Reworking Intensity—A Key Factor Leading to the Formation of Superlarge Gold Deposits in Greenstone Belts and Metamorphosed Microclastic Rocks in China

The greenstone belt and metamorphosed microclastic rock-type superlarge gold deposits in China are hosted in metamorphic rocks and later intrusive bodies.Sedimentation.regional metamorphism and mineralization contributed a lot to the formation of the deposits,so did remelting magmatic process to some deposits,but the deposits were finally formed by reworking processes.The key factor leading to the formation of superlarge gold deposits is the reworking intensity,which for superlarge gold deposits is reflected by the large-scale reworked source rocks and even ore materials of various sources,strongly oxidized ore-forming fluids with a long and repeated active history and stable geothermal heat current.The factor which decides the reworking intensity is the network consisting of structures of different classes.     

参考作物腾发量预测的径向基函数法

探讨了采用径向基函数网络模型进行参考作物腾发量预测方法的可行性,设计多组数字实验处理研究了输入因子间相关性对网络模型预测准确性的影响,预测结果与Penman-Montieth方法计算结果比较表明,所确定的模型与改进的Penman公式计算值有很高的一致性,具有一定精度。     

武汉市供电局110联动报修计算机管理系统

介绍了武汉市供电局110联动报修计算机管理系统的设计和实现方法。系统利用ATM网络和PSTN网络,在SQLServer数据库环境中实现配电故障的信息处理。     

三层BP网隐层节点数确定方法的研究

对确定三层ＢＰ网络隐层节点数的理论依据和现有做法进行了研究,提出了一种三层ＢＰ网络隐层节点数的双向确定法     

Retrievals of rain-rate over oceans from ssm/ i data using som model

1.IntroductionOverthepasttwentyyears,anumberoftheoreticalandexperimentalworkhasbeencarriedoutontheretrievalsofrain--ratefromthespace--bornemicrowaveradiometers.Inrecentyears,manyalgorithmshavebeendevelopedtoretrieverain--ratefromtheSSM/Idata(Barrettetal.,1994;Ebertetal.,1996;Ferraroetal.,1995andsoon).However,becauseofthecomplicatednonlinearrelationshipbetweenbrightnesstemperatures(BTs)andrain--ratewhichiscausedbyvariousphysicalprocesses,theretrievalresultsbystatisticallychasedalgorithmsare…     

用神经网络方法确定测区似大地水准面

本文提出一种用神经网络确定测区似大地水准面的方法,并给出了相应的神经网络结构和算法。研究了一个测区中用该方法确定的似大地水准面的精度,结果表明用该方法确定的似大地水准面的精度为厘米级,并与二次多项式曲面拟合似大地水准面的方法作了比较,神经网络方法的精度较优。     

遥感图像分类与后处理综合技术研究—基于约束满足神经网络方法

遥感图像计算机分类的精度问题是阻碍计算机遥感信息处理系统实用化的一个关键问题。将分类后处理中的分类结果平滑过程模型化为约束优化问题,采用神经网络方法把分类结果平滑过程与遥感图像分类过程结合起来,提出了基于约束满足神经网络的遥感信息分类与后处理综合技术。实验表明该方法可明显提高森林类型划分、土地利用调查等遥感应用专题的分类精度。     

自组织网络在遥感土地覆盖分类中应用研究

设计完成和比较了自组织网络的几种算法在遥感土地覆盖分类中的应用,结果表明非监督和监督学习结合方法进行遥感土地覆盖分类,各算法在分类性能上无显著差异,因此可采用算法和较简单的单竞争学习网络,根据最邻近原则进行非参数分类。     

人工神经网络及其在地震研究中的应用

简述了人工神经网络的发展历史,详述了Ｂ－Ｐ神经网络的基本原理,介绍了其在地震研究中的应用,文后对神经网络研究中应注意的问题进行了讨论。．     

On the reoccurrence of site specific response

The period and amplitude variations of local peaks in the Fourier amplitude spectra of free-field strong ground motion recorded at five stations in San Fernando Valley of metropolitan Los Angeles, California, are described, searching for peaks that reoccur during different earthquakes. The data suggest that some local peaks reoccur (about 50% of the time), during shaking by small local earthquakes (peak ground velocities, v_max<10–20 cm/s). During large strong motion amplitudes (v_max>20 cm/s), these peaks are shifted towards longer periods (by nonlinear response of soils) or disappear. The data also suggest that densification and settlement of soil, minutes and hours following the strong shaking may contribute towards fluctuations in the effective stiffness of the shallow surface layers.