共查询到20条相似文献，搜索用时 171 毫秒
1.
岩心光谱属于混合像元光谱,维度多、数据量大,在端元数量、端元光谱及混合矩阵未知的情况下,定量解译岩心光谱以提取岩心所含矿物信息难度大,噪声的存在使问题更加复杂。文章应用PCA和ICA法定量解译岩心光谱主要有三步:采用PCA法预处理混合像元光谱矩阵,在新的特征空间中保留特征值较大的少量特征矢量,有效滤除能量较小的成分,只保留主要成分信息,同时滤除数据中的噪声;采用ICA法分离岩心混合像元,得到混合像元中的端元光谱集,通过矿物识别获取岩心矿物成分;对端元光谱进行归一化处理,基于线性光谱混合模型进行丰度反演,得到岩心混合像元中各端元丰度。通过研究仿真数据获得光谱定量解译的一般规律,创建有效的算法模型,再处理实际测量的岩心光谱,获得了比较理想的结果。 相似文献
2.
成分数据的因子分析及其在地质样品分类中的应用 总被引：1，自引：0，他引：1
地质样品的化学组成都是成分数据。确定一组混合源成分数据的端元数目、端元的化学组成及各样品中含各端元的绝对份额是地质样品成因分类的关键。求解这一问题相当于确定模型X=LB(lij≥ 0 ,∑lij=1)中X矩阵的秩、B矩阵的行向量和L矩阵的列向量。在确定X矩阵的秩和初始B0 矩阵的基础上 ,用限定最小二乘法循环调整矩阵B0 ,使其bij≥ 0和∑bij=1而得矩阵B ,同时也解得了矩阵L。X矩阵的秩和B0 是在传统的Q型因子分析基础上 ,求最优斜交因子解得到的 相似文献
3.
基于灰度共生矩阵的图像纹理特征地物分类应用 总被引：9，自引：0，他引：9
针对传统遥感影像分类方法的分类精度不高,在分析图像的光谱信息的基础上,对基于灰度共生矩阵的纹理特征在地物分类中的应用进行了研究.本研究利用原始图像进行主成分分析后的前两个主成分,经过编程运算,提取了基于灰度共生矩阵方法的不同测度的纹理特征,将提取的纹理特征作为新的波段,与原始波段进行组合,再对组合图像进行监督分类,探索... 相似文献
4.
所谓近似对称矩阵是指矩阵中仅有极少一部分元素是非对称的,在将对角线以上的非对称元素用其对角线以下的对称元素替代后,该矩阵就变成了一个对称矩阵。在求解非线性连续介质力学问题时常常会遇到近似非对称矩阵。基于Sherman-Morrison定理,给出了一种新的近似非对称矩阵的分解算法。在确保数值稳定性的前提下,无论在求解效率还是在内存开销方面新算法都优于一般的非对称稀疏矩阵的求解器,且仅需对传统的基于LDLT分解的求解器略做修改,即可开发出适应于对称和非对称稀疏矩阵的求解器。最后用一个摩擦接触算例,显示了新算法的优越性。 相似文献
5.
矿物的结构和成分可用来反演岩浆演化过程。为探讨酸性岩浆房内岩浆演化过程,以浙江拔茅火山主体英安岩中的斜长石为例,对其开展了矿物学、地球化学研究。结果显示,斜长石普遍具有核-幔-边结构,暗示其在喷发前处于开放性岩浆房内,并经历了复杂的岩浆过程。对代表性斜长石的电子探针成分及背散射图像分析表明,斜长石核部、幔部及边部分别具有不同的成因,反映了英安岩岩浆从深部聚集并在浅部岩浆房内周期性补给的过程。岩浆的这种周期性补给过程抑制了结晶分异作用对熔体成分的改变,全岩地球化学特征主要反映其源区特征及熔融过程。 相似文献
6.
