ENSO动力学与预测

发生在热带太平洋地区的ENSO现象是海气相互作用的集中表现,是年际气候变化中的最强信号.由于它的发生会在全球许多地区引起严重的气候异常,极大地影响着这些地区的工农业生产和人民生活,因此,对ENSO的机理及其预测的研究一直是大气海洋界的一个热点研究课题.中国科学院大气物理研究所在ENSO的机理及其预测等方面进行了大量的研究,取得了许多研究成果.作者将对中国科学院大气物理研究所在这方面的一些研究成果进行回顾和介绍.     

沿岸上升流和沿岸急流的一个半解析理论

在考虑了陆架地形后，在垂直海岸的x z剖面上 ，对Boussinesq流体的非线性海洋运动方程求得了总动量守恒、温度守恒和位势涡度守恒的 普 适形式，进而得到流函数所满足的椭圆型二阶偏微分方程，在给定流体沿地形运动的条件下 ，算出问题的解. 计算结果表明，沿岸可以出现上升流也可以出现下沉流，它依赖于海洋的 大尺度背景条件. 计算所得的上升流、沿岸急流、温度的锋区结构与一些观测事实接近.     

从碱湖沉积和孢粉组合看内蒙古伊盟地区近5ka以来的古植被古气候变迁

内蒙古伊盟地区哈马尔太碱湖沉积特征和孢粉组合特征反映了该区近５ｋａ以来的古植被古气候变迁。研究表明，此期间该区植被由阔叶、灌木草原变为疏树灌木草原，最后变为荒漠草原。在这５ｋａ中存在两次暖期两次冷期，近１．６ｋａ以来以冷干为主。湿润气候只存在于４．５～５．５ｋａＢ．Ｐ．，气候总的趋势是由较湿润变干旱直至干燥。     

1996年6月1日甘肃天祝MS5.4地震的震源机制

１９９６年６月１日甘肃天祝ＭＳ５．４地震的震源机制董积平（国家地震局兰州地震研究所，兰州７３００００）主题词：甘肃震源机制解天祝１９９６年６月１日２２时４９分，在甘肃省天祝县发生了ＭＳ５．４地震。经甘肃省地震监测台网测定，该次地震的震中位置为北纬３７...     

人工神经网络在岩体质量分级中的应用

结合四川省金沙江某水电站工程实例，应用BP人工神经网络方法建立3层BP网络模型，选取岩石单轴抗压强度等6个影响因素为输入变量，对坝基复杂岩体进行质量分级。通过机算机Visual C 语言编程实现神经网络模型，进行网络的学习和运算。以神经网络合理结构分析方法选取合理结构，确定合理隐层单元的数量，提高网络测试的精度。对测试结果的分析发现，经过优化的BP网络模型经多次学习后，测试精度提高，结果可靠，取得较好的实际应用效果。     

承压水减压引起的沉降分析

由下卧承压水层减压引起的固结沉降计算与一般的由堆载或潜水位下降引起的固结沉降计算不同,本文着重研究了在深厚弱透水层下卧强透水承压层的复杂地质条件下,下卧承压水层减压引起的土中应力变化及周围地表沉降的计算方法;在假定一维竖向固结的条件下推导了减压引起的沉降固结度计算公式,该公式与常规的双向排水固结公式相同,表明排水减压固结与常规的双向排水固结有着相同的效果.最后,将此分析方法应用于潮州供水枢纽工程西溪水闸沉降分析中,实践的结果表明,该方法简单可行,能较好地满足工程要求.     

