基于K-Means聚类的辽宁省主体功能区试划研究

在国家提出的新的主体功能区概念体系和划分思路前提下,基于辽宁省主体功能区划定要求的8个基本指标项定量综合评价基础数据,选择“典型的”优化开发区、重点开发区、农产品主产区和重点生态功能区,采用K-Means聚类方法,分别在不指定初始聚类中心、指定初始聚类中心、以及PCA分析（即主成份分析）基础上指定初始聚类中心3种方法,对于全省100个县市区做4类主体功能区试划分.分别将划分结果与全省最终采用的划分方案进行比较,结果表明采用在PCA分析基础上指定初始聚类中心技术方法的划分结果更接近最终采用方案.     

Windowed nearest neighbour method for mining spatio-temporal clusters in the presence of noise

In a spatio-temporal data set, identifying spatio-temporal clusters is difficult because of the coupling of time and space and the interference of noise. Previous methods employ either the window scanning technique or the spatio-temporal distance technique to identify spatio-temporal clusters. Although easily implemented, they suffer from the subjectivity in the choice of parameters for classification. In this article, we use the windowed kth nearest (WKN) distance (the geographic distance between an event and its kth geographical nearest neighbour among those events from which to the event the temporal distances are no larger than the half of a specified time window width [TWW]) to differentiate clusters from noise in spatio-temporal data. The windowed nearest neighbour (WNN) method is composed of four steps. The first is to construct a sequence of TWW factors, with which the WKN distances of events can be computed at different temporal scales. Second, the appropriate values of TWW (i.e. the appropriate temporal scales, at which the number of false positives may reach the lowest value when classifying the events) are indicated by the local maximum values of densities of identified clustered events, which are calculated over varying TWW by using the expectation-maximization algorithm. Third, the thresholds of the WKN distance for classification are then derived with the determined TWW. In the fourth step, clustered events identified at the determined TWW are connected into clusters according to their density connectivity in geographic–temporal space. Results of simulated data and a seismic case study showed that the WNN method is efficient in identifying spatio-temporal clusters. The novelty of WNN is that it can not only identify spatio-temporal clusters with arbitrary shapes and different spatio-temporal densities but also significantly reduce the subjectivity in the classification process.     

Clustering of temporal event processes

A temporal point process is a sequence of points, each representing the occurrence time of an event. Each temporal point process is related to the behavior of an entity. As a result, clustering of temporal point processes can help differentiate between entities, thereby revealing patterns of behaviors. This study proposes a hierarchical cluster method for clustering temporal point processes based on the discrete Fréchet (DF) distance. The DF cluster method is divided into four steps: (1) constructing a DF similarity matrix between temporal point processes; (2) constructing a complete linkage hierarchical tree based on the DF similarity matrix; (3) clustering the point processes with a threshold determined by locating the local maxima on the curve of the pseudo-F statistic (an index which measures the separability between clusters and the compactness in clusters); and (4) identifying inner patterns for each cluster formed by a series of dense intervals, each of which contains at least one event of all processes of the cluster. The contributions of the article are: (1) the proposed DF cluster method can cluster temporal point processes into different groups and (2) more importantly, it can identify the inner pattern of each cluster. Two synthetic data sets were created to illustrate the DF distance between temporal point process clusters (the first data set) and validate the proposed DF cluster method (the second data set), respectively. An experiment and a comparison with a method based on dynamic time warping show that DF cluster successfully identifies the preconfigured patterns in the second synthetic data set. The cluster method was then applied to a population migration history data set for the Northern Plains of the United States, revealing some interesting population migration patterns.     

浅谈芦山地震

地震序列是对地震现象的回顾性描述,只有在地震序列结束后它才有可能被确切无疑地判定.鉴于龙门山断裂带或地震带的整体性、统一性和系统性,不可否认在其西南段发生的芦山地震是与汶川地震一样的同属于龙门山断裂带的事件.鉴于芦山地震的震源位置、震源机制、震级大小和破裂区覆盖的范围,目前可认为它是汶川地震迄今最大的余震.鉴于地震序列的判定是对地震现象的唯象的描述,有相当的任意性,对芦山地震究竟是"新的主震"还是"汶川余震"的"讨论"实质上是在根据经验判定地震的类型,即使目前看"主震说"提出的4条"论据"明显缺乏说服力,"余震说"比较有说服力,但最后都需要等到地震序列结束之后才有可能"定论".作者认为,相对于需要等到地震序列结束之后才能"定论"、甚而在地震序列结束之后未必能"定论"的芦山地震究竟是"新的主震"还是"汶川余震"问题,应当更为关注芦山地震的发生引出的一些重要的亟待研究的科学问题与防震减灾问题.     

