共查询到20条相似文献，搜索用时 640 毫秒
1.
2.
频带比率是预处理卫星图像的一种有用方法,特别是对于地形影响非常重要的地区。频带比率方法首先是估计和消除光径辐射率。本文论述了一种估计光径辐射率最小方差比(MRV)的一种新方法。利用MRV技术时,基本优点是在频带比率区有最小的方差。因此,从所给出的最均匀的比率图像角度考虑,所推导出的估算是最理想的。进一步MRV方法的假设比协方差矩阵法所做假设更一般化。而且计算时间与协方差法或回归法一样,这种方法可以应用于一个大图像的某些小部分,以提供对光径辐射率空间变化的估计。本文还讨论了MRV法的误差分析。其设计满足计算机模拟。已证实光径辐射率估值的误差为正值,它受地形、噪声强度、不同波长频带地面内在辐射率比值等影响。在地形影响很强、噪声强度低、内在辐射率之比(较短波长频带与较长波长频带之比)小时,偏差相当小。 相似文献
3.
集合卡尔曼滤波(Ensemble Kalman Filter,EnKF)作为一种有效的数据同化方法,在众多数值实验中体现优势的同时,也暴露了它使用小集合估计协方差情况下精度较低的缺陷。为了降低取样噪声对协方差估计的干扰并提高滤波精度,应用局域化函数对小集合估计的协方差进行修正,即在协方差矩阵中以舒尔积的形式增加空间距离权重以限制远距离相关。在一个二维理想孔隙承压含水层模型中的运行结果表明,局域化对集合卡尔曼滤波估计地下水参数的修正十分有效,局域化可以很好地过滤小集合估计中噪声的影响,节省计算量的同时又可以防止滤波发散。相关长度较小的水文地质参数(如对数渗透系数)更容易受到噪声的干扰,更有必要进行局域化修正。 相似文献
4.
陈伯茂 《物探化探计算技术》1984,(3)
地质统计学将区域化变量分为两类,一类是空间的实函数,诸如矿石品位、矿体厚度、累积量、密度等参数;而另一类却是空间的几何场,比如矿体的体积、面积及样品的支撑等。两者彼此相关,但在研究这两类变量时,自然采用不同的理论与方法。对于前者,采用内蕴理论,它是随机函数概念的具体应用,用概率进行表达,同时需要平稳假设(内蕴假设),用变异函数来描述变量的空间变化。而后者,我们却采用跃迁理论来研究,利用变程协方差函数来刻划它们空间的变化。这种理论在空间变量的正则化、金属量的总体估计以及确定矿体面积和体积等方面都有着广泛的应用。 相似文献
5.
对于具有已知对称非负定协方差阵的一般随机效应模型,通过比较回归系数和参数线性可估函数的最优线性无偏估计方差的大小,给出了一个随机效应模型至少与另一个随机效应模型一样好的定义,并在可估空间的子空间上对两个随机效应模型进行比较,通过利用矩阵的广义逆理论和方法,得到了一个随机效应模型至少与另一个随机效应模型一样好的充分必要条件,为统计建模过程中模型选择奠定了理论依据. 相似文献
6.
EnKF同化的背景误差协方差矩阵局地化对比研究 总被引：1，自引：1，他引：0
在集合数据同化中,背景场误差的协方差估计特别重要。通常有限个成员的集合在估计背景误差协方差矩阵时会引入伪相关,从而造成协方差被低估、滤波发散。虽然协方差膨胀的经验性方法能一定程度缓解协方差被低估的问题,但不能消除协方差的伪相关问题。因此,结合EnKF方案探讨2种消除伪相关的局地化方法(协方差局地化方法和局地分析方法),分析这2种局地化方法对背景误差协方差矩阵、增益矩阵、集合转换矩阵以及同化结果的影响。实验结果表明:局地化方法不仅能消除背景误差协方差矩阵的伪相关,还可以增加背景误差协方差矩阵的秩;在"弱"同化强度下,2种局地化方法的增益矩阵和集合转换矩阵相等;随着同化强度的增大,增益矩阵和集合转换矩阵的差异会变大;在不同的同化强度下,2种局地化方法各具特色,相对而言,协方差局地化方法在更新集合均值和集合扰动上具有较强的鲁棒性。研究结论有助于背景场误差协方差的精细分析和估计。 相似文献
7.
《吉林大学学报(地球科学版)》1991,(1)
用石墨炉原子吸收法测定锗的困难在于,试样中的锗在原子化之前以易挥发的GeO(g)形式损失掉,使锗的测定灵敏度降低。许多作者用涂锆的石墨管改善锗的测定,选用不同的基体改进剂改善测定锗的条件。笔者研究了用钼酸铵浸渍处理石墨管,硝酸镍作为基体改进剂,石墨炉原子吸收法测定锗时的最佳条件。用涂钼管有效地避免了锗在原子化之前以挥发性GeO(g)形式的损失,延长管的使用寿命,可用于直接测定矿泉 相似文献
8.
给出了BCK代-数上(λ1,λ2)-广义模糊关联理想,模糊关联理想的概念并在此定义下推出了它们的一些重要性质。 相似文献
9.
