Gradient Boosting Decision Tree for Lithology Identification with Well Logs: A Case Study of Zhaoxian Gold Deposit,Shandong Peninsula,China

Natural Resources Research - Identifying lithology from well logs is an important step in deep prospecting and resource estimation. Various machine learning algorithms have been adopted to identify...     

基于遗传支持向量机的城市扩张非线性组合模型

在分析当前城市扩张模拟模型优缺点的基础上,利用支持向量机能有效表达、拟合复杂非线性系统的功能,将多个单项城市扩张模型进行非线性组合,有机融合各单项模型优点,最后构建支持向量机的城市空间扩张非线性组合模拟模型。利用遗传算法优化支持向量机的参数,减小参数设置不合理对支持向量机建模精度引起的影响,有效提高支持向量机模型精度。通过分析组合模型误差和各参与组合的单项模型之间的关系,总结出提高支持向量机的城市扩张非线性组合模型精度的方法是：① 提高参与组合的各单项模型精度;② 增加单项模型之间的差异性。以长沙市为例,分别构建多元回归、GM(1,8)、BP网络和LS-SVM单项城市空间扩张模拟模型,并在此基础上建立线性组合城市扩张模型和遗传支持向量机非线性组合城市扩张模型。通过各模型精度对比分析证明,遗传支持向量机的城市扩张非线性组合模型精度远优于各单项模型,并且优于线性组合模型,是一种有效的城市扩张新模型。     

基于决策树与监督、非监督分类方法相结合模型的遥感应用研究

遥感影像解译中的决策树分类法一般是引入NDVI植被指数、亮度阈值法、DTM、空间结构、纹理、和其它一些地貌特征来实现地物类别的分离;而传统的监督分类、非监督分类是直接基于像元的亮度值而进行的分类,两者各有优缺点。将两者在遥感影像解译中结合使用,建立统一的分类模型,并以皖东地区TM影像为例进行了分类实验,结果证明,采用该模型分类比单一的最大似然法分类精度提高了4.45%,Kappa指数提高了0.107,该模型能有效地提高影像分类的精度。     

厂商跨界学习能力--"邻近性"观点回顾与研究途径的建议

本文以资源依赖取径出发讨论厂商的跨界学习过程,提出研究者应关注技术密集度岛、已具备全球分工态势之外的产业类别。以及技术学习之外的知识需求,在承认影响知识传递的各种邻近性既非自动出现,也非既定存在之后,厂商地理尺度对学习能力的形塑如何成为建立邻近性的基础,是检视当代厂商跨界学习能力的试金石,同时主张以动态取径观察厂商能力的变化与提升过程,作者认为后进国家的厂磕。经验将是一个适当的切入点。响应于经济地理学文化转变后的研究旨趣并接合厂商跨国学习能力的讨论,作者提出以华人企业前往大陆投资并进行创新和学习活动的研究,观察厂商自主性的展现和以及厂商能力的形塑的关键力量与地理尺度的关连。可成为地理学对当代知识经济论述的基础。     

On the Analysis of Vector Arrays on a Spherical Surface

Summary. The need for, and difficulties commonly experienced during, the analysis of patterns of vectors with superimposed 'noise' upon the surfaces of spheres is discussed. A simple procedure is described whereby many of these difficulties may be circumvented and certain classes of systematic array detected even against a considerable random background. An extension of the same procedure to the analysis of vector patterns in a plane is discussed.     

A Novel Multiple-Kernel Support Vector Regression Algorithm for Estimation of Water Quality Parameters

Natural Resources Research - The quality of surface waters plays a key role in the sustainability of ecological systems. Measuring water quality parameters (WQPs) is of high...     

信息农业的支撑基础与发展探讨

介绍信息农业的概念和特点 ,分析发展信息农业的支撑条件 ,提出发展信息农业的对策     

Thermal Properties of a Supraglacial Debris Layer with Respect to Lithology and Grain Size

This paper focuses on the impacts of debris cover on ice melt with regards to lithology and grain size. Ten test plots were established with different debris grain sizes and debris thicknesses consisting of different natural material. For each plot, values of thermal conductivity were determined. The observations revealed a clear dependence of the sub‐debris ice melt on the layer thickness, grain size, porosity and moisture content. For the sand fraction the moisture content played a dominant role. These test fields were water saturated most of the time, resulting in an increased thermal conductivity. Highly porous volcanic material protected the ice much more effectively from melting than similar layer thicknesses of the local mica schist. However, the analysis of thermal diffusivities demonstrated that the vertical moisture distribution of the debris cover must be taken into consideration, with the diffusivity values being significantly lower in deeper layers.     

