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。     

基于噪声谱比法的内蒙古西部地区台站场地响应研究

利用内蒙古西部12个台站的地脉动噪声数据，采用噪声谱比法研究台站的场地响应情况。分析表明，台站场地响应按曲线形态可分为3类，且可能受地形地貌、局部构造和台基状况等条件影响。对比分析噪声谱比法与Moya方法的场地响应结果发现，场地响应曲线形态基本一致，只有极少数台站存在明显差异。结果表明，内蒙古西部区域大部分台站的台基状况总体较好，场地响应曲线较为平坦，无明显频率放大点。     

用实测波浪数据确定JONSWAP谱参数

本文给出应用实测波浪数据确定JONSWAP谱参数的方法，若Hs,Tz,Tc分别为实的有效波高，平均上跨零线周期，平均峰－峰周期并令y=Tz/Tc，本文导出求解谱参数γ值的方程f（γ，y）＝0。根据这个方程的解并应用最小二乘法，γ作为y的函数的表达式可写为（对于2.54≤γ≤15.34及1.6≤y≤1.79）。     

Bacterial productivity in the Prydz Bay and its adjacent waters,Antarctic

1　IntroductionBacteriaandtheiractivitiesplayanimportantroleintheelementalbiogeochemicalcyclesandenergytransformingintheocean (Zhenetal.1 997) .DortchandPackard(1 989) proposedthatfoodwebsintheeutrophicwatersaredominatedbythebiomassofprimaryproducerswhilefoodwebsintheoligotrophicwatersaredominatedbythebiomassofmicrobes.Heterotrophicbacteriahadbeenshowntoplayanimportantroleinthedecompositionoflarge ,rapidlysinkingorganicparticleswithinandbelowtheeuphot iczone ,andfurthertoaffecttheelementaldyn…     

观驾山—黑岛一带信息量法找金研究

本文以成矿地质模型为依据，对观驾山—黑山岛一带的化探、物探、遥感及地质等信息变量进行了详细的分析，选取了１０个信息变量２４个标志状态；在此基础上，运用信息量法对研究区内４５８个单元的信息量值进行计算，并由此确定了研究范围内的成矿远景区。事实表明，研究区信息量成矿预测是非常有效的     

Contrast between the Climatic States of the Warm Pool in the Indian Ocean and in the Pacific Ocean

Based on the analysis of Levitus data, the climatic states of the warm pool in the Indian Ocean (WPIO) and in the Pacific Ocean (WPPO) are studied. It is found that WPIO has a relatively smaller area, a shallower bottom and a slightly lower seawater temperature than those of WPPO. The horizontal area at different depths, volumes, central positions, and bottom depths of both WPIO and WPPO show quite apparent signals of seasonal variation. The maximum amplitude of WPIO surface area‘s seasonal variation is 58% larger over the annual mean value. WPIO‘s maximum volume variation amplitude is 66% larger over the annual mean value. The maximum variation amplitudes of the surface area and volume of WPPO are 20.9% and 20.6% larger over the annual mean value respectively. WPIO and WPPO show different temporal and spatial characteristics mainly due to the different wind fields and restriction of ocean basin geometry. For instance, seasonal northern displacement of WPIO is, to some extent, constrained by the basin of the Indian Ocean, while WPPO moves relatively freely in the longitudinal direction. The influence of WPIO and WPPO over the atmospheric motion must be quite different.