10.
提出了多变量分形体的数学模型,共可由各变量的自协方差函 互协方差函数应用麦夯脱法进行迭代求解,以获得多变量本性组合系数ｂ１、ｂ２,．．．ｂｐ,以及分形害虫律截距ａ和ｇｒａｐｈｆ的盒维数ｓ等未知的待定参数。适应用于其多变量线性组合的自相关函数满足害虫律且目标变量的自协方差函数获得最佳估计的实际问题。 相似文献
11.
Michael L. Stein 《Mathematical Geology》1986,18(7):625-633
Marshall and Mardia (1985) and Kitanidis (1985) have suggested using minimum norm quadratic estimation as a method to estimate parameters of a generalized covariance function. Unfortunately, this method is difficult to use with large data sets as it requires inversion of an n × n matrix, where n is number of observations. These authors suggest replacing the matrix to be inverted by the identity matrix, which eliminates the computational burden, although with a considerable loss of efficiency. As an alternative, the data set can be broken into subsets, and minimum norm quadratic estimates of parameters of the generalized covariance function can be obtained within each subset. These local estimates can be averaged to obtain global estimates. This procedure also avoids large matrix inversions, but with less loss in efficiency. 相似文献
12.
ABSTRACTThe ground is one of the most highly variable of all engineering materials. As a result, geotechnical designs depend upon a site investigation to estimate the ability of the ground to perform acceptably. For example, when a shallow foundation is being proportioned to avoid a bearing capacity failure under a certain applied load, the frictional and cohesive properties of the ground under the foundation must first be estimated through a site investigation. Questions which arise are: How does the quality and intensity of the site investigation affect the design? Is more investigation cost effective? If the site is sampled at one location and the foundation placed at a different location, how does this mismatch affect the target design and the reliability of the final foundation? By modelling the ground as a spatially variable material, questions such as the above can be investigated through Monte Carlo simulation and sometimes theoretical probabilistic models. Using such tools, this paper looks specifically at how the intensity (frequency and spatial distribution) of a site sampling plan, and how the samples are used, affects the understanding of the ground properties under a foundation. Interestingly, it is found that removing the sample mean outperforms removing the best linear unbiased estimate (BLUE) when the actual field correlation length is small but the BLUE correlation length is assumed equal to the field size. Recommendations are made regarding number of samples and the type of trend to best characterise the field.Abbreviations: BLUE: best linear unbiased estimate; MCS: Monte Carlo simulation; LAS: local average subdivision 相似文献
13.
Quadratic estimators of components of a nested spatial covariance function are presented. Estimators are unbiased and possess a minimum norm property. Inversion of a covariance matrix is required but, by assuming that spatial correlation is absent, a priori, matrix inversion can be avoided. The loss of efficiency that results from this assumption is discussed. Methods can be generalized to include estimation of components of a generalized polynomial covariance assuming the underlying process to be an intrinsic random function. Particular attention is given to the special case where just two components of spatial covariance exist, one of which represents a nugget effect. 相似文献
14.
Bayesian estimation of reservoir properties—effects of uncertainty quantification of 4D seismic data
Kjersti Solberg Eikrem Geir Nævdal Morten Jakobsen Yan Chen 《Computational Geosciences》2016,20(6):1211-1229
This paper shows a history matching workflow with both production and 4D seismic data where the uncertainty of seismic data for history matching comes from Bayesian seismic waveform inversion. We use a synthetic model and perform two seismic surveys, one before start of production and the second after 1 year of production. From the first seismic survey, we estimate the contrast in slowness squared (with uncertainty) and use this estimate to generate an initial estimate of porosity and permeability fields. This ensemble is then updated using the second seismic survey (after inversion to contrasts) and production data with an iterative ensemble smoother. The impact on history matching results from using different uncertainty estimates for the seismic data is investigated. From the Bayesian seismic inversion, we get a covariance matrix for the uncertainty and we compare using the full covariance matrix with using only the diagonal. We also compare with using a simplified uncertainty estimate that does not come from the seismic inversion. The results indicate that it is important not to underestimate the noise in seismic data and that having information about the correlation in the error in seismic data can in some cases improve the results. 相似文献
15.
Ensemble size is critical to the efficiency and performance of the ensemble Kalman filter, but when the ensemble size is small,
the Kalman gain generally cannot be well estimated. To reduce the negative effect of spurious correlations, a regularization
process applied on either the covariance or the Kalman gain seems to be necessary. In this paper, we evaluate and compare
the estimation errors when two regularization methods including the distance-dependent localization and the bootstrap-based
screening are applied on the covariance and on the Kalman gain. The investigations were carried out through two examples:
1D linear problem without dynamics but for which the true Kalman gain can be computed and a 2D highly nonlinear reservoir
fluid flow problem. The investigation resulted in three primary conclusions. First, if localizations of two covariance matrices
are not consistent, the estimate of the Kalman gain will generally be poor at the observation location. The consistency condition
can be difficult to apply for nonlocal observations. Second, the estimate of the Kalman gain that results from covariance
regularization is generally subject to greater errors than the estimate of the Kalman gain that results from Kalman gain regularization.
