全椒县范水洼金矿区控矿构造初步研究

研究区主要构造活动可分为3期：早期，发育以透入性的剪切劈理为特征的复式褶皱及倒转翼的逆冲剪切减薄拉断作用；中期，伸展作用；后期，脆性的挤压剪切破碎作用。通过详细地质构造剖面测制。建立了区内的主要构造格架：总体为一复式向斜一汪洼向斜，以杨柳岗组中上段为两翼，以琅琊山组为核，二级褶皱总体表现为向斜开阔，背斜紧闭，向南东倒伏的特征；二级褶皱的转折端为主要的控矿容矿构造，矿(化)体主要赋存于复式向斜的核部、背斜转折端。二级褶皱背斜转折端的拉张垮塌破碎角砾岩及翼部特别是近倒转翼的剪切破碎带是最重要的控矿容矿构造。     

不同边界条件对多年冻土上限影响的模型试验研究

在全球气候转暖对多年冻土影响的情景下，开展了在多模式下气温升高对冻土上限的影响试验研究。针对不加抛石遮阳棚、加抛石、加抛石遮阳棚3种情况，地表温度均采取基本温度加2次不同升温冻融循环，每次温控指标逐级提高0.5℃，即地表温度3次变化依次按照-16.1～5.8～-13.3℃、-15.6～6.3～-12.8℃、-15.1～6.8～-12.3℃进行，结果表明，加抛石及遮阳棚对多年冻土上限有着明显的影响，冻土上限的变化与气温基本上呈正相关，为寒区道路工程设计与施工提供了理论参考依据。     

基于人工神经网络的航磁信息关联方法的研究

应用人工神经网络方法 ,以航磁数据为样本 ,建立了航磁信息关联的BP人工神经网络模型。对山东省 1∶5 0万航磁水平一阶导数轴向图进行计算机自动关联叠置、提取航磁解释构造轴线的研究。经与垂向二阶导数对比分析 ,证实了该方法给出的模型具有较强的非线性、自适应和学习能力。     

油罐地基不均匀沉降原因分析及纠偏加固处理方法

某油罐因地基发生不均匀沉降而倾斜，本文通过对油罐发生均匀沉降的原因分析，提出了倾斜油罐纠偏设计及地基处理方案，叙述了地基处理及纠偏施工方法，施工处理后经过半年的使用观察，效果良好。     

云南个旧南部地区元素地球化学特征

在野外调研和大量样品采集分析的基础上， 运用数学地质中对应分析和因子分析两种方法对样品和变量进行分类划分和成因解释。首先，将样品和元素进行综合计算，按第一、二因子轴作出样品－元素因子平面图，在图上划分四个象限，按象限进行分类，探讨其样品－元素分类规律。其次，按象限计算因子载荷矩阵，分析各因子轴所代表的元素共生组成规律和成矿期次。根据上述研究认为第一因子轴（F1）所代表的Sn、Cu、Pb、Cu、Zn、Au、Ag等元素为主成矿元素组合和主成矿期次，而其它因子轴所代表的Mo、W、Bi、Be等元素为次成矿元素组合和次成矿期次。成矿作用是在区域构造应力驱动力的作用下，与花岗岩侵入有关。成矿溶液除来自深源以外，还与周围基性岩体（脉）和围岩密切相关，矿液实质是一种混合矿浆，在上升运移过程中，充填在有利的构造部位和有利 的地层层位中形成矿体。     

T—R旋回层序在东淮凹陷前梨园地区Es3^3亚段储层研究中的应用

依据对东濮凹陷前梨园地区测井、岩芯和高分辨率地震资料的综合研究，可将Es3^3亚段划分为三个湖进-湖退（T-R）旋回沉积层序，自底往上，Es3^3I层序发育辫状三角洲-半深湖沉积体系，Es3^3ⅡEs3^3Ⅲ层序则以扇 三角洲-半深湖、深湖-湖底扇沉积体系为主，由沉积层序的区域对比和沉积体系的空间展布，建立起了前梨园地区Es3^3亚段高分辨率等时地层格架，将储集岩层置于等时界面限定的沉积层序中进行划分和对比，可精确地解释砂体的成因及其横向变化规律。     

沮漳河现代沉积研究

通过对沮漳河 1994～ 1998年洪水期与枯水期沉积的连续观察研究 ,指出了洪水期与枯水期沉积物搬运的不同机制和过程 ,识别出了 16种岩石相及 6种组合类型 ,每种组合的水动力条件及沉积过程决定了其沉积方式、沉积序列、产出位置和微环境特点。据此 ,将沮漳河可划分为 4个沉积微相单元 :河道内砾石及砂坝 ,典型凸岸点坝 ,决口扇与广布的河漫滩。提出了曲流河两个相邻弯顶之间存在凹岸滩坝的可能性 ,探讨了曲流河“二元结构”的形成过程和加积机理     

Study on the relationship between geothermal exploitation and seismic activity in Xi’an region

Based on the analysis of the induced earthquakes in China and abroad, we get some ideas about earthquakes induced by pumping water out of a well or injecting water into a well. The induced earthquakes usually occur near the well, and they are generally small earthquakes. The earthquake sources are shallow, and they belong to the main shock-after shock type of earthquake or the swarm-type of earthquake. The magnitude and the quantity of the induced earthquakes obviously depend on the pressure and the quantity of water pumped or injected. These earthquakes happen as soon as pumping or injecting occurrence, or after ten or twenty days, they may occur at the time of injecting mud or injecting high pressure water when a well is being drilled, or at the time when the ground water is being normally exploited. A large quantity of hot water has been exploited since 1990 in Xi’an, and the quantity of water exploited has been increasing year by year, as a result the groundwater level has been dropping with the water pumped out and the water level is high in summer and low in winter. The earthquakes in Xi’an region belong to the solitary-type and they spread outside Xi’an city where the wells are concentrated but no earthquake happens. The seismic frequency and the energy released have no relation with the quantity of water exploitation or the water level in the well. It is considered that geothermal exploitation does not induce earthquakes in and around Xi’an because of its specially geological condition. Foundation item: Project sponsored by the Landslide Office of Shaanxi Province and Society of Disaster Reduction of Shaanxi Province.     

Neuro‐control of seismically excited steel structure through sensitivity evaluation scheme

The neuro‐controller training algorithm based on cost function is applied to a multi‐degree‐of‐freedom system; and a sensitivity evaluation algorithm replacing the emulator neural network is proposed. In conventional methods, the emulator neural network is used to evaluate the sensitivity of structural response to the control signal. To use the emulator, it should be trained to predict the dynamic response of the structure. Much of the time is usually spent on training of the emulator. In the proposed algorithm, however, it takes only one sampling time to obtain the sensitivity. Therefore, training time for the emulator is eliminated. As a result, only one neural network is used for the neuro‐control system. In the numerical example, the three‐storey building structure with linear and non‐linear stiffness is controlled by the trained neural network. The actuator dynamics and control time delay are considered in the simulation. Numerical examples show that the proposed control algorithm is valid in structural control. Copyright © 2001 John Wiley & Sons, Ltd.     

平凉“95．7．19”强对流风暴分析

分析具有代表性的平凉局地强对流风暴过程，揭示了平凉发生强对流天气独特的地形因素。