三维叠前时间偏移技术在江苏WZD地区的应用

当地下地质构造复杂、断裂发育,横向速度变化不是太剧烈的情况下,提高偏移成像精度,三维叠前时间偏移是一种行之有效的方法。三维叠前时间偏移取得成功的关键:一是高保真、高信噪比的叠前数据;二是建立一个合理的三维偏移速度场。随着计算机性能的不断提高,三维叠前时间偏移逐渐成为常规处理技术,该技术在江苏油田WZD地区得到了广泛的应用,实际资料处理结果及钻探效果证实了三维叠前时间偏移技术是解决WZD地区复杂构造成像精度的一种好方法。     

Heavy metal adsorption on the Le An River sediment —The adsorption model

In this study,the surface properties and adsorption properties of the Le An River sediment were modelled via surface complexation approach.The model parameters were determined based on the data of our potentiometric titration experiments and the metal adsorption experiments with the Le An River sediment samples.Consequently,the surface complexation models for the natural sediment,in our case the Le An River sediments,which can interpret the experimental data very well were successfully established.Three typical surface complexation models that is the constant capacitance model,the diffuse layer model and the triple layer model,were considered in this research.This work indicated that the consistency and the interdependency among model parameters together with the selection of the surface adsorbed species should be emphasized.     

Effect of domestic sewage on growth of phytoplankton-mathematical model

A mathematical model relating to the change in phytoplankton biomass in the period of growth and nutrient concentration in the media was proposed on the basis of the Monod equation and was testified by simulation experiments. Analysis of the experiment data showed that: the half-saturation constants for growth Kp (μmol/L) for Skeletonema, Chaetoceros and Prorocentrum were 5.52, 1.90 and 0.46, respectively; the balance between stimulation of nutrients and the inhibition of some other materials was found in the effect of domestic sewage on algal growth and the stimulation played a leading role; domestic sewage was more stimulative on dinoflag-ellate than on diatom and chlorophyte when the assemblage of the algae was cultured. The experiment suggested that mathematical model reasonably explained the characteristics of phytoplankton growth in different nutrient conditions and was worthy to be further improved for eutrophication prediction in off-shore water.     

中国农业植被净初级生产力模拟(I)——模型的建立与灵敏度分析

以业已建成的稻麦作物净初级生产力模型为基本框架,建立了一个具有普适性的中国农业植被净初级生产力模型(CropC-)。CropC-的模拟对象为占我国农作物总播种面积2/3的水稻、小麦、玉米、棉花、油菜和大豆。该模型包括2个主要功能模块:光合作用和呼吸作用;土壤-作物系统氮素运移。前者综合考虑了环境因子和氮素的影响,后者包括了作物氮素吸收、土壤氮矿化和化肥氮释放。灵敏度分析表明,在输入参数变化±10%时,CropC-对6个主要输入参数响应的敏感性依次为温度>光合有效辐射>大气CO2浓度>土壤全氮含量>施氮量>降水。模型分析表明,气候变暖将降低作物净初级生产力。     

工业废水的生化耗氧过程Logistic动力学研究

在Monod方程的基础上推导了适用于描述工业废水的生化耗氧过程Logistic动力学模型,比较了Logistic动力学模型和一级动力学模型的区别。测定了废水的BOD5/CODcr,在此基础上对可生化废水的生化耗氧曲线进行分析,利用Logistic动力学模型对生化耗氧过程进行动力学研究,并比较各种不同稀释倍数时的kLog和S0。研究结果表明:随着稀释比的增大,耗氧过程的动力学参数kLog和计量系数S0增大,且S0和废水的BOD5非常接近。     

青藏高原生态资产地域划分中的SOFM网络技术

针对目前地域划分中存在的问题,论文尝试以人工神经网络技术作为区划工作的理论支撑,构建了自组织特征映射SOFM网络,以青藏高原环境与生态系统资产作为待分客体,探索了新技术和方法在生态资产地域划分中的应用。结果表明,对于自然界中广泛存在的非线性问题,SOFM网络具有比聚类分析等线性分类器更强的适应性。应用SOFM网络在对待分客体生态资产进行类型划分的基础上,使用策略性循环尺度转换(SCS)范式对其进行了区域转换,最终完成了青藏高原范围内生态资产的地域划分。     

用神经网络辨识化学强化一级处理系统

运用实验室得到的数据为样本 ,选取了CODCr和絮凝剂投加量为指标 ,采用三层结构的神经网络 ,利用Matlab的神经网络工具箱中的批处理梯度下降法对CEPT系统经行辨识。辨识结果表明 :模型的预测值与实测值的平均误差在 3 .7%左右 ,具有较高的精度。同时 ,该模型还具有适应性强 ,使用方便 ,高效的特点 ,为CEPT系统的在线实时控制提供了一条有效的途径。     

长江常熟段排污口布置方案数值模拟论证研究

为合理确定常熟氟化工项目排污口布置方案 ,本文在分析长江常热段水环境特征的基础上 ,针对其复杂的水下地形和水流流场 ,建立了较为准确和实用的平面二维水流水质耦合数学模型 ,预测计算了三套方案污水对重点保护水域影响程度和范围 ,并从水流流场、水质影响评价、技术经济多角度对方案进行了比较论证 ,最终推荐污水江中排放的方案 ,为项目的工程设计和环境管理提供了科学依据。     

吉林省的风能潜力(英文)

近两年我国电力突然短缺,已经引起政府的高度重视。由于我国风电发展的紧迫性和风电发展的潜力,特别是国际上正在如火如荼地开发风电的情景,中国- 奥地利政府间双边合作计划将风电评估列入2001～2003 年度合作计划,论文是该计划的成果之一。以我国吉林省为研究对象,采用广泛使用的风机选址算法和空间分布诊断程序( WAsP和ZAWIMOD2),运用数字地形图和遥感资料获得的土地利用图,进而得到数字化的表面粗糙度,再对吉林省的风能潜力进行估算。完成了近1km² 分辨率的距地面60m高度的风速和风能密度计算,并制作了它们的分布图。这是我国第一次在较大区域上估算距地面60m 高度的风速和风能密度,其方法对我国风电资源的评估有重要的借鉴作用。结果显示最有效的风能资源在吉林省的中西部地区,而南部和东部的高风资源区位于山脊和山顶,对风能的开发有一定的限制。     

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.