Third, in terms of removing spurious correlations in the estimation of spatially correlated variables, the performance of
screening Kalman gain is comparable as the performance of localization methods (applied on either covariance or Kalman gain),
but screening Kalman gain outperforms the localization methods in terms of generality for application, as the screening method
can be used for estimating both spatially correlated and uncorrelated variables, and moreover, no assumption about the prior
covariance is required for the screening method. 相似文献
16.
We introduce a novel, time-dependent inversion scheme for resolving temporal reservoir pressure drop from surface subsidence
observations (from leveling or GPS data, InSAR, tiltmeter monitoring) in a single procedure. The theory is able to accommodate
both the absence of surface subsidence estimates at sites at one or more epochs as well as the introduction of new sites at
any arbitrary epoch. Thus, all observation sites with measurements from at least two epochs are utilized. The method uses
both the prior model covariance matrix and the data covariance matrix, which incorporates the spatial and temporal correlations
between model parameters and data, respectively. The incorporation of the model covariance implicitly guarantees smoothness
of the model estimate, while maintaining specific geological features like sharp boundaries. Taking these relations into account
through the model covariance matrix enhances the influence of the data on the inverted model estimate. This leads to a better
defined and interpretable model estimate. The time-dependent aspect of the method yields a better constrained model estimate
and makes it possible to identify non-linear acceleration or delay in reservoir compaction.
The method is validated by a synthetic case study based on an existing gas reservoir with a highly variable transmissibility
at the free water level. The prior model covariance matrix is based on a Monte Carlo simulation of the geological uncertainty
in the transmissibility. 相似文献
17.
Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications 总被引：1，自引：0，他引：1
Sampling errors can severely degrade the reliability of estimates of conditional means and uncertainty quantification obtained
by the application of the ensemble Kalman filter (EnKF) for data assimilation. A standard recommendation for reducing the
spurious correlations and loss of variance due to sampling errors is to use covariance localization. In distance-based localization,
the prior (forecast) covariance matrix at each data assimilation step is replaced with the Schur product of a correlation
matrix with compact support and the forecast covariance matrix. The most important decision to be made in this localization
procedure is the choice of the critical length(s) used to generate this correlation matrix. Here, we give a simple argument
that the appropriate choice of critical length(s) should be based both on the underlying principal correlation length(s) of
the geological model and the range of the sensitivity matrices. Based on this result, we implement a procedure for covariance
localization and demonstrate with a set of distinctive reservoir history-matching examples that this procedure yields improved
results over the standard EnKF implementation and over covariance localization with other choices of critical length. 相似文献
18.
Estimating observation error covariance matrix of seismic data from a perspective of image denoising
Estimating observation error covariance matrix properly is a key step towards successful seismic history matching. Typically, observation errors of seismic data are spatially correlated; therefore, the observation error covariance matrix is non-diagonal. Estimating such a non-diagonal covariance matrix is the focus of the current study. We decompose the estimation into two steps: (1) estimate observation errors and (2) construct covariance matrix based on the estimated observation errors. Our focus is on step (1), whereas at step (2) we use a procedure similar to that in Aanonsen et al. 2003. In Aanonsen et al. 2003, step (1) is carried out using a local moving average algorithm. By treating seismic data as an image, this algorithm can be interpreted as a discrete convolution between an image and a rectangular window function. Following the perspective of image processing, we consider three types of image denoising methods, namely, local moving average with different window functions (as an extension of the method in Aanonsen et al. 2003), non-local means denoising and wavelet denoising. The performance of these three algorithms is compared using both synthetic and field seismic data. It is found that, in our investigated cases, the wavelet denoising method leads to the best performance in most of the time. 相似文献
19.
Generalized covariance functions in estimation 总被引：3，自引：0，他引：3
Peter K. Kitanidis 《Mathematical Geology》1993,25(5):525-540
I discuss the role of generalized covariance functions in best linear unbiased estimation and methods for their selection. It is shown that the experimental variogram (or covariance function) of the detrended data can be used to obtain a preliminary estimate of the generalized covariance function without iterations and I discuss the advantages of other parameter estimation methods. 相似文献
20.
Although there are multiple methods for modeling matrix covariance functions and matrix variograms in the geostatistical literature, the linear coregionalization model is still widely used. In particular it is easy to check to ensure whether the matrix covariance function is positive definite or that the matrix variogram is conditionally negative definite. One of the difficulties in using a linear coregionalization model is in determining the number of basic structures and the corresponding covariance functions or variograms. In this paper, a new procedure is given for identifying the basic structures of the space–time linear coregionalization model and modeling the matrix variogram. This procedure is based on the near simultaneous diagonalization of the sample matrix variograms computed for a set of spatiotemporal lags. A case study using a multivariate spatiotemporal data set provided by the Environmental Protection Agency of Lombardy, Italy, illustrates how nearly simultaneous diagonalization of the empirical matrix variograms simplifies modeling of the matrix variograms. The new methodology is compared with a previous one by analyzing various indices and statistics. 相似文献