QN—模型及其在覆盖区资源靶区预测中的应用

本文把数量化理论Ⅲ与Ｑ－型非线性映射在机地结合在一起，定义了一种新的数学模型－ＱＮ－模型。详细地介绍了该模型的基本原理和计算步骤，给出了模型在覆盖区资源靶区以预测中应用的实例。     

人工神经网络与分析测试技术的研究与发展

回顾了人工神经网络研究的发展历程，简要介绍了神经网络模型与算法，对分析测试技术和相关学科中的人工神经网络研究及在流程控制、错误诊断、参数估计、传感器模型、模式识别与分类、环境监测与治理及光谱与化学分析中的应用等作了评述。引用参考文献１１３篇。     

Neural dynamic modelling on earthquake magnitude series

Earthquake magnitude prediction is of vital importance for human safety. The earthquake is a very complicated and non-linear dynamic process. It cannot be described adequately by any deterministic models. In this paper a neural dynamic modelling for earthquake magnitude prediction is reported. Historical records of earthquake magnitude series are used to construct the optimal non-linear dynamic model, and the consequent outcome of the earthquake behaviour is then predicted by this model. In turn, the latest recorded data set can be fed back to improve the accuracy of the neural dynamic model. The modelling of experiments of three earthquake magnitude series in China and Japan and their extrapolated predictions are included in this paper. The values predicted by extrapolation are in good agreement with the historical data.     

吉林省安图县海沟金矿床控矿构造特征

海沟金矿是大型石英贫硫化物脉型金矿床。矿区侵入岩可划分为四个花岗岩单元，矿体（脉）在平、剖面上都具有斜列的特点。含金石英脉的产出和分布严格受ＮＮＥ－ＮＥ向断裂控制。金矿区控矿构造型式为ＥＷ向大断裂的派生断裂控制矿床，成矿构造序列划分为成矿前、成矿期、成矿后构造。构造对矿床（矿体）的控制作用主要表现为等距性构造控制矿脉分布；断裂多期活动对富矿体的控制。分析了断裂构造地球化学特征，表明成矿作用是以充填     

滑坡定量预测的非线性理论方法

滑坡时空预测是当前滑坡研究中的难题，特别是滑坡时间预测工作，其进展缓慢。本文基于非线性科学理论，分析了滑坡活动的分形特征及时间分形预测方法，研究了滑坡空间预测的人工神经网络特征，系统介绍了滑坡时间预测的非线性动力学理论。在此基础上，讨论了滑坡定量预测的发展趋势。     

不整合界限地层单位确立及划分的重要意义──兼论华北地层区不整合界限地层单位的划分和命名

在进行岩石地层划分时，有必要进行不整合界限地层单位的划分。不整合界限地层单位是顶底以不整合界面为限，而由有关联的若干个基本岩石地层单位──组（群）组成的组合。和岩石地层单位一样，是自然界存在的客观实体。初步建议华北地区划分为８个不整合界限地层单位：桑干河序列、五台山序列、滹沱河序列、秦幽序列、冀鲁序列、三晋序列、燕辽序列、河海序列。不整合界限地层单位的划分、确立，是进行地层格架分析、进行层序地层学研究的前提，有利于以岩石地层单位（即以不整合界限地层单位）编制小比例尺地质图。     

应力波与可滑移岩石界面间的相互作用研究

研究了应力波与用节理刚度系数描述的线性滑移岩石界面间的相互作用，给出了其透、反射波幅的一般表达式。通过应力波垂直人射界面时的具体分析，反映出岩石界面的节理刚度对应力波传播的重要影响，并揭示了岩石界面对应力波的高频滤波作用。     

SEDIMENT TRANSPORT MODEL OF A WATERSHED

ChinaisoneOfthecountrieswhichsufferfromthemostserioussoilerosionintheworld,andthehillyandgullyareasof~PlateauinthemiddlereachesOftheYellowRiverarethemostseriouserosionareainChina.Inthisarea,themainerosivefactorsarerainfallandrunoff.Sometimes,thesedimentyieldinasinglefloodeventcanbe40%oftheannualsedimentload.Therefore,itisveryimPOrtanttostudytheyieldandgraphOfSedimentOfsinglefindevent.Forthispn~,thispaperiswritten.AInongsedimentmodelsforsinglefloodevent,moreandmoreattentionwaspaidtOunitse…     

Regionalization of unit hydrograph parameters: 1. Comparison of regression analysis techniques

Hydrologic regionalization is a useful tool that allows for the transfer of hydrological information from gaged sites to ungaged sites. This study developed regional regression equations that relate the two parameters in Nash's IUH model to the basin characteristics for 42 major watersheds in Taiwan. In the process of developing the regional equations, different regression procedures including the conventional univariate regression, multivariate regression, and seemingly unrelated regression were used. Multivariate regression and seeming unrelated regression were applied because there exists a rather strong correlation between the Nash's IUH parameters. Furthermore, a validation study was conducted to examine the predictability of regional equations derived by different regression procedures. The study indicates that hydrologic regionalization involving several dependent variables should consider their correlations in the process of establishing the regional equations. The consideration of such correlation will enhance the predictability of resulting regional equations as compared with the ones from the conventional univariate regression procedure.     

Regionalization of unit hydrograph Parameters: 2. Uncertainty analysis

Hydrologic model parameters obtained from regional regression equations are subject to uncertainty. Consequently, hydrologic model outputs based on the stochastic parameters are random. This paper presents a systematic analysis of uncertainty associated with the two parameters, N and K, in Nash's IUH model from different regional regression equations. The uncertainty features associated with N and K are further incorporated to assess the uncertainty of the resulting IUH. Numerical results indicate that uncertainty of N and K from the regional regression equations are too significant to be ignored.