桂林市褶皱构造对岩溶塌陷的控制作用 总被引：6，自引：0，他引：6
对桂林市褶皱构造的几何形态、应力状态及裂隙发育特征研究表明,背(向)斜的轴(核)部、倾伏端(扬起端)及指状分叉钳口处是岩溶塌陷发育的有利构造部位.本区654个岩溶塌陷,近20%发生在剥蚀深度不大且地势低洼的背斜轴部、倾伏端、指状分叉钳口处,以及埋藏深度较浅的向斜核部和扬起端.市区东西两侧近SN向的尧山背斜和塘家湾背斜等,控制了本区岩溶塌陷总的水文构造环境. 相似文献
7.
断裂带基性构造岩中的角闪石变形行为及变形过程中成分变化是研究断裂带变形环境及构造演化的重要手段,也是研究中地壳角闪质岩石流变学特征的重要依据。本文通过对洛南-栾川断裂带庙子构造剖面中基性糜棱岩和构造片岩中角闪石的变质变形分析研究表明角闪石的显微变形特征以膨凸式和亚颗粒式动态重结晶为主,其变形环境应为角闪岩相。探针成分分析显示角闪石全部为阳起石,具有明显的成分环带,角闪石Al2O3-Ti O2图解显示其核部成分偏壳幔混合,边部成分偏壳源;(Na+K)-Ti变异图显示其形成环境主要为角闪岩相,核部偏麻粒岩相,边部偏绿片岩相。与角闪石共生斜长石也具有明显的成分环带,残斑核部成分偏基性,边部及基质中新生斜长石成分偏酸性。用斜长石-角闪石实验地质温压计计算结果指示了核部温压为620~640℃,0.4~0.7GPa;幔部的温压为490~520℃,0.15~0.65GPa;边部的温压为465~482℃;0.5~0.8GPa。所以,庙子基性糜棱岩中角闪石的核部形成于相对较高温的深部环境;边部是在断裂带剪切作用下,产生塑性变形和退变质偏壳源成分的环带,反映出在活动过程中有一定的抬升。所以,由断裂带角闪石的变质变形分析可以揭示断裂带的构造活动过程以及温压环境,对研究造山带中断裂带的活动条件及演化有重要意义。 相似文献
8.
藏南拉轨岗日变质核杂岩带的TM影像特征 总被引：5，自引：0，他引：5
通过对拉轨岗日地区TM图像进行主成分变换、缨帽变换、比值运算等数字处理, 其成果图像进一步印证了拉轨岗日变质核杂岩带具有典型三层结构这一野外区调成果.通过对系列成果图像的分析对比, 总结了变质核、接触带和盖层的影像特征, 分析了拉轨岗日构造带各变质核杂岩之间的变化规律. 相似文献
9.
《物探化探计算技术》2016,(4)
地球化学元素作为成矿预测一种重要的数据源,其异常分析往往是进行下一步找矿工作的基础,也是确定找矿靶区一种重要技术手段。作者以青海省大柴旦镇柴达木山南坡一带为研究区,通过开展1:10 000土壤地球化学测量,共分析了Au、Cu、Pb、Zn、As、Sb六种元素,采用核主成分分析地球化学元素组合异常,并与主成分分析进行比较分析,从应用结果可以看出核主成分的降维效果更好。从异常等值线图可以看出,采用核主成分的异常区域分布更加集中,所圈定的元素组合异常与已知矿床点在空间上具有良好的对应关系,说明利用核主成分圈定元素组合异常能够较好地确认地球化学元素组合异常。 相似文献
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11.
针对低孔、低渗致密储层识别较常规储层难这一问题,首次应用核主成分分析与支持向量机(KPCA-SVM)模型进行储层识别.该模型先通过核主成分分析(KPCA)进行非线性特征参数提取,然后将提取的特征参数作为支持向量机(SVM)的输入变量,最终实现储层识别.由于KPCA-SVM模型集成了核函数、主成分和支持向量分类机的优点,较好地解决非线性小样本的问题,能消除数据之间的噪音,降低维数,而又不缺失有效信息,达到准确快速预测的功能.将该模型应用到新场须二气藏新856井区储层预测中,预测结果验证了本模型的优越性,可作为致密储层预测的可选方法. 相似文献
12.
