遥感数据融合的进展与前瞻

数据融合是提升遥感影像应用能力的重要手段,一直是遥感信息处理与应用领域的研究热点。本文系统综述了遥感数据融合的进展与前瞻:首先对数据融合的层次与分类进行了总结和归纳,将遥感数据融合划分为同质遥感数据融合、异质遥感数据融合、遥感—站点数据融合、遥感—非观测数据融合4大类;在此基础上,重点针对时—空—谱光学遥感数据的融合,从多视超分辨率融合、多尺度融合、空—谱融合、时—空融合、时—空—谱一体化融合等方面进行了详细阐述;最后总结了遥感数据融合的前瞻研究方向,包括时—空—谱一体化融合的拓展、空天地观测数据的跨尺度融合、传感网环境下的在线融合、面向应用的融合方法等。     

Landsat 8热红外数据定标参数的变化及其对地表温度反演的影响

Landsat系列卫星上的TIRS热红外传感器数据已被大量应用,针对TIRS数据的地表温度反演也相继开发出一些算法,并有一些研究对TIRS数据的定标及其地表温度反演算法的精度进行了对比。本文主要就TIRS热红外传感器定标参数的变化,结合这些定标参数变化的时间点对有关地表温度反演算法的适用性和有效性进行分析,特别是对劈窗算法是否适合当前的TIRS数据进行了讨论,以使用户能够对Landsat 8 TIRS热红外数据的正确使用有进一步的认识。总的看来,由于视域外杂散光的影响,TIRS数据的定标精度仍达不到设计目标,TIRS第11波段的不确定性仍成倍大于TIRS 10波段。因此,在Landsat团队未彻底解决这一问题之前,同时用TIRS第10、第11这两个差距较大的波段构成的劈窗算法来反演地表温度,其精度存在较大的不确定性,US6-S团队仍在致力于改进第11波段的精度,改进后的波段可以用劈窗策法。目前应以TIRS第10单波段的方式来反演地表温度为宜。     

面向矿床三维动态建模的地质勘探数据库增量更新方法

针对深部找矿过程中地质勘探数据增加和动态三维地质建模需要,提出了一种基于版本管理的矿床地质勘探数据库增量更新方法。在分析矿床地质勘探工程数据与三维地质模型映射关系的基础上,建立时序版本和建模版本作为矿床勘探数据增量更新版本标识。针对矿床地质勘探数据库增量更新的版本管理,提出了一种扩展的有向无环图版本管理模型,设计了基于关系数据库的版本管理方法并探讨了其实现过程。以安徽铜陵凤凰山矿床地质勘探数据为例,基于SQL Server数据库系统建立了实例矿床地质勘探版本数据库,以C#编程实现了实例矿床勘探数据库的版本管理,包括版本的建立、查询与显示等功能,通过勘探工程增量更新版本数据建立了三维矿体的动态修正模型,证实了方法的可行性与有效性。     

基于客观分析的多源卫星叶绿素a浓度产品融合方法研究

本文基于Aqua/MODIS、Terra/MODIS和Envisat/MERIS多源卫星叶绿素a浓度产品,研究了客观分析融合方法,制作了西北太平洋海域(0°~50°N,100°~150°E)叶绿素a浓度融合产品,并从有效数据空间覆盖率和产品精度两个方面对融合方法进行了评价。与单传感器以及欧洲太空局发布的GSM模型业务化融合产品相比,客观分析融合产品空间覆盖率明显提高;与收集的2002-2012年间叶绿素a浓度实测数据比较,GSM模型业务化融合产品的匹配数据点为578个,偏差为-0.20 mg/m3,均方根误差为0.37 mg/m3,客观分析法融合产品的匹配数据点为1432个,偏差为-0.21 mg/m3,均方根误差为0.36 mg/m3。结果表明:本文研究的客观分析融合方法在保证融合产品精度的同时可显著提高产品的空间覆盖率,在海洋水色融合应用前景广阔。     

南海中尺度涡强度的季节和年际变化分析

为了研究南海中尺度涡强度的季节和年际变化规律,利用Matlab提取50 a(1958~2007年)简单海洋资料同化(Simple Ocean Data Assimilation,SODA)月平均数据集中流场和海表面高度场数据,应用一个涡旋自动探测算法对南海中尺度涡初始生成位置进行分析,并分析了海表面高度异常均方根值的季节变化和年际变化。结果表明:50 a里南海中尺度涡主要分布在吕宋岛西北海域、吕宋岛西南海域和越南以东广大海域,秋、冬季中尺度涡能量较高,春季中尺度涡最弱,中尺度涡强度高值区年际变化明显。从季节变化上看,海面高度异常均方根春、夏季最小,秋冬季最大;从年际变化上看,与同时期Nino3指数有显著负相关,周期大约为3 a。     

Why Edit a Journal? Academic Irony and Paradox

Although journal editing is central to scholarly enterprise, helping to maintain academic standards and shape disciplines, it is frequently discouraged within the academic assemblages that depend on it. Following recent contributions by geographers to discussions on academic service such as book editing and reviewing, this article explores some ironies and paradoxes of journal editing. It first discusses the challenges of journal editing as well as its problematic place in “academic capitalism.” It then examines professional and personal rewards of journal editing. Despite strong disincentives, journal editing offers valuable opportunities for self-development and deepening professional networks, as well as for refining the discipline.     

