地震信号的相位恢复及误差分析

常见的几种地震子波有:零相位子波、最小相位子波、最大相位子波和混合相位子波。以不同类型的信号为激发子波,对不同地层模型合成地震道。仅用原始合成地震道记录的振幅谱来恢复原始记录,讨论不同方法下,由于激发子波的不同,对比分析原始记录与恢复记录的差异,及其恢复效果。     

我国西部1∶5万地形图空白区地物解译研究——以西藏那曲其香错地区为例

我国西部部分地区1∶5万地形图数据尚未完成,给区域和国家经济发展带来极大不便。空间技术、计算机技术和信息技术的高速发展为遥感技术应用提供了技术支持,高分辨率影像获取能力的提高为大比例尺地物特征的提取提供了数据可能。本文采用SPOT5遥感影像,以1∶5万地形图图式标准为依据,进行青藏高原其香错地区地物判读,结果表明SPOT5能基本满足1∶5万地形图制图要求。为进一步提高判读结果,可采用多时相、多分辨率影像进行1∶5万地形图地物判读。     

GPS接收机专用芯片组技术发展

在总结国外现有的完整GPS接收机专用芯片组最新情况的基础上，结合典型的芯片产品，阐述了GPS接收机专用芯片组从分立式器件到片上系统、再到单片式接收机的技术发展过程、现状和趋势。     

参量阵浅地层剖面测量技术 在近岸海洋工程的应用效果

介绍了海底浅地层声学探测技术的发展情况, 简述了新型参量阵声纳探测的原理和特点, 通过SES-96参量阵测深或浅地层剖面系统在山东半岛海区的应用实例, 对此新型参量阵声纳技术的应用效果进行了分析评价。     

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。     

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。     

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).     

关于ENSO及其统计预测方法评估

结合近年来“九五”国家重中之重项目有关ENSO业务预报系统研究工作的体会和见解 ,简要综述了近年来ENSO的预报问题 ,尤其是ENSO统计预测方法的研究成果 ,并对各种预报方法的效果进行评价。     

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.