八庙—青山金红石矿床化学成分与成矿关系

本矿床是我国金红石矿床重要的成因及工业类型，为具有特定化学成分的岩石在特定变质条件下形成的。本文通过对大量数据的处理，查明了化学成分与成矿的关系：高Ａｌ２Ｏ３、ＭｇＯ、Ｆｅ２Ｏ３＋ＦｅＯ、Ｋ２Ｏ、Ｐ２Ｏ５、Ｈ２Ｏ＋、ＭｎＯ，低ＳｉＯ２、ＣａＯ、Ｎａ２Ｏ、ＣＯ２、Ｓ等，利于钛富集；高ＴｉＯ２、ＭｇＯ、Ｐ２Ｏ５、Ｓ，低ＳｉＯ２、ＣａＯ、Ｋ２Ｏ、Ａｌ２Ｏ３、Ｆｅ２Ｏ３／ＦｅＯ，利于金红石形成；高Ｓｃ、Ｚｒ、Ｎｉ、Ｚｎ及Ｌａ／Ｙｂ，低Ｙ、Ｎｂ、Ｔｈ、Ｃｄ、Ｂａ、Ｂｅ、ΣＲＥＥ，利于金红石纯度提高。对研究矿床成因及寻找新的同类矿床等有重要意义     

案例推理的地学应用背景和方法

案例推理 (CBR)研究 ,是人类认知的一种合理性模型 ,接近于人类认识、解决问题最原始的思维方式 ,具有在无法获取机理模型、确定规则或统计模型时 ,采用简单的历史相似性推理实现问题的定量求解和预测 ,成为人工智能领域近年来的研究热点。     

Radiative association in Li+H revisited: the role of quasi-bound states

The role in radiative association of narrow resonances arising from quasi-bound levels is examined. For the rate coefficient the contribution from such levels is readily calculated in terms of the Einstein A -coefficient from bound rovibrational levels generated using a suitable alternative boundary condition. By locating systematically all the quasi-bound levels, an enhancement by more than an order of magnitude of the rate coefficient for Li(2s) + H(1s) association above about 600 K is obtained. The effect on the LiH abundance in the early Universe is no more than an increase of a factor of 3 for limited ranges of values of the redshift. For the Li(2p) state the effect of narrow resonances appears to have been taken into account in previous calculations.     

区域GPS网实时计算可降水量的若干问题

目前地基GPS气象学测得的可降水量 (PWV )精度好于 2mm ,但在利用区域GPS网实时计算每个测站上空的PWV时 ,要涉及到很多常规GPS资料处理时所忽略的问题 ,如需考虑数据处理软件和计算方式的选择、站坐标的确定和约束、轨道的使用方法、网外辅助站最佳数量的确定、海潮对实时计算PWV的影响以及实时应用于气象服务时的端部效应等问题。利用上海GPS综合应用网获取的 2 0 0 2年 6、7月份长江三角洲地区入梅前后的数据 ,分析了利用区域性的GPS网实时计算高精度的PWV时要解决的各种问题 ,探讨了其数据处理方案     

Automated Separation of Stars and Normal Galaxies Based on Statistical Mixture Modeling with RBF Neural Networks

For LAMOST, the largest sky survey program in China, the solution of the problem of automatic discrimination of stars from galaxies by spectra has shown that the results of the PSF test can be significantly refined. However, the problem is made worse when the redshifts of galaxies are not available. We present a new automatic method of star/(normal) galaxy separation, which is based on Statistical Mixture Modeling with Radial Basis Function Neural Networks (SMM-RBFNN). This work is a continuation of our previous one, where active and non-active celestial objects were successfully segregated. By combining the method in this paper and the previous one, stars can now be effectively separated from galaxies and AGNs by their spectra-a major goal of LAMOST, and an indispensable step in any automatic spectrum classification system. In our work, the training set includes standard stellar spectra from Jacoby's spectrum library and simulated galaxy spectra of EO, SO, Sa, Sb types with redshift ranging from 0 to 1