基于纹理特征与LSSVM的青土湖地物提取

纹理特征作为一种非光谱信息能够增强地物之间的特征差异,这对于高分辨率遥感影像的地物提取有着重要意义。以青土湖为研究区,以Worldview-2影像为数据源,通过引入权重因子定义联合概率函数来确定最佳窗口尺度,利用灰度共生矩阵提取最佳窗口尺度下的纹理特征,将其与原始遥感影像合成,采用最小二乘支持向量机（LSSVM）进行地物提取,将提取结果与仅利用光谱信息的支持向量机（SVM）提取结果、辅以纹理特征的SVM提取结果对比分析。结果表明：此方法可以更加快速准确地提取青土湖地物,精度高达85.86%,优于仅利用光谱信息的SVM的65.13%,辅以纹理特征的SVM的73.45%,可为地物破碎的干旱区高分辨率遥感影像地物提取提供有益借鉴。     

Prediction of Vibration Velocity Generated in Mine Blasting Using Support Vector Regression Improved by Optimization Algorithms

Natural Resources Research - Ground vibration generated from blasting is a detrimental side effect of the use of explosives to break the rock mass in mines. Therefore, accurately predicting ground...     

地方院所提升自主创新能力研究

河南省科学院是全国地方科学院中综合实力比较强的单位之一,在材料科学、生物工程、化学化工、能源与环境技术、电子与自动化和软科学研究领域均拥有独特自主创新优势,在建设创新型河南中将发挥重要作用。为了贯彻落实全国科学技术大会精神,河南省科学院将采取多种措施,深化管理体制改革,努力打造一流的创新团队,力争在自然科学领域大幅度提升自主创新能力。     

Prediction of Blast-Induced Rock Movement During Bench Blasting: Use of Gray Wolf Optimizer and Support Vector Regression

Natural Resources Research - A large ore loss and dilution can be expected when using a pre-blast ore boundary for shovel guidance because of the movement and re-distribution of ore in the muck...     

To Inhabit Well: Counterhegemonic Movements and the Right to the City

A right to the city, understood as a conjoint claim to a right to inhabit urban space well, can be an effective starting point from which diverse urban movements can begin to build broad counterhegemonic coalitions for alternative urban futures. In this article, I argue that the right to the city supports the project of establishing relations of equivalence among members of coalitions––balancing relations of sameness/difference and interdependence/autonomy.     

GIS中面积偏差控制下的矢量数据压缩算法

地理信息系统对矢量数据进行处理和应用的过程中,数据压缩是一个必须解决的问题,而通常采用的两种经典曲线数据压缩法:垂距限值法和道格拉斯-普克算法,都存在线段空间偏移过大以及面积偏差不可控的问题。利用曲线空间直线拟合的方法对曲线段中心轴进行空间逼近,通过增加面积偏差限值,提出了面积偏差控制下的矢量数据压缩算法,并以上海市崇明县岛屿边界轮廓矢量为例对该算法进行了验证。试验表明该方法对于解决两种经典压缩算法线段空间偏移过大以及面积偏差不可控的问题效果明显。     

Goodnews Bay Platinum Resource Estimation Using Least Squares Support Vector Regression with Selection of Input Space Dimension and Hyperparameters

Resource estimation of a placer deposit is always a difficult and challenging job because of high variability in the deposit. The complexity of resource estimation increases when drill-hole data are sparse. Since sparsely sampled placer deposits produce high-nugget variograms, a traditional geostatistical technique like ordinary kriging sometimes fails to produce satisfactory results. In this article, a machine learning algorithm—the support vector machine (SVM)—is applied to the estimation of a platinum placer deposit. A combination of different neighborhood samples is selected for the input space of the SVM model. The trade-off parameter of the SVM and the bandwidth of the kernel function are selected by genetic algorithm learning, and the algorithm is tested on a testing data set. Results show that if eight neighborhood samples and their distances and angles from the estimated point are considered as the input space for the SVM model, the developed model performs better than other configurations. The proposed input space-configured SVM model is compared with ordinary kriging and the traditional SVM model (location as input) for resource estimation. Comparative results reveal that the proposed input space-configured SVM model outperforms the other two models.     

Group Method of Data Handling (GMDH) Neural Network for Estimating Total Organic Carbon (TOC) and Hydrocarbon Potential Distribution (S1, S2) Using Well Logs

Natural Resources Research - Recent advancement in computing capabilities has brought to light the application of machine learning methods in estimating geochemical data from well logs. The widely...     

老工业基地城市对农村劳动力吸纳力的研究——以长春市为例

解决"三农"问题的根本途径是实现农村剩余劳动力的有效转移,而农村劳动力转移的主要方向之一是离开农村到城市就业。就全国而言,城市制造业就业比重下降、三产比重上升是我国经济结构转换、升级的一般趋势,但这种趋势在区域之间又存在着差异。老工业基地除面临制造业升级排斥就业之外,其它产业发育也相对不足。在这种情势下,老工业基地城市如何发挥农村劳动力接收器的作用,就此问题以长春市为例,在实地调查的基础上,进行了深入地研究和探讨。     

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.     

光卤石冷分解能力强化途径

利用钾肥生产车间现有设备 ,尝试了几种增大分解槽内光卤石矿颗粒之间自磨能力的方法 ,加大了分解槽光卤石矿的分解能力 ,对同等冷分解———浮选设备 ,钾肥的产量有较大的提高