A Re-Evaluation of Long-Term Flux Measurement Techniques Part II: Coordinate Systems

To convert measurements of windspeed, eddy flux and scalar concentration into estimates of surface scalar exchange, we implicitly or explicitly assimilate the measurements into mathematical statements of the mass balance in a control volume on a representative patch of the surface. The form of this statement depends on the coordinate system in which it is written and the coordinate system should be chosen so that measurements can be used optimally. This requirement imposes a set of conditions on the coordinates. Here we perform a comparative analysis of some candidate coordinate systems, concentrating on the Cartesian and physical streamline systems. We show that over gentle topography there are definite advantages in working in streamline coordinates. Transforming measurements of vector and tensor quantities measured in the reference frame, s _i, of the anemometer into the reference frame, e _i, of the chosen coordinate system involves using the measured statistics of the wind field to define three Euler rotation angles. We compare the method in most common use, which employs the components of the mean wind vector and the Reynolds stress tensor to define these angles, with the more recent planar-fit method that uses instead an ensemble of mean wind vectors to define the rotations. We find that, in real flows, the standard method has a previously unrecognized closure problem that ensures that the third rotation angle defined using the stress tensor or scalar flux vector will always be in error and often give unphysical results. An alternative procedure is recommended. Finally, the relationships between measurements and model outputs are discussed.     

多特征差分核支持向量机模型的湿地变化检测

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.     

基于遗传算法-支持向量机模型在热带气旋强度预报中的应用

利用遗传算法对支持向量机(SVM)模型参数进行寻优,找到最优参数组合后代入SVM模型中,得到基于遗传算法的支持向量机模型(GA-SVM),利用此模型对热带气旋强度进行预报实验。该模型对热带气旋强度12 h、24 h和48 h的预报平均绝对误差分别为3.01 m/s、4.46 m/s和6.57 m/s;比最小二乘回归法的预报精度分别提高了12%、11%、14%。     

Analysis of earthquake ground motions using an improved Hilbert–Huang transform

Some limitations of the Hilbert–Huang transform (HHT) for nonlinear and nonstationary signal processing are remarked. As an enhancement to the HHT, a time varying vector autoregressive moving average (VARMA) model based method is proposed to calculate the instantaneous frequencies of the intrinsic mode functions (IMFs) obtained from the empirical mode decomposition (EMD) of a signal. By representing the IMFs as time varying VARMA model and using the Kalman filter to estimate the time varying model parameters, the instantaneous frequencies are calculated according to the time varying parameters, then the instantaneous frequencies and the envelopes derived from the cubic spline interpolation of the maxima of IMFs are used to yield the Hilbert spectrum. The analysis of the length of day dataset and the ground motion record El Centro (1940, N–S) shows that the proposed method offers advantages in frequency resolution, and produces more physically meaningful and readable Hilbert spectrum than the original HHT method, short-time Fourier transform (STFT) and wavelet transform (WT). The analysis of the seismic response of a building during the 1994 Northridge earthquake shows that the proposed method is a powerful tool for structural damage detection, which is expected as the promising area for future research.     

应用SVM方法进行沉积微相识别

作者针对目前沉积微相中的特征提取问题,提出了应用ＳＶＭ（支持向量机）方法进行沉积微相识别的方案。该方法不是象传统方法那样首先试图将原输入空间降维（即特征选择变换）,而是设法将输入空间升维,以求在高维空间中问题变得线性可分（或接近线性可分）。因为升维后只是改变了内积运算,并没有使算法复杂性随着维数的增加而增加,因此这种方法才是可行的。所以。利用该方法更能胜任实际情况。实际处理表明该方法在小样本情况下     

大地网三类设计的应变分析法

本文针对大地网三类设计的质量标准问题,提出了一种新方法:应变分析法。它可以根据区域性的尺度、旋转、变形三个应变参数的变化,来发现网的局部区域性系统差或粗差;并能较明晰地反映出新增观测值对原网的局部改进或细节改进。     

An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit

Sungun porphyry copper deposit is in East Azarbaijan province, NW of Iran. There exist four hypogene alteration types in Sungun: potassic, propylitic, potassic–phyllic, and phyllic. Copper mineralization is essentially associated more with the potassic and less with the phyllic alterations and their separation is, therefore, quite important. This research has tried to separate these two alteration zones in Sungun porphyry copper deposit using the Support Vector Machine (SVM) method based on the fluid inclusion data, and seven variables including homogenization temperatures, salinity, pressure, depth, density and the Cu grade have been measured and calculated for each separate sample. To apply this method, use is made of the radial basis function (RBF) as the kernel function. The best values for λ and C (the most important SVM parameters) that perform well in the training and test data are 0.0001 and 1, respectively. If these values for λ and C are applied, the phyllic and potassic alteration zones in the training and test data will be separated with an accuracy of about 95% and 100%, respectively. This method can help geochemists in separating the alteration zones because classifying and separating samples microscopically is not only very hard, but also quite time and money consuming.     

论航空影像的纹理与描述

本文在前人研究的基础上，提出以中心像元的近邻像元，而不是近邻像元的组合，作为纹理基元，纹理基元的灰度统计规律反映出影像纹理的特征，这种特征可以用马尔柯夫随机场（ＭＲＦ）来描述，其特征参数可以作为纹理分类的依据。通过实验分析，证明本文的提法是合理的。     

跨孔观测地震数据的速度重建

利用矢量射线追踪正演模拟技术 ,计算地震波传播的路径及走时 ,进而利用射线走时及路径的内插 ,发展了弯曲射线迭代反演技术 .该方法可用来重建井间地层的速度图像 .基于所发展的方法 ,我们对两种较为复杂的典型地质模型进行了井间速度重建 .结果表明该方法是一项快速、高精度的跨孔数据速度重建技术     

耦合PCA-SVM算法的金矿矿床规模预测分析研究

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。