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.     

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。     

Automated Kerogen Classification in Microscope Images of Dispersed Kerogen Preparation

We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naïve Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p<0.05) and indistinguishable from the other four classifiers. The initial set of 32 features was subsequently reduced to 6 features without compromising the classification accuracy. A further evaluation of the system alerted us to the possible sensitivity of the classification to the ground truth that might vary from one human expert to another. The analysis also revealed that the logistic classifier made most of the correct classifications with a high certainty.     

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.     

基于树结构的数字遥感图像存储方法与应用

数字遥感图像一般是采用栅格结构来存储。图像中地物的结构特征是隐含其中的。该文从图像识别角度，重点研究了基于树结构的数字遥感图像存储方法。这种方法与遥感中常用的四又树等存储方法不同，它注重图像中地物本身的结构特征。通过树结构使像元按地物目标形成一个个的组合体。该文以机场跑道识别为例，介绍了基于树结构的数字遥感图像存储方法的应用以及将图像变为树的算法。     

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。     

矿山工程裂隙破碎带的三维地震模式识别原理及应用效果

本文从时间域、频率域、三维空间域介绍了矿山工程裂隙破碎带主要的地震运动学、动力学属性参数,包括相对振幅、波峰相位时间、相似系数、主频带能量、三维空间时间梯度和相干系数等,提出了矿山工程裂隙破碎带三维地震属性的去噪平滑、归一化、相关分析、特征变换的处理方法以及裂隙破碎带的模式识别原理与方法,并用实例说明了该方法的应用效果。结果表明:模式识别法对于解释矿山工程的裂隙破碎带是有效的,并具有广阔的应用前景。     

关于地理信息系统学科的理论基础与体系框架

地理信息系统已成为一门独立的学科，关于它的理论基础与体系框架应该是一个值得研究的问题。从地理信息系统的本质看，它是传输地理环境信息的现代化工具，“空间认知”与“地理系统”作为地理信息系统的认识论和方法论，是地理信息系统的理论基础；地理信息系统理论、技术与应用构成了它的体系框架。     

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。