Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Curve matching approaches to waveform classification: a case study using ICESat data

Light Detection and Ranging (LiDAR) waveforms are being increasingly used in many forest and urban applications, especially for ground feature classification. However, most studies relied on either discretizing waveforms to multiple returns or extracting shape metrics from waveforms. The direct use of the full waveform, which contains the most comprehensive and accurate information has been scarcely explored. We proposed to utilize the complete waveform to test its ability to differentiate between objects having distinct vertical structures using curve matching approaches. Two groups of curve matching approaches were developed by extending methods originally designed for pixel-based hyperspectral image classification and object-based high spatial image classification. The first group is based on measuring the curve similarity between an unknown waveform and a reference waveform, including curve root sum squared differential area (CRSSDA), curve angle mapper (CAM), and Kullback–Leibler (KL) divergence. The second group assesses the curve similarity between an unknown and reference cumulative distribution functions (CDFs) of their waveforms, including cumulative curve root sum squared differential area (CCRSSDA), cumulative curve angle mapper (CCAM), and Kolmogorov–Smirnov (KS) distance. When employed to classify open space, trees, and buildings using ICESat waveform data, KL provided the highest average classification accuracy (87%), closely followed by CCRSSDA and CCAM, and they all significantly outperformed KS, CRSSDA, and CAM based on 15 randomized sample sets.     

进积型三角洲交汇区沉积模式--以东营凹陷沙三中亚段为例

作为碎屑岩储层发育的最主要沉积相类型之一,进积型三角洲在油气勘探领域具有举足轻重的地位;加强对进积型三角洲沉积模式的研究,对于油气储层预测具有重要现实意义。渤海湾盆地东营凹陷古近纪湖盆内发育的东营三角洲和永安三角洲在沙河街组三段中亚段沉积期发生了交汇,而有关两个三角洲的交汇方式、沉积特征及交汇区储层的预测尚未引起足够的重视。以岩芯观测、录井和地震资料为基础,分析了东营凹陷古近系沙三段中亚段三角洲交汇区沉积特征,探讨了沉积期次、交汇过程,建立了三角洲交汇区沉积模式。研究表明,东营凹陷沙河街组三段中亚段主要发育湖泊、进积型三角洲沉积;储集砂体主要形成于三角洲前缘水下分流河道,河口坝微相次之,浊积岩主要分布于深湖区。认为此前界定的东营三角洲的分布范围可能被夸大,而永安三角洲的沉积规模可能被低估。东营凹陷古近系沙三中亚段可划分为9个期次,其作用过程可分为局部交汇阶段和完全交汇阶段。三角洲水下交汇区是水流汇聚和沉积物卸载的有利场所,叠置砂体可成为有利的油气储层,因而具有重要的油气勘探价值。     

基于光谱特征的月球岩性分类方法研究——以Apollo 16登月区域为例

利用NASA行星数据系统提供Apollo计划登月点采样线路影像数据,通过与嫦娥二号CCD数据、印度M~3数据空间校正获得采样路线坐标。开展嫦娥二号CCD数据与印度M~3数据MAP(后验概率)融合并选择Apollo 15、Apollo 16-62231的LSCC测得的标准岩石双向反射率光谱与M~3、嫦娥二号进行交叉定标。本文采用月球岩石光谱谱型全特征分析方法,选取涵盖Apollo计划登月获取的36个基站主要岩性87种、285件岩石样品,利用校正后的M~3数据分析月球典型岩石各阶吸收反射特征,建立月球典型岩石标准遥感影像光谱库,此后应用Apollo 623个岩石样品进行对比得到很好结果,同时完成Apollo 16登月点周围领域岩性分布图,并讨论了研究区的岩石成因,Apollo 16区域形成于高地大撞击,在早期的研究中已经被用于划分月球年代,本文方法对于月球岩石类别研究与理解月球的岩浆演化具有重要的研究价值。     

基于最优分割分级法的月球撞击坑分级及其演化分析

撞击坑是月球表面广泛分布的重要构造形态,占据了月球表面的大部分面积。撞击坑的直径差别很大,从几微米到数百千米,其退化程度与形成年代具有密切关系。为了研究不同地质年代形成的撞击坑直径大小及其演化规律,需采用量化分级方法对大小不同的撞击坑进行定量分级和统计分析。本文在月表撞击坑数据库LU60645GT和Lunar_Impact_Crater_Database(2011)的基础上,结合数据库中撞击坑的直径、深度和年代信息,利用最优分割分级法对撞击坑直径进行定量化分级,并根据分级结果,综合分析撞击坑几何形态特征及其演化规律。研究结果表明,撞击坑形态特征的演化与年代有密切的关系。在相同级别、相同地体下,撞击坑形成的年代越早,其形态特征的精细结构退化程度越明显,只保留了大体的几何形状;而在不同级别、相同地体、相同年代下的撞击坑形态特征则由简单逐渐变为复杂,坑物质也逐渐变得复杂。     

