南黄海盆地中部隆起CSDP-2井初步成果及其地质意义

南黄海盆地是新生代、中生代、古生代大型叠合盆地,具有良好的油气资源前景.中部隆起是南黄海盆地的二级构造单元,由于缺乏钻探资料,对中部隆起前新生代地层分布和油气资源前景的认识众说纷纭.因此,在中部隆起上实施科学钻探,探查中-古生代地层属性,对研究南黄海区域地质构造特征和油气地质条件意义重大.针对科学钻探目标,采用区域地质背景指导下的地质、地球物理综合研究的方法,拟定了科学钻探井(CSDP-2井)位置;利用叠前时间偏移成像处理剖面,采用地震相、地震速度分析和储层预测技术,进行了钻前层位预测和油气储层分析.该井由青岛海洋地质研究所负责的"大陆架科学钻探"项目实施,全取芯钻探成果表明,中部隆起上赋存了三叠系-奥陶系海相残余地层,发育中等—好级别的、厚度较大的海相中-古生界烃源岩,在三叠系青龙组、二叠系龙潭组和栖霞组、泥盆系和志留系中获得多层不同级别的油气显示.CSDP-2井的钻探发现,结束了关于中部隆起海相地层属性的争论,坚定了南黄海海相油气勘探的信心,为区域地质研究和油气地质条件分析提供了基础数据.     

Frequent subgraph mining in oceanographic multi-level directed graphs

We present an adaptation and application of frequent subgraph mining (FSM) in a time series of spatial multi-level directed graphs depicting probabilistic transitions of water masses between neighboring sea areas within a given time interval. The directed graphs are created from the results of the numerical model, the Mediterranean Ocean Forecasting System. We assign unique labels (geographical locations) to vertices of the multi-level directed graphs. Then, we add the edge labels as discretized values of the probabilities of transitions between vertices. This modification allows the use of the established algorithm gSpan to search for frequently directed subgraphs in the sequence of such directed graphs. Thus, we obtain both general and specific subgraphs, such as convergences, divergences, and paths of the ocean currents in the numerical model. The resulting substructures, revealed by directed subgraphs, match oceanographic structures (gyres, convergences/divergences, and paths) deduced from field observations, and can also serve as a tool for the validation of the numerical model of circulation in the sea.     

高分六号红边特征的农作物识别与评估

红边作为植被敏感波段,其红边特征的运用是遥感识别农作物并实现精准农业的高新手段之一。以黑龙江松嫩平原北部为研究区,以国内首个提供红边波段的多光谱高分六号影像和玉米、大豆、水稻总计82859个作物样本同时作为研究对象,从以下几个方面研究了红边波段和红边指数波段等红边特征在农作物识别中的表现,并评估了农作物的识别精度。（1） 通过作物样本辐射亮度值的统计特征,初步显示了在两红边波段0.710 μm和0.750 μm处有比其他波段更好的区分;（2） 根据传统归一化植被指数形式构建了红边归一化植被指数NDVI710和NDVI750,综合两指数在J-M距离表征的作物样本类别区分度上比传统NDVI更显著;（3） 通过多种手段筛选了有效波段并且制定了支持向量机（SVM）框架下4种农作物识别的分类策略,分别在5∶5、6∶4、7∶3、8∶2、9∶1等5套随机样本分割方案下完成研究区域农作物的分类预测。在这20类分类精度中kappa系数均高于0.9609,总体精度高于0.9742;列向上5∶5分割方案的精度最高,8∶2的精度最低;横向上分类精度排序如下：SVM-RFE > SVM-RF > SVM-有红边波段 > SVM-无红边波段,该结果表明了红边指数和红边波段的参与显著地提高了作物的识别精度;（4） 由于水域等其他样本的缺少,SVM-RFE方法和SVM-RF方法的分类图像均存在少量错分现象。但从分类精度和图像细节展示上来看,SVM-RFE方法要优于SVM-RF方法,二者分类图像的交叉验证中kappa系数为0.8060,总体精度为0.8743。总之,高分六号红边特征在作物识别中表现优越,使得识别精度显著提高。后续研究者可开发更多与红边相关的植被指数,充分发挥红边特征在精准农业中的作用。     

基于遥感和机器学习的内陆水体水深反演技术

文章主要根据机器学习算法（随机森林算法和极端梯度提升算法）和遥感水深反演的原理,利用Sentinel_2多光谱卫星数据和无人船实测水深数据,对内陆水体——梅州水库建立了随机森林（RF）、极端梯度提升（XGBoost）和支持向量机（SVM）水深反演模型,并对反演结果进行对比分析。结果表明：1）RF的训练精度为97%,测试精度为0.80;XGBoost模型的训练精度为97%,测试精度为0.79;SVM的训练精度为90%,测试精度为0.78。说明了在水深预测方面RF模型和XGBoost模型比SVM模型表现更好,对各个区段的水深值较为敏感。2）根据运行时间考察各个模型的效率,其中RF模型从读取数据至输出结果耗时3.92 s;XGBoost模型4.26 s;SVM模型6.66 s。因此,在反演精度和效率上RF模型优于XGBoost模型优于SVM模型,且RF模型的预测结果图细节更加丰富,轮廓更加分明;XGBoost模型次之,但总体效果也较好;SVM模型表现最差。由此可知,机器学习水深反演模型获得的水深结果精度明显提高,解决了传统水深反演模型精度不高的问题。     

