基于PSR-WSVM模型的边坡位移预测

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。     

RECENT TECTONIC DEFORMATION ANOMALY AND EARTHQUAKES IN GANSU-NINGXIA-QINGHAI AREA

1　IntroductionAccordingtogeologicalstructure ,theGansu Ningxia Qinghaiareabelongstothenortheastmar ginofQinghai Tibetblock .Thisareahasbeenpaidmuchattentionby geo specialistsinChinaandabroadbecauseofitssignificanttectonicmovement,itsintensiveseismicity ,anditsimportanceinearth quakehazardmitigation .IntheDevelopmentPro gramonNationalKeyBasicResearchesundertheProject“MechanismandPredictionofContinentalStrongEarthquakes”,themechanismsofcontinen talstrongearthquakesarestudied ,usinghypo…     

一种从经纬度网格资料获得正方形网格资料的插值方案

本文提出了一种把经纬度网格点上的气象要素值转化到正方形网格点上的多项式插值方案。对插值方案作了稳定性讨论和定量及定性分析。结果表明,这种插值方案方便可行,而且有广泛适用性。     

昆仑山口大地震与地形变异常的讨论

针对昆仑山口大地震，总结了多种地形变（大地测量）手段所显示的异常变化及其时空分布，结果显示：8．1级大震前存在空间尺度大，时间尺度的地形变前兆异常，简要介绍了相关的异常图像，给出了初步解释，并对未来震情的发展进行了探讨，认为近期内强震活动向华北迁移的可能性不大。     

形变大地测量学的进展、问题与地震预报

简要概括了形变大地测量学的革命性进展，研讨了它的科学特色、功能和定义以及对地球科学和防灾减灾的推动。形变大地测量学有助于从根本上击破多年来制约地震预报的“瓶颈”，但也存在不少急待解决的问题。着重研讨了在21世纪前10年，形变大地测量学如何依托多年的学科积累并充分受益于人造卫星和数字化等新技术，开展创新性研究和试验以推进地震预报。为此，对当前的研究工作提出了12条科学技术途径。最后对学科名称提出了建议。     

A MESO-β SCALE SIMULATION OF THE EFFECTS OF BOREAL FOREST ECOSYSTEM ON THE LOWER ATMOSPHERE

Based on the Intensive Field Campaign(IFC-1)data of Boreal Ecosystem-Atmosphere Study(BOREAS).a three-dimensional meso-β scale model is used to simulate the effect of boreal forests onthe lower atmosphere.A fine horizontal resolution of 2 km×2 km is used in order to distinguish thevegetative heterogeneity in the boreal region.A total of 20×25 grid points cover the entire sub-modeling area in BOREAS' South Study Area(SSA).The ecosystem types and their coverage ineach grid square are extracted from the North American Land Cover Characteristics Data Base(NALCCD)generated by the U.S.Geographical Survey(USGS)and the University of Nebraska-Lincoln(UNL).The topography of the study area is taken from the Digital Elevation Map(DEM)of USGS.The model outputs include the components of the energy balance budget within the canopyand at the ground.the turbulence parameters in the atmospheric boundary layer and the wind.temperature and humidity profiles extending up to a height of 1500 m.In addition to the fine timeand spatial step,the unique feature of the present model is the incorporation of both dynamic andbiological effects of the Boreal forest into the model parameterization scheme.The model resultscompare favorably with BOREAS' IFC-1 data in 1994 when the forest was in the luxuriant growingperiod.     

流褶层及韧变带研究

流褶层与韧变带是地壳拉伸变形，顺层固态流变作用下的产物。流褶层是以原始层理为变形面或再经递进变形的褶皱变形岩层或岩石共生组合层位。韧变带具明显的层控性，受岩石成分和应变程度控制，不同环境和不同成分岩石的韧变带具有相异的组合型式和变形机制，井具有一定的递变规律。流褶层和韧变带可分属不同层位，但流褶层可实现向韧变带的转化。     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

石英砂扫描电镜在漓江“泥砾”成因中的应用

本文根据扫描电镜对从漓江河谷钻孔中的泥砾层取得的481粒石英砂表面形态特证研究结果:石英砂多数颗粒保持棱角状外形并遭受不同程度的磨蚀,具阶状贝壳断口,平直或弧形刮削面和多向擦痕,沿解理面破碎呈“V”字形沟和撞击碟形坑,以及微溶蚀孔、隙、蜂窝状溶孔群和SiO_2再沉积等表面形态结构特征。认为它是在第四纪古地理湿热气候条件下泥石流沉积的产物,物源可能来自境内或周边的泥盆系砂岩和境外的花岗岩。     

成都市冬季重污染日气象条件成因分析及预报

本文通过对成都10个重污染日进行天气学分析,将污染浓度与气象要素进行聚类、研究了重污染日形成原因及污染浓度与气象要素的关系。在此基础上,建立了SO_2、TSP日平均浓度分级预报方程。