多特征差分核支持向量机模型的湿地变化检测   总被引：1，自引：0，他引：1  

潘琛  冯琰  林怡  陈映鹰 《测绘科学》2014,39(12):104-108,119

文章通过对不同特征空间、不同时相信息和不同类别引入多核函数组合和类别加权方式,构建了基于多特征差分核的加权支持向量机变化检测模型,能够获得平衡样本大小、综合多特征空间的变化检测结果.以上海崇明岛东端地区为例,利用4期ETM+/TM影像数据,基于构建的SVM变化检测模型,最终得到研究区湿地资源的空间变化格局,结果表明检测模型能够有效、准确地提取多种湿地变化类别的空间分布信息;并从景观水平上对检测结果进行格局分析,为湿地保护和管理提供科学参考.  相似文献   

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations     

R. Qahwaji  T. Colak  M. Al-Omari  S. Ipson 《Solar physics》2008,248(2):471-483

Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided.  相似文献   

Application of PSO-LIBSVM in Recognition of Potassium Salt Deposits     

Fuqiang Yang  Kegui Chen  Changbing Huang  Yuanyuan Chen  Jin Li  Xiaolin Ma 《地球科学进展》2019,34(7):757-764

The shortage of potassium salt seriously restricts the development of China's agriculture. Increasing the exploration and development of potash will help improve the self-sufficiency of potassium in China. With rich potassium salt resources, Sichuan basin is one of the most important research areas for potash exploration and development in China. Polyhalite is an important solid potassium salt mineral in Sichuan basin, often intercalated in rock minerals such as anhydrite, rock salt and dolomite. Aiming at the problem that conventional log interpretation methods are difficult to accurately identify polyhalites, this paper proposed a new Support Vector Machine (SVM) recognition method based on Particle Swarm Optimization (PSO) to classify polyhalites in Sichuan basin. Based on particle swarm optimization and support vector machine theory, combined with logging interpretation theory, the effective data sensitive to polyhalite logging response were selected as input samples to generate training sets and test sets randomly. The Radial Basis Function (RBF) parameters were optimized by particle swarm optimization, and the classification and prediction model of polyhalite was established. Compared with mud logging results, the recognition accuracy of SVM model based on particle swarm optimization reached 97.5758%, which is obviously better than that of SVM model optimized by cross validation method in recognition accuracy and speed. The results show that the model has broad application prospects in potash exploration in Sichuan basin.  相似文献   

基于遗传算法优化的支持向量机在岩性识别中的应用     

张昭杰  方石 《世界地质》2019,(2):486-491

为提高测井岩性识别的精度,本文结合乌夏地区岩芯资料和测井数据,总结该地区砂砾岩测井响应特征,优选出声波、自然伽马、密度、中子孔隙度和电阻率等5条测井曲线参数作为训练和测试样本,通过遗传算法挑选出最佳的支持向量机核函数参数σ和惩罚因子C,建立支持向量机岩性识别模型。结果表明该模型实际数据预测总体符合率为81.6%,在识别准确率上与传统测井识别砂砾岩岩性方法相比都有明显提升。  相似文献   

耦合PCA-SVM算法的金矿矿床规模预测分析研究     

刘承照  韩帅  李明超  朱月琴 《地学前缘》2019,26(4):138-145

判断矿床(点)的类型是矿床勘探中的重要内容,传统预测金矿成矿规模的方法不仅耗时耗力,而且所需的经济成本较大。为提高矿床规模的勘探效率和准确度,揭示元素与金矿成矿规模的潜在联系,文中提出了耦合主成分分析(principal component analysis,PCA)和支持向量机(support vector machine,SVM)算法的预测分析PCA-SVM(principal component analysis-support vector machine)方法。该方法先通过主成分分析提取数据中的主要特征,再将主要特征带入支持向量机算法,从而训练出最优分类器以预测金矿成矿规模。文中共使用了3 812个金矿样本数据用于学习训练和预测分析,训练准确率为92.3%,测试准确率为88.7%,分别比直接使用支持向量机算法高出14.3%和17.1%。基于PCA-SVM的预测模型,不仅消除了人为主观因素的影响,而且有效提高了勘探过程中矿床预测的准确率和矿床勘探的效率,为地质勘查工作提供依据。  相似文献   