Representing Spatial Uncertainty Using Distances and Kernels 总被引：8，自引：7，他引：1
Assessing uncertainty of a spatial phenomenon requires the analysis of a large number of parameters which must be processed
by a transfer function. To capture the possibly of a wide range of uncertainty in the transfer function response, a large
set of geostatistical model realizations needs to be processed. Stochastic spatial simulation can rapidly provide multiple,
equally probable realizations. However, since the transfer function is often computationally demanding, only a small number
of models can be evaluated in practice, and are usually selected through a ranking procedure. Traditional ranking techniques
for selection of probabilistic ranges of response (P10, P50 and P90) are highly dependent on the static property used. In
this paper, we propose to parameterize the spatial uncertainty represented by a large set of geostatistical realizations through
a distance function measuring “dissimilarity” between any two geostatistical realizations. The distance function allows a
mapping of the space of uncertainty. The distance can be tailored to the particular problem. The multi-dimensional space of
uncertainty can be modeled using kernel techniques, such as kernel principal component analysis (KPCA) or kernel clustering.
These tools allow for the selection of a subset of representative realizations containing similar properties to the larger
set. Without losing accuracy, decisions and strategies can then be performed applying a transfer function on the subset without
the need to exhaustively evaluate each realization. This method is applied to a synthetic oil reservoir, where spatial uncertainty
of channel facies is modeled through multiple realizations generated using a multi-point geostatistical algorithm and several
training images. 相似文献
13.
14.
Computing with functions on the rotation group is a task carried out in various areas of application. When it comes to approximation,
kernel based methods are a suitable tool to handle these functions. In this paper, we present an algorithm which allows us
to evaluate linear combinations of functions on the rotation group as well as a truly fast algorithm to sum up radial functions
on the rotation group. These approaches based on nonequispaced FFTs on SO(3) take O(M+N)\mathcal{O}(M+N) arithmetic operations for M and N arbitrarily distributed source and target nodes, respectively. In this paper, we investigate a selection of radial functions
and give explicit theoretical error bounds, as well as numerical examples of approximation errors. Moreover, we provide an
application of our method, namely the kernel density estimation from electron back scattering diffraction (EBSD) data, a problem
relevant in texture analysis. 相似文献
15.
An algorithm for determining the area of a spherical polygon of arbitrary shape is presented. The kernel of the problem is to compute the interior angle at each vertex of the spherical polygon; a well-known relationship between the area of a spherical polygon and the sum of its interior angles then may be exploited. The algorithm has been implemented as a FORTRAN subroutine and a listing is provided. Both algorithm and subroutine are general in their capabilities and can be used, for example, to determine the area of a spherical polygon containing one or more holes. 相似文献
16.
The gravity solid tide signal includes daily wave, half-day wave and annual wave and moon wave harmonic component, but the energy of day wave and half-day wave component is relatively strong, and the energy of annual wave and moon wave component is relatively weak. In order to effectively extract these harmonic components with large energy differences and reveal the modulation relationship between them, according to the cause of gravity tide, a gravity solid tide signal decomposition model is used to compare the tidal harmonic components with different strengths. The form of the independent component is decomposed into different orthogonal directions. At the same time, a new optimization algorithm is used to improve the independent component analysis algorithm and separate the independent components of different orthogonal directions. In the spectral correlation analysis of the components of independent components, the autocorrelation operation will make the strong component stronger and the weak component weaker. For this problem, the cross-correlation spectrum between independent components is used to reveal the gravity tide signal., the modulation relationship between harmonic components. The experimental results show that the proposed algorithm not only effectively separates the independent components with large intensity difference in the gravity tide signal from the perspective of additive decomposition, but also reveals the multiplicative modulation relationship between the corresponding tidal harmonics based on the cross-correlation spectrum. 相似文献
17.