应用Bayes逐步判别分析识别辛176区块Es4储层岩性

辛176区块沙四段储层存在粗砂岩、不等粒砂岩和细砂岩,岩性非均质性较强.“四性”关系研究表明,岩性的准确识别是正确评价储层静态参数,识别油水层特别是低阻油层的前提.这里介绍了Bayes逐步判别方法原理和技术流程,在应用有序聚类分析方法开展测井曲线自动分层的基础上,综合应用自然电位(SP)、自然伽玛(GR)、声波时差(AC)、深探测电阻率(Rt)、浅探测电阻率(Rxo)测井资料和岩心分析资料,建立了粗砂岩、不等粒砂岩、细砂岩和泥岩的判别函数.应用效果表明,Bayes逐步判别法识别岩性符合率达到了86％,能够满足辛176区块沙四段储层岩性识别的需要.     

青海五道梁地区渐新统雅西错组地球化学特征及成矿作用浅析

以五道梁地区1∶20万水系沉积物测量更新数据为基础,以渐新统雅西错组为研究对象,利用地层汇水域水系沉积物测量元素原始数据进行了参数统计,通过相对丰度、R型聚类分析和因子分析,分析了地层中各元素的含量分布特征及元素间的亲疏关系,进一步浅析了地层在成矿中的作用.认为雅西错组高钙碳酸盐岩建造环境显著,Ba、Sr、Ag、As、Cd、Mo在地层中有不同程度的富集.地层有一定的含矿性,其中Ag趋向于与黏土岩伴随的硅质砂岩类中,其他在碳酸盐岩中.在后期岩浆热源作用下,主要成矿元素能进一步富集成异常或成矿.     

气象区域站数据全流程监控系统的设计和实现

气象区域站数据是防灾减灾服务开展的重要核心数据,实时性非常强,但由于传输环节多、监控不连续,之前主要依靠接收资料数量来反推传输情况,在业务实际应用中存在故障无法准确定位、监控精密度不细致、追查环节复杂等问题。该文通过引入工作流的方法,梳理区域站数据采集和应用的全工作流程,搭建了Zabbix集群监控系统,开展工作流程和监控脚本设计,从而实现对复杂故障和关键环节的监控,为区域站数据全流程监控提供一种解决方案。系统在业务运行中取得非常好的应用效果,使数据监控精准,数据传输和服务更加高效。     

空间U统计量法在遥感蚀变信息提取中的应用研究

鉴于传统蚀变异常信息提取方法的局限性,试图在二维光谱特征空间(即散点图)中研究遥感蚀变信息的提取方法.在二维散点图中,两个波段灰度值的联合分布体现了各向异性的特点,聚类常呈类似椭圆形分布.对散点图中点的频数使用空间U统计量法逐个获得地物聚类的椭圆参数,将每个椭圆内的点映射到遥感图像上,经过目视解译,最终获得蚀变异常信息...     

基于集群的影像并行OPTA算法研究

并行计算是提高海量遥感影像细化处理速度的有效途径.基于对OPTA细化算法的分析,深入研究OPTA细化并行算法中数据划分和数据通信优化等关键问题,提出一种适合集群并行处理的通信优化并行算法,实验证明该算法具有良好的加速比,适合集群计算环境下海量遥感影像的细化处理.     

地理信息产业园建设的研究与思考

依托地理信息产业同建设的实践,通过对产业网发展的驱动力分析,详细归纳出产业园建设模式,并充分论述了各种模式的主要特点,进而提出了产业网建设和发展的支撑体系,尤其是针对产业园的现状深刻反思了产业集群的新态势。