All pixels are useful,but some are more useful: Efficient in situ data collection for crop-type mapping using sequential exploration methods

A precise knowledge of the crop distribution in the landscape is crucial for the agricultural sector to inform better management and logistics. Crop-type maps are often derived by the supervised classification of satellite imagery using machine learning models. The choice of data sampled during the data collection phase of building a classification model has a tremendous impact on a model's performance, and is usually collected via roadside surveys throughout the area of interest. However, the large spatial extent, and the varying accessibility to fields, often makes the acquisition of appropriate training data sets difficult. As such, in situ data are often collected on a best-effort basis, leading to inefficiencies, sub-optimal accuracies, and unnecessarily large sample sizes. This highlights the need for new more efficient tools to guide data collection. Here, we address three tasks that one commonly faces when planning to collect in situ data: which survey route to select among a set logistically feasible routes; which fields are the most relevant to collect along the chosen survey route; and how to best augment existing in situ data sets with additional observations. Our findings show that the normalised Moran's I index is a useful indicator for choosing the survey route, and that sequential exploration methods can identify the most important fields to survey on that route. The provided recommendations are flexible, overcome the main logistical constraints associated with in situ data collection, yield accurate results, and could be incorporated in a mobile application to assist data collection in real-time.     

Mapping hurricane damage: A comparative analysis of satellite monitoring methods

Wetlands are the second-most valuable natural resource on Earth but have declined by approximately 70 % since 1900. Restoration and conservation efforts have succeeded in some areas through establishment of refuges where anthropogenic impacts are minimized. However, these areas are still prone to wetland damage caused by natural disasters. Severe storms such as Hurricane Irma, which made landfall as a Category 3 hurricane in southwest Florida (USA) on September 11, 2017, can cause the destruction of mangroves and other wetland habitat. Multispectral images from commercial satellites provide a means to assess the extent of the damage to different wetland habitat types with high spatial resolution (2 m pixels or finer) over large areas. Using such images presents a number of challenges, including deriving consistent and accurate classification of wetland and non-wetland vegetation. Machine learning methods have demonstrated high-accuracy mapping capabilities on small spatial scales, but require a large amount of robust training data. Meanwhile, ambitious efforts to map larger areas at finer resolutions may use hundreds of thousands of images, and therefore encounter Big-Data processing challenges. Large-scale efforts face the dilemma of adopting traditional mapping methods that may lend themselves to Big Data analytics but may result in accuracies that are inferior to new methods, or move to machine learning methods, which require robust training data. Given these considerations, we describe a version of the traditional Decision Tree (DT) approach and compare two common machine learning methods to derive land cover classes using a WorldView-2 image collected on November 12, 2018 to include one growing season after Hurricane Irma affected this area. Specifically, we compared the Support Vector Machine [SVM] and Neural Network [NN] methods, trained and validated with separate ground-truth datasets collected during a robust field campaign. Overall accuracies were only marginally different (85 % NN vs 83 % each DT and SVM), but healthy mangroves were more accurately identified with the DT (91 % vs 88 % NN and 86 % SVM), and degraded mangroves were more accurately identified with NN (62 % vs 57 % NN and 38 % DT). These results, combined with their respective training requirements, have implications for the direction with which large-scale high-resolution mapping of coastal habitats proceeds.     

Constructing a raster-based spatio-temporal hierarchical data model for marine fisheries application

1 Introduction The GIS has shown great success for terrestrial ap- plicationsin the last decades, but its use in marine fields has been far more restricted. One of the main reasons is that most of the GIS systems or their data models are designed for land…     

龙虎山风景名胜区峨眉峰景区的发现及其意义

本文对鹰潭市龙虎山风景名胜区峨眉峰景区作了概略介绍,同时对整个名胜区的旅游环境、旅游资源进行了分析评价,最后指出了对该景区发掘的意义及开发利用的初步设想。     

数据挖掘和知识发现的技术方法

数据挖掘是指从数据库中提取出隐含的、先前不知道的有用知识的过程。本文从数据挖掘和知识发现的概念出发 ,总结了数据挖掘常采用的技术方法 ,并从数据挖掘采用的技术方法的角度对数据挖掘进行分类 ,指出每种技术适合挖掘出哪些知识 ,比较每种技术的优缺点 ,同时还对几个数据挖掘的应用进行了阐述。     

地学信息共享与知识发现

地学信息共享是地学研究的重要内容 ,也是地学 (数字地球 )发展的趋势之一。促使或完成地学信息共享的核心技术主要为技术标准化规范、空间数据库、互操作 (元数据 )以及传输信道技术等。知识发现是建立在共享的地学信息基础之上 ,能有效地实现知识的增值以及知识的再生。知识发现与信息共享二者共同促进 ,相互发展。