姚家岭锌金多金属矿床围岩蚀变三维空间定量分析研究

姚家岭锌金多金属矿床是近年来在长江中下游成矿带铜陵矿集区新发现的大型多金属矿床。姚家岭锌金多金属矿床的成矿作用具有多阶段性的特点,矿床范围内围岩蚀变强烈,蚀变类型复杂多样。三维地质信息技术及三维空间分析方法为定量化分析围岩蚀变的空间分布以及与矿化之间的相关关系提供了有利工具。本文基于上述方法,有效地对姚家岭锌金多金属矿床的围岩蚀变的分带性及其与不同矿化之间的关联性行了定量分析。较之于传统研究方法,不仅能快速地对蚀变与矿化的分布特征进行分析,还能从定量的角度获取不同信息之间的规律性和相关性。本文采用的相关方法不但有助于更准确的判定蚀变分带及规律,还可为矿床的成矿模式及找矿模型的建立提供定量化的数据支持。     

早古生代古亚洲洋俯冲记录:来自东天山卡拉塔格高镁安山岩的年代学、地球化学证据

吐哈盆地南缘荒草坡群大柳沟组火山岩出露于大草滩断裂以北的彩霞山、土屋铜矿北和卡拉塔格地区。该组火山岩主要为基性、中酸性熔岩及火山碎屑岩组合。LA-ICP MS锆石U-Pb分析获得安山岩和英安岩的年龄分别为434.8±3.8Ma和438.4±4.9Ma,表明大柳沟组火山岩形成于早志留世。安山岩Si O2含量为54.35%~60.70%;Al2O3含量为11.8%~16.3%,Mg O含量为4.94%~8.27%,Ti O2含量为0.38%~0.52%,Na2O和K2O的含量分别为0.50%~3.83%和0.08%~1.26%,铝饱和指数(A/CNK)为0.77~1.37,低ΣREE(43×10-6~70×10-6),为具有亏损源区特征的高镁安山岩。英安岩较安山岩Si O2含量高(63.74%~75.35%),具有相似的Al2O3(12.3%~16.0%%)和Ti O2(0.32%~0.52%)含量;Na2O和K2O含量分别为2.01%~5.53%和0.16%~2.19%,ΣREE含量较安山岩略高(61×10-6~84×10-6),铝饱和指数(A/CNK)为0.77~5.74。安山岩和英安岩均轻重稀土分异明显,具有中等的Eu负异常;富集大离子亲石元素(LILE),亏损高场强元素(HFSE),具有明显的Nb-Ta-Ti负异常。相似的岩石地球化学特征表明安山岩和英安岩可能为同源岩浆,英安岩是安山质岩浆经过角闪石等矿物结晶分异作用形成的,安山岩的母岩岩浆很可能来自亏损大洋岩石圈地幔。基于该组岩石组合、地球化学特征和区域地质资料,推测研究区高镁安山岩可能形成于洋内弧环境,这表明东天山吐哈盆地南缘卡拉塔格地区在早古生代已经存在古亚洲洋的俯冲。     

华北克拉通东部鲁西早白垩纪基性岩墙成因:来自锆石年龄、地球化学和Sr-Nd-Hf同位素的证据

中生代基性辉绿岩墙广泛分布于华北克拉通东部山东地区。本研究给出代表性岩墙的U-Pb锆石年龄、地球化学和Sr-Nd-Hf同位素证据,4个代表性锆石的LA-ICP-MS年龄范围处于121.9±0.6Ma和124.3±0.5Ma之间(早白垩纪)。岩石的主量元素组成变化较小,岩石富集轻稀土元素和大离子亲石元素(如,Rb、Ba、U、K和Pb),以及亏损高场强元素(如,Nb、Ta和Ti)。另外,基性岩墙具有相对一致的(⁸⁷Sr/⁸⁶Sr)_i比值(~0.7098),负的ε_Nd(t)值(-14.7~-14.5)、ε_Hf(t)值(-31.4~-26.7)和高的Hf模式年龄(t_DM1=1817~2024Ma)。研究显示基性岩墙来自富集岩石圈地幔的部分熔融作用,并在上升侵位过程中经历了一定程度的地壳混染作用影响。总体研究表明,基性岩墙的成因机制与扬子克拉通与华北克拉通的碰撞有关,岩浆源区为晚中生代前受俯冲扬子地壳沉积物交代后的富集岩石圈地幔。