The oldest (~ 1.9 Ga) metadolerites of the southern Siberian craton: age,petrogenesis, and tectonic setting

Geological, geochronological, and isotope-geochemical studies of the metadolerites of the Angaul complex, widespread in the Urik-Iya graben of the southern Siberian craton, were carried out. The metadolerites forming separate conformal bodies (sills) among the metasandstones of the Ingash Formation were studied in detail. U-Pb zircon (SHRIMP) dating of metadolerites yielded an age of 1913 ± 24 Ma, and U-Pb baddeleyite (ID-TIMS) dating of these rocks yielded an age of 1914.0 ± 1.7 Ma. Thus, the date of 1914 ± 2 Ma can be taken as the most precise age estimate for the studied rocks. The metadolerites of the Angaul complex correspond in chemical composition to the normal-alkaline tholeiitic basalts. Metadolerites are differentiated rocks with mg# of 36 to 58. They show fractionated REE patterns: (La/Yb)_n = 1.2-3.5. All metadolerites, independently of their mg# value, have low contents of Nb (1.6-10.2 ppm) and show well-pronounced negative Nb-Ta anomalies in multielement patterns (Nb/Nb* = 0.19-0.54). The metadolerites are characterized by positive ε_Nd(T) values ranging from 0.4 to 5.2, which correlate well with their SiO₂ content and mg# value. The isotope-geochemical parameters of the metadolerites of the Angaul complex indicate that fractional crystallization, along with the assimilation of the host rocks (AFC), might have been the main process during the formation of the most differentiated metadolerites. The geochemical characteristics of metadolerites with the maximum mg# values of 57-58 and ε_Nd(T) = 5.2 suggest that the parental mantle source of the metadolerites resulted from mixing of predominant depleted mantle material with the subcontinental-lithosphere material. Intrusion of the dolerites of the Angaul complex, as well as the deposition of the sedimentary strata of the Ingash Group, took place at the Paleoproterozoic stage of intracontinental extension caused by the collapse of the orogen resulted from the collision of the Biryusa block with the Tunguska superterrane in the southern Siberian craton.     

卷积神经网络及其在矿床找矿预测中的应用——以安徽省兆吉口铅锌矿床为例

大数据人工智能地质学刚刚起步,基于大数据智能算法的地质研究是非常有意义的探索性实验。利用大数据和机器学习解决矿产预测问题,有助于人们克服不能全面考虑地质变量的困难及评估当前模型在已有数据中的可靠性。元素地表分布特征量主要受原岩成分、成矿作用影响和地表过程的影响,它们携带某些指示矿体就位的信息,即矿体在地下空间就位时在地表的响应,且未在地表过程中消失。以往的地球化学勘查工作仅仅识别异常,但未能发现矿体在地表响应的成矿特征量。本文以安徽省兆吉口铅锌矿床为例,通过机器学习,利用卷积神经网络算法,不断挖掘元素Pb分布特征与矿体地下就位空间的耦合相关性。经过1000次训练后,可以得到准确率0. 93,损失率0. 28的卷积神经网络模型。这种神经网络模型就是矿体在地下就位时元素在地表分布的响应,可以用来进行矿产资源预测。应用该模型对未知区进行预测,结果显示第53号区域具有很大概率存在尚未发现的矿体。     

面向矿产资源信息的空间关联性分析

关联性分析是综合多源、多类数据,发现和挖掘数据中潜在的相关关系,提取和挖掘数据之间的关联性。该方法对于充分利用地质大数据、发现地质要素之间的共生组合规律具有重要的意义。本文首先基于数据的空间位置,将不同类型的地质空间数据建立联系,形成空间属性数据库;其次应用统计方法,对不同来源数据中的属性特征进行分析,发现热液型金矿的形成与火山作用存在明显相关性;最后基于Apriori算法提取热液型金矿的伴生矿与侵入岩的频繁项集,发现伴生矿与侵入岩酸碱性二者密切相关。因此,在今后的工作中,有望应用空间关联性的方法对地球物理、地球化学、遥感等多源地质数据开展深入的研究分析。     

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.