Kernel Principal Component Analysis for Efficient,Differentiable Parameterization of Multipoint Geostatistics 总被引：6，自引：5，他引：1
This paper describes a novel approach for creating an efficient, general, and differentiable parameterization of large-scale
non-Gaussian, non-stationary random fields (represented by multipoint geostatistics) that is capable of reproducing complex
geological structures such as channels. Such parameterizations are appropriate for use with gradient-based algorithms applied
to, for example, history-matching or uncertainty propagation. It is known that the standard Karhunen–Loeve (K–L) expansion,
also called linear principal component analysis or PCA, can be used as a differentiable parameterization of input random fields
defining the geological model. The standard K–L model is, however, limited in two respects. It requires an eigen-decomposition
of the covariance matrix of the random field, which is prohibitively expensive for large models. In addition, it preserves
only the two-point statistics of a random field, which is insufficient for reproducing complex structures.
In this work, kernel PCA is applied to address the limitations associated with the standard K–L expansion. Although widely
used in machine learning applications, it does not appear to have found any application for geological model parameterization.
With kernel PCA, an eigen-decomposition of a small matrix called the kernel matrix is performed instead of the full covariance
matrix. The method is much more efficient than the standard K–L procedure. Through use of higher order polynomial kernels,
which implicitly define a high-dimensionality feature space, kernel PCA further enables the preservation of high-order statistics
of the random field, instead of just two-point statistics as in the K–L method. The kernel PCA eigen-decomposition proceeds
using a set of realizations created by geostatistical simulation (honoring two-point or multipoint statistics) rather than
the analytical covariance function. We demonstrate that kernel PCA is capable of generating differentiable parameterizations
that reproduce the essential features of complex geological structures represented by multipoint geostatistics. The kernel
PCA representation is then applied to history match a water flooding problem. This example demonstrates that kernel PCA can
be used with gradient-based history matching to provide models that match production history while maintaining multipoint
geostatistics consistent with the underlying training image. 相似文献
18.
基于支持向量机分类算法的湖泊水质评价研究 总被引：11，自引：1，他引：10
支持向量机（SVM）是由Vapnik等人提出的建立在统计学习理论基础上的一种小样本机器学习方法,最初用于解决二分类问题。由于使用结构风险最小化原则代替经验风险最小化原则,使它较好地解决了小样本情况下的学习问题。又由于采用了核函数思想,使它将非线性问题转化为线性问题来解决,降低了算法的复杂度。利用支持向量机多类分类算法,构建湖泊水环境评价模型。实验结果表明,该方法能够正确地对湖泊水环境质量进行分类评价。 相似文献
19.
为了更快、更准确地进行瞬变电磁一维正、反演,研究了中心回线装置计算全区视电阻率的平移算法和核函数算法。研究表明,均匀半空间大定源回线的瞬变响应曲线具有平移伸缩特性和核函数特点,可以运用平移算法和核函数算法计算。推导出全区视电阻率的计算公式,通过三层K型、4层KH型地电模型理论计算,对比分析了平移算法和核函数算法的运算速度和误差,结果表明:平移算法的运算速度为0.140 6 s,均方根误差为1.824×10-2,核函数算法的运算速度为3.241 8 s,均方根误差为0.728×10-2,两种方法均能计算大定源回线的全区视电阻率,各有优缺点。 相似文献
20.
针对地震勘探中强随机噪声的去噪问题,引进支持向量回归方法,提出并证明一种新的Ricker子波核函数。支持向量回归采用核映射的基本思想,基于结构风险最小化原则,将回归问题转化为一个二次规划问题。对单道记录或多道记录中任选道的仿真实验表明,与传统的基于径向基核函数的支持向量回归及褶积滤波方法相比,使用本方法去噪后的同相轴更为清晰,波形恢复得更好,信噪比也较高,因此有可能将其应用于地震勘探记录的去噪处理中。 相似文献