ArcGIS在矢量测绘数据质量控制中的应用     

谭福初  裴亚波  李广兰 《北京测绘》2007,(4):45-47

GIS应用和信息化测绘发展对测绘数据质量提出更高要求,本文通过对矢量测绘数据质量要素控制的简单分析,详细介绍了实际工作中运用ArcGIS对主要的质量要素进行控制检查的方法和步骤,对实施矢量测绘数据的质量控制具有实际意义。  相似文献   

内核地球模型的形变场和地球参考系     

张捍卫  郑勇  刘长建 《测绘学院学报》1999,(2)

讨论了内核地球模型的形变场（ 位移、速度） 和地球参考系的确立, 为内核地球动力学的研究提供了依据。  相似文献   

各向异性介质的矢量波场分离与应用          下载免费PDF全文

芦俊  石瑛  杨春颖 《地球物理学报》2018,61(8):3310-3323

针对裂缝各向异性介质,本文提出一种非正交假设下的矢量波场分离方法.本文首先对多分量地震勘探中常见的波型泄漏现象进行了数学描述,提出在纵、横波波场分离的同时应该考虑恢复纵、横波的矢量振幅.为了对裂缝方位角与各向异性系数进行定量预测,本文将矢量波场分离拆分成三个步骤来实施：第一步,用Z、R两分量的仿射坐标系变换分离ZR平面内的P波投影与SV波;第二步,用ZR平面内的P波投影与T分量的仿射坐标系变换分离P波与SH波;第三步,用纯净的SV波与SH波的成像剖面分离快慢横波,并预测裂缝发育参数.模型数据与实际数据的试验结果表明,本文提出的纵、横波波场分离方法能够获得完整的矢量振幅信息,并提供裂缝预测的精度.  相似文献   

一种基于天基的瞬变电磁高分辨探测小尺度太空碎片的方法          下载免费PDF全文

李文翰  李贺  鲁凯亮  崔洪亮 《地球物理学报》2018,61(12):5066-5076

目前,太空碎片的天基探测方法一般是激光雷达法.由于激光雷达的辐射频率一般为可见光或红外波段,在分辨厘米量级的太空碎片时具有一定的优势.但激光雷达在探测时也面临一些客观问题：即（1）难以捕捉快速移动的太空碎片;（2）对目标体穿透能力低;（3）外层空间的强干扰环境对激光雷达的不良影响等.这些问题在一定程度上限制了激光雷达在太空碎片探测方面的应用.本文针对太空碎片体积小、预警距离远和强干扰环境等特点,提出使用高性能瞬变电磁辐射源对远距离、小尺度的太空碎片进行探测.通过三维矢量有限元方法,分析了利用高性能瞬变电磁辐射源的优点,对比不同瞬变电磁辐射源的幅频特性,从发射机理上证明高性能瞬变电磁辐射源在辐射能力与频率带宽方面都优于传统瞬变电磁辐射源.并通过调整脉冲宽度,得到最适合探测本文太空碎片模型的辐射脉冲.最后通过电场分布图和多测道图对太空碎片的明显电场分异结果,证实了高性能瞬变电磁辐射源在探测远距离、小尺度太空碎片方面的有效性.  相似文献   

Assessing model of highway slope stability based on optimized SVM     

《China Geology》2020,3(2):339-344

Considering the geological hazards attributed to the highway slope, using a common simple model cannot accurately assess the stability of the slope. First, principal component analysis (PCA) was conducted to extract the principal components of six factors (namely, bulk density, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio) affecting the slope stability. Second, four principal components were adopted as input variables of the support vector machine (SVM) model optimized by genetic algorithm (GA). The output variable was slope stability. Lastly, the assessing model of highway slope stability based on PCA-GA-SVM is established. The maximal absolute error of the model is 0.0921 and the maximal relative error is 9.21% by comparing the assessment value and the practical value of the test sample. The above studies are conducive to enrich the assessing model of highway slope stability and provide some reference for highway slope engineering treatment.  相似文献