The Barth Island layered structure is an oval, 6 by 9 km body, consisting of rhythmically layered adamellitic rock in the center which grades outward through jotunite into troctolite. Farther outward the sequence repeats itself in reversed order, strongly reduced in magnitude and finer grained; the adamellitic zone is followed by jotunite which grades into coarse-grained leuconorite and into anorthosite of the Nain complex. The Barth Island structure, having an inverted conical base topped by a hemispherical depression, seems to represent a distorted sequence of rock layers with troctolite at the bottom, grading upward into adamellitic rocks which grade into anorthosite at the top. Trend-surface analysis demonstrates the regional variation of plagioclase and orthopyroxene compositions in the troctolite—adamellite sequence of the central part of the structure. The fits for the second- and third-degree surfaces are good and significant at the 99 percent level. The regression line for compositional variation in coexisting plagioclase and orthopyroxene in all analyzed rocks has a correlation coefficient of r2 = 0.78. The difference between the trends in the troctolite—adamellite sequence and the anorthosite—adamellite sequence is insignificant. The regression curve for compositional variation in coexisting orthopyroxene and olivine has a correlation coefficient of r2 = 0.98. The curve shows good correlation with the experimentally established partitioning curve of Medaris, which indicates that equilibrium conditions prevailed during formation of the olivine—ortho-pyroxene pairs. The results suggest that the troctolite—adamellite sequence and the anorthosite—adamellite sequence are products of fractional crystallization, possibly from the same parental magma.相似文献
极光卵极光强度的空间分布是太阳风-磁层-电离层能量耦合过程的重要表现,并且随着空间环境参数和地磁指数的变化而变化,是空间天气的重要指示器.建立合适的极光强度模型对亚暴的预测以及磁层动力学的研究具有重要意义.本文基于Polar卫星的紫外极光成像仪(Ultraviolet Imager,UVI)数据,采用两种不同的极光强度表征方法,即曲线拟合方法(从UVI图像数据中提取极光强度沿磁余纬方向上的曲线特征,Curve Feature along the Magnetic Co-latitude Direction of the Auroral Intensity,CFMCD_AI)和网格化方法(从UVI图像数据中提取极光强度的网格化特征,Gridding Feature of the Auroral Intensity,GF_AI),来构造极区极光强度特征数据库.然后,利用该数据库,采用广义回归神经网络(Generalized Regression Neural Network,GRNN)构建了以行星际/太阳风参数(行星际磁场三分量、太阳风速度和密度)和地磁指数(AE指数)为输入参数的两种极光强度预测模型(GRNN_CFMCD_AI模型和GRNN_GF_AI模型).利用图像质量评价指数结构相似度(structure similarity,SSIM)作为极光强度模型预测结果和对应的UVI图像的相似性评价标准(完全相似为1,不相似为0,一般认为SSIM大于0.5是具有较好的相似性),对两种极光强度模型进行了性能评价.结果显示,GRNN_GF_AI模型预测结果对应的SSIM值范围为0.36~0.77,均值为0.54,性能优于GRNN_CFMCD_AI模型的.