TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification per- formance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91％ for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05％ using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.     

基于随机森林算法的陆相沉积烃源岩定量地震刻画:以东海盆地长江坳陷为例

烃源岩的定量地震刻画对于勘探开发区块的优选、盆地油气资源量的估算都具有重要意义.陆相沉积环境下的浅湖或半深湖相的烃源岩横向变化快,其空间展布需要依靠钻井约束下的反射地震进行刻画,但是其地震弹性特征与岩性和有机质含量的映射关系呈现高度非线性化,因而很难利用传统基于地震岩石物理模型驱动的烃源岩地震预测方法进行有效刻画.本文...     

长春市九台区大青山金矿地质特征、发现过程及地质意义

九台区大青山金矿位于北东向与北西向断裂交汇部位,金矿化主要赋存于白垩系下统营城组中酸性火山岩系中,是该区近年来新发现的一处金矿。笔者通过总结该矿点的地质特征,认为该金矿点是一处岩浆喷溢低温热液型金矿。该矿点是在地质、物化探密切配合下发现的,其发现过程对该地区今后的金矿勘查找矿工作具有重要意义。     

基于上下文感知的地理信息服务发现与匹配技术研究

如何在网络上寻找到最适合的地理信息服务,即服务的发现与匹配技术的需求变得越来越迫切。本文结合普适思想提出了基于上下文感知的地理信息服务多级发现与匹配框架,包括基本描述、服务功能、约束条件、空间上下文4级匹配过程,并详细分析了空间上下文的建模与推理、地理信息相似性等关键技术,最终通过一个原型系统验证了该框架及关键技术的可行性。     

柴达木盆地石炭系油气调查最新进展

柴达木盆地石炭系是海相油气勘探的新领域,通过对柴达木盆地石炭系油气地质条件进行调查,评价其资源潜力,为该区寻找油气战略接替区提供科学依据。以柴达木盆地石炭系为主要目标,通过调查和研究,首次获得柴达木盆地石炭系油气流,在柴达木盆地古生界海相新层系油气方面取得重要发现;综合区域调查、平衡剖面反演及镜质体反射率等分析,获取柴达木盆地主要构造运动期次;结合盆地模拟技术,重建了石炭系埋藏史,分析了柴达木盆地德令哈坳陷石炭系烃源岩油气地化指标,研究了烃源岩生烃演化史。研究表明:柴达木盆地德令哈坳陷石炭系烃源岩广泛发育,有机质丰度较高,埋深较大,但未发生变质,处于成熟-高成熟阶段;德令哈坳陷石炭系埋藏史主要表现为快速埋藏期、稳定期与强烈抬升剥蚀期,新生代以来沉降史与抬升剥蚀史存在着差异;石炭系烃源岩热演化史主要表现为“存在二次生烃,晚期生烃为主”的特点,主要受该区构造运动控制;柴达木盆地热演化总体表现为缓慢降低的趋势,主要受控于柴达木盆地岩浆热事件与构造活动。调查分析表明,新层系石炭系油气条件良好,资源前景广阔,是柴达木盆地下一步勘探的接替层系。     

Development and future prospects of quantitative mineral assessment in China

Mineral potential assessment at the Earth’s surface has been an important research for geoscientists around the world in the past five decades. The fundamental aspects of mineral assessment at different scales can be associated with the following tasks, e.g., mineral potential mapping and estimation of mineral resources. This paper summarized the history and development in terms of theories, methods technologies and software platforms for quantitative assessment of mineral resources in China, e.g. comprehensive information methodology, geological anomaly, three-component quantitative prediction method, 5P ore-finding area, integrated information assessment method, nonlinear process modeling and fractals, three dimensional mineral potential mapping, etc. At last, to discuss the future of quantitative mineral assessment in an era of big data including platform for 3D visualization, analysis and sharing, new methods and protocols for data cleaning, information enhancement, information integration, and uncertainties and multiple explanations of multi-information.© 2019 China Geology Editorial Office.     

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基于移动激光扫描点云特征图像和SVM的建筑物立面半自动提取方法

建筑物立面是城市地物的重要组成部分,而移动激光扫描是获取城市地物三维信息的重要手段之一。本文提出了一种基于移动激光扫描点云的建筑物立面半自动提取算法。该方法首先构建研究区水平网格;然后计算局部点云几何特征,并且将特征投影到水平网格生成点云特征图像;接着基于支持向量机（Support Vector Machine,SVM）对建筑物立面网格进行粗提取;最后使用网格属性（形状系数、网格面积、最大高程）对粗提取结果进行过滤,并将结果反投影到三维空间中得到精确的建筑物立面。以卡内基梅隆大学的移动激光扫描点云进行试验后表明,本算法能够较好地提取出建筑物立面,提取精度为84%,召回率为90%,数据修正后精度为88%,召回率为91%。通过与现有算法对比,本文提出的算法具有较高精度。     

BP神经网络和SVM在矿山环境评价中的应用分析

矿山环境的影响因素多样,定量评价过程易受人为因素干预。BP神经网络与SVM算法能够自动模拟各因子间的非线性关系。首次将其引入到矿山环境评价中,选取160个单元作为训练样本,以自然地理、基础地质、开发占地及地质环境等4个大类的14个变量指标为输入向量,以单元评价得分为输出向量,分别建立BP神经网络与SVM矿山环境评价模型。结果表明：两种模型均能满足矿山环境评价的精度要求;SVM模型收敛速度较BP神经网络快,MSE小于BP神经网络,更适合矿山环境评价工作;将定量模型应用于研究区,评价得分划分为4个级别,与定性评价结果一致,为矿山环境评价工作提供了新思路。