通辽CK－9井水位动态及影响因素的分析

本文在对通辽ＣＫ－９井、水文地质条件和水动态特征初步分析的基础上，采用ＰＣ－１５００计算机，对该井的观测数据与气压、蒸发、降雨、开采、河水位、固体潮、地震波和潜水位等相互关系，进行了定量的相互分析．研究所得结果，对该井水位动态变化和影响因素的关系，提出了新的结论．     

广州城市郊区化的进程及动力机制

随着城市经济社会的发展，我国一些大城市出现了郊区化的初步征兆，开展了郊区化研究有着重要的理论和实际两个方面的意义。广州是我国少数几个已开始进入城市郊区化阶段的大城市之一。本文分析该市郊区化的进程及动力机制，并提出调控对策。     

新疆强震活动与环境因子预报

本文运用新疆历史地震资料,分析了该地区的强震时空分布及地震活跃周期和活动图象,并探讨了强震活动与空间环境因子的关系,这无疑对新疆强震活动趋势分析和该地区强震中长期预报具有较重要的意义。     

阿希金矿地质特征及成因初探

阿希金矿产于下石炭统大哈拉军山组,第五岩性段构成的古火山机构西南缘。矿体产出形态及空间分布,严格受近南北向弧形张扭性断裂控制。主矿体呈厚大似板状体,以南北两个富集中心向NE50°倾角侧伏。矿石以石英脉型为主,其次为蚀变岩型。围岩为英安岩和角砾熔岩。属中一低温火山期后热液型金矿床。     

论地方地震科研工作的制约因素及改革对策

本文列据说明地方地震科技队伍是一支重要力量，但与专业地震科学队伍相比较，因制约因素多，存在很大差距，要发挥地方地震科学队伍的作用，必须走改革之路，在认识上、组织上、步调上应该协调统一，以达到专群，上下真正结合。     

宝鸡市地质灾害特征及形成主要因素分析

介绍了宝鸡市地质灾害的类型、规模及特征,并着重分析了宝鸡市地质灾害形成的主要因素。认为虽然造成区内地质灾害发生的因素甚多,但近年来大多与人为因素诱发有关。因此,在国土开发和国民经济建设中,应重视对地质环境的保护,避免因人类不适宜的活动而诱发地质灾害。     

对补偿式航空电磁法干扰因素的讨论

把补偿式航空电磁法测量中遇到的主要干扰因素按性质分为地质噪声、人文干扰和机械系统形变三类、并讨论了各类干扰异常的特点。     

An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

ABSTRACT

Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.     

Extensions to Spatial Factor Methods with an Illustration in Geochemistry

Many applications involving spatial data require several layers of information to be simultaneously analyzed in relation to underlying geography and topographic detail. This in turn generates a need for forms of multivariate analysis particularly oriented to spatial problems and designed to handle spatial structure and dependency both within and between spatially indexed multivariate responses. In this paper we focus on one group of such methods sometimes referred to as spatial factor analysis. Use of these techniques has so far been mostly restricted to applications in the geosciences and in some forms of image processing, but the methods have potential for wider use outside these fields. They are concerned with identifying components of a multivariate data set with a spatial covariance structure that predominantly acts over a particular spatial range or zone of influence. We review the various forms of spatial factor analysis that have been proposed and emphasize links between them and with the linear model of coregionalization employed in geostatistics. We then introduce extensions to such methods that may prove useful in exploratory spatial analysis, both generally and more specifically in the context of multivariate spatial prediction. Application of our proposed exploratory techniques is demonstrated on a small but illustrative geochemical data set involving multielement measurements from stream sediments.