Discrimination of sedimentary facies by association analysis

The ability of association analysis to discriminate sedimentary facies was tested on Purdy's modal analyses of modern sediments of the Great Bahama Bank. Purdy's data set has served in the past as a standard reference for evaluating various multivariate classification algorithms. In order to adapt Purdy's data to association analysis, the percent abundance of the 12 constituents was converted to binary form by dichotomizing each variable on its mean value. The results obtained by association analysis are virtually identical to those obtained by Purdy and other authors. The same four main sedimentary facies were discriminated; 86% of the samples were identically classified (97% when misclassified borderline cases are counted as matches); the total partition variance of the classification is only negligibly greater (4%); and the grouping of the variables yielded the same four groups. The rank order of the three division-attributes responsible for the sample classification is fines, oolites, and corals. Association analysis has been employed by other authors to differentiate meaningful facies groups in studies of ancient reef carbonates, modern reef sediments, and heavy minerals in stream sediments. In all these studies, the results were found to be compatible with those obtained by using the continuous quantitative measurements, indicating that qualitative binary data may often be sufficient for the purpose of facies discrimination in many branches of geology and that association analysis is an effective method for this purpose.     

高斯投影公式的机助推导

随着GPS应用的普及,高斯投影在测量中出现的机会越来越多。许多文献上都给出了推导过程,这个推导过程的运算量是非常大的,而且计算精度难于提高。经过研究,一种易于编程的递推公式被发现,虽然这个公式在计算量上没有减少,但是可以较好地解决精度问题。     

构建基于GIS的房产管理

阐述了基于GIS房产管理的关键是建立统一的数据分类与编码标准,核心是建立房屋的空间地理属性信息、自然属性信息、社会人文属性信息的链接,基础是建立房产地理信息数据库。并提出了建立房产地理信息数据库的方法、设计思路和方式。     

Automatic classification of volcanic earthquakes by using Multi-Layered neural networks

The application of neural networks as classifiers of seismic events is described with the aim of developing an automatic system for the classification of explosion quakes at the Stromboli volcano. The architecture of the network that we trained to identify four different classes of shocks was a Multi-Layer Perceptron, using the Back Error Propagation algorithm. Five different approaches for representing the information embedded in the seismograms, both in the time and in the frequency domain, were considered, and the results compared. The direct use of the time series of the shocks was not satisfactory. The auto-correlation function worked well, but in some cases it was misleading. A better performance was obtained with a frequency domain representation. Finally, the use of the envelope function did not work well. Combining parameters such as the auto-correlation and envelope functions can improve one source of error, but it may introduce new ones. The performance obtained highlights the importance of the data attributes used for the training of the network. Topologies with eight neurons in a single hidden layer gave, on average, the best results among the considered neural network structures. The overall results provide a large number of events (89% with the best performance) correctly classified, indicating that this automatic technique is reliable, and encouraging further applications in the field of volcanic seismology.     

三种地统计学图像纹理用于遥感图像分类的比较

该文运用LANDSAT TM数据对三种地统计学纹理量测方法用于图像分类的性能进行了比较和分析。研究发现，基于绝对值变差函数的纹理具有更好的性能。经典的变差函数和基于方根的变差函数的性能依赖于提取纹理时所用的窗口大小。     

多右端非对称位移方程组的GMRES种子投影方法

考虑用GMRES方法求解多右端非对称位移方程组(A-σjI)x^(j)=b^(j)，1≤j≤p。基于Smith的求解多右端方程组的种子投影思想，提出了求解上述位移方程组的GMRES种子投影方法，利用种子方程组产生的Krylov子空间来求近似解。本文给出了近似解的误差界，最后数值结果显示了该方法的有效性。     

观赏石的分类与审美

从观赏石是自然形成的具有观赏价值的石质艺术品这一认识出发，将观赏石分为造型石、图纹石、矿物晶体石、古生物化石、纪念石和特殊石６大类。又以观赏石的地域审美观为指导，从色泽、形态、图纹、质感、组合、使巧、命名和意境８个方面对观赏石的审美进行了探讨。     

膨胀土判别与分类的灰关联分析法

基于灰色系统理论，给出一种用于膨胀土判别与分类的新方法—灰关联分析法。实际算例表明，该法具有简单方便、结果客观可靠等优点。     

生物礁岩分类方案

本文系统地提出了一个生物礁岩分类的新方案。生物礁岩首先根据次生组分的有无分为原生礁岩和次生礁岩。次生礁岩根据次生组分来源分为骨源次生礁岩和礁源次生礁岩。次生礁岩的二级分类根据次生组分的含量进行。骨源次生礁岩分为骨屑岩和含骨屑礁（灰）岩、倒骨央和含倒骨礁（灰）岩；礁源次生礁岩分为礁屑岩和含礁屑礁（灰）岩。骨屑岩根据次生组分的粒度分为巨骨砾（屑灰）岩、骨砾（屑灰）岩、细骨砾（屑灰）岩、骨砂（屑灰）岩；