practically any hysteretic trace. Three identification algorithms are developed to estimate the control parameters of hysteresis for different classes of inelastic structures. These algorithms are based upon the simplex, extended Kalman filter, and generalized reduced gradient methods. Novel techniques such as global search and internal constraints are incorporated to facilitate convergence and stability. Effectiveness of the devised algorithms is demonstrated through simulations of two inelastic systems with both pinching and degradation characteristics in their hysteretic traces. Owing to very modest computing requirements, these identification algorithms may become acceptable as a design tool for mapping the hysteretic traces of inelastic structures. Copyright © 2002 John Wiley & Sons, Ltd.     

应用CP网络进行岩性识别

为通过测井解决岩性识别问题，引入了具有分类准确、算法简练等优点的CP（Counter-Propagation)网络。在详细介绍CP网络的网络模型和算法的基础上，结合某油田的实际测井资料，进行了CP网络识别研究。应用结果表明：CP网络训练周期短、识别准确率高、不存在收敛问题。通过试验研究得出结论：CP网络完全可以用于解决岩性识别等问题，具有广阔的应用前景。     

沉积物物源定量识别的非线性规划模型——以东海陆架北部表层沉积物物源识别为例

依据“质量守衡”原理和沉积物物源难以穷尽的认识 ,提出了沉积物物源定量识别的非线性规划数学模型。利用该模型实现了东海陆架北部表层沉积物物源的定量计算 ,阐明了长江、黄河入海物质的大致分布特征。长江沉积物主要分布于 32°N以南、台湾暖流以西的海域 ,黄河沉积物主要分布于 32°N以北、台湾暖流以东的海域。长江、黄河两类沉积物的这种分布格局与本海域的流系分布相吻合。