顾及位置关系的网络POI地址信息标准化处理方法

针对互联网POI(兴趣点)地址信息中广泛存在的地址要素不完整、文字表达不一致等不规范现象,提出一种顾及位置关系的网络POI地址信息标准化处理方法,首先对POI信息进行切分提取并逐层匹配地址树模型;然后基于4种位置关系从标准POI库中选出相应集合,作为丰富和修正非标准POI地址要素的候选;最后通过最小粒度地址要素的回溯,实现POI地址信息的快速标准化处理。试验表明该方法可以获得较高的准确率,尤其适用于在互联网数据环境中的POI地址信息标准化。     

河南嵩县松里沟金矿床中碲化物的发现及其地质意义

松里沟金矿位于华北陆块南缘熊耳山地区,已探明金资源储量26 t。矿体产于中新太古界-古元古界太华群片麻岩NWW向的断裂带中。其热液成矿过程包括4个成矿阶段:黄铁矿-石英阶段、石英-黄铁矿阶段、金-碲化物阶段和石英-碳酸盐阶段。显微镜下发现金-碲化物阶段存在大量与金共生的碲化物。利用电子探针和能谱分析查明碲化物的种类、共生关系和形成条件,确认的碲化物有碲铅铋矿、碲铅矿、碲铋矿、碲金矿、碲金银矿、硫碲铋铅金矿、辉碲铋矿,此外还有大量的自然金和少量的辉铋矿。该矿床为一与岩浆作用有关的碲金矿床。Au主要以自然金和金银碲化物的形式存在。Au、Ag以硫氢络合物的形式发生迁移,Te2(g)和H2Te(g)冷凝进入含贵金属的氯化物溶液是碲化物沉淀主要机制。相图及化学反应方程式分析表明,金-碲化物阶段受温度、碲逸度、硫逸度、氧逸度和酸碱度控制,其中,黄铁矿-石英阶段和石英-黄铁矿阶段形成于logf_(Te2)-14.4和logf_(S2)=-11.1~-6.5的环境。金-碲化物阶段形成于温度为110~313℃、logf_(Te2)=-15.2~-9.4和logf_(S2)=-16.5~-14.6、f_(O_2)升高和pH值降低的环境。碲化物的发现为探讨该矿床成因和熊耳山地区寻找同类型的矿床提供了依据。     

Bias estimation and correction for triangle-based surface area calculations

The calculation of surface area is meaningful for a variety of space-filling phenomena, e.g., the packing of plants or animals within an area of land. With Digital Elevation Model (DEM) data we can calculate the surface area by using a continuous surface model, such as by the Triangulated Irregular Network (TIN). However, just as the triangle-based surface area discussed in this paper, the surface area is generally biased because it is a nonlinear mapping about the DEM data which contain measurement errors. To reduce the bias in the surface area, we propose a second-order bias correction by applying nonlinear error propagation to the triangle-based surface area. This process reveals that the random errors in the DEM data result in a bias in the triangle-based surface area while the systematic errors in the DEM data can be reduced by using the height differences. The bias is theoretically given by a probability integral which can be approximated by numerical approaches including the numerical integral and the Monte Carlo method; but these approaches need a theoretical distribution assumption about the DEM measurement errors, and have a very high computational cost. In most cases, we only have variance information on the measurement errors; thus, a bias estimation based on nonlinear error propagation is proposed. Based on the second-order bias estimation proposed, the variance of the surface area can be improved immediately by removing the bias from the original variance estimation. The main results are verified by the Monte Carlo method and by the numerical integral. They show that an unbiased surface area can be obtained by removing the proposed bias estimation from the triangle-based surface area originally calculated from the DEM data.     

Characterization of shale matrix pore structure via experiment and model

Shale gas reservoirs develop multi-scale pores ranging in size from nanometer to micrometer, the characteristics of gas transport involve the multi-scale pore space which divided into organic and inorganic matrix pores. This paper reveals the shale pore structure with large amounts of organic mesoporous based on the techniques of focused ion beam scanning electron microscopes (FIB-SEM), high-pressure mercury intrusion (MICP), and low-pressure adsorption (LPA), which also shows the size and distribution of these pores. Then the research characterizes effective pore scale via circular tube bundle model with due regard for gas adsorption layer thickness on the walls of organic pores and water film thickness on the walls of inorganic pores, and the investigation of shale pore geometry is significant for designing and developing shale gas reservoirs. This work shows that the widely existing shale mesoporous volume with diameter of 2~50 nm accounts for 81% based on experimental testing, then it reduces to about 76% via effective diameter model calculation.