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1.
Nonuniqueness in geophysical inverse problems is naturally resolved by incorporating prior information about unknown models into observed data. In practical estimation procedures, the prior information must be quantitatively expressed. We represent the prior information in the same form as observational equations, nonlinear equations with random errors in general, and treat as data. Then we may define a posterior probability density function of model parameters for given observed data and prior data, and use the maximum likelihood criterion to solve the problem. Supposing Gaussian errors both in observed data and prior data, we obtain a simple algorithm for iterative search to find the maximum likelihood estimates. We also obtain an asymptotic expression of covariance for estimation errors, which gives a good approximation to exact covariance when the estimated model is linearly close to a true model. We demonstrate that our approach is a general extension of various inverse methods dealing with Gaussian data. By way of example, we apply the new approach to a problem of inferring the final rupture state of the 1943 Tottori earthquake (M = 7.4) from coseismic geodetic data. The example shows that the use of sufficient prior information effectively suppresses both the nonuniqueness and the nonlinearity of the problem.  相似文献   

2.
This paper is concerned with developing computational methods and approximations for maximum likelihood estimation and minimum mean square error smoothing of irregularly observed two-dimensional stationary spatial processes. The approximations are based on various Fourier expansions of the covariance function of the spatial process, expressed in terms of the inverse discrete Fourier transform of the spectral density function of the underlying spatial process. We assume that the underlying spatial process is governed by elliptic stochastic partial differential equations (SPDE's) driven by a Gaussian white noise process. SPDE's have often been used to model the underlying physical phenomenon and the elliptic SPDE's are generally associated with steady-state problems.A central problem in estimation of underlying model parameters is to identify the covariance function of the process. The cumbersome exact analytical calculation of the covariance function by inverting the spectral density function of the process, has commonly been used in the literature. The present work develops various Fourier approximations for the covariance function of the underlying process which are in easily computable form and allow easy application of Newton-type algorithms for maximum likelihood estimation of the model parameters. This work also develops an iterative search algorithm which combines the Gauss-Newton algorithm and a type of generalized expectation-maximization (EM) algorithm, namely expectation-conditional maximization (ECM) algorithm, for maximum likelihood estimation of the parameters.We analyze the accuracy of the covariance function approximations for the spatial autoregressive-moving average (ARMA) models analyzed in Vecchia (1988) and illustrate the performance of our iterative search algorithm in obtaining the maximum likelihood estimation of the model parameters on simulated and actual data.  相似文献   

3.
利用最小二乘配置进行地壳形变分析,其结果的合理性关键在于经验协方差函数的拟合.考虑到观测数据存在粗差的情况,提出基于观测值中位数初值的抗差最小二乘配置方法和基于中位参数法的抗差最小二乘配置方法.两种方法首先分别利用观测值中位数给出观测值初始权阵以及利用中位参数法给出最小二乘配置初始解,然后均在给定协方差函数参数初始值的情况下,应用合适的等价权进行抗差估计并通过迭代计算,最终获得稳健的协方差函数参数估值及最小二乘配置解.利用本文提出的两种方法以及传统方法分别对庐山地震的GPS垂直位移数据和意大利L'Aquila地震的InSAR同震位移数据进行处理分析.结果表明:相对传统方法,基于观测值中位数初值的抗差最小二乘配置方法效果更好,更具稳健性.  相似文献   

4.
 The efficiency of a sequential data assimilation scheme relies on the capability to describe the error covariance. This aspect is all the more relevant if one needs accurate statistics on the estimation error. Frequently an ad hoc function depending on a few parameters is proposed, and these parameters are tuned, estimated or updated. This usually requires that the covariance is second-order stationary (i.e. depends only on the distance between two points). In this paper, we discuss this feature and show that even in simple applications (such as one-dimensional hydrodynamics), this assumption does not hold and may lead to poorly described estimation errors. We propose a method relying on the analysis of the error term and the use of the hydrodynamical model to generate one part of the covariance function, the other part being modeled using a second-order stationary approach. This method is discussed using a twin experiment in the case where a physical parameter is erroneous, and improves significantly the results: the model bias is strongly reduced and the estimation error is well described. Moreover, it enables a better adaptation of the Kalman gain to the actual estimation error.  相似文献   

5.
— Velocity evaluation is a key step in seismic analysis. The covariance of the true velocity field must be known when interpolating or simulating velocities from well measurements using geostatistical methods. In addition, inversion procedures often require information pertaining to this covariance. Traditionally it has been taken to be the covariance of stacking velocities. We present a simple example to show that this approximation can lead to significant errors. Better methods, such as those of Touati (1996) and Iooss (1998), use the variance of prestack picked travel times as a function of offset to infer that of the velocities. In this paper we extend their results on the estimation of the covariance of the reflected traveltimes, and obtain an explicit expression for the covariance of the square of the stacking slowness as a function of the covariance of the velocities. Although we are not able to invert the formula analytically to yield an explicit estimator for these parameters, the results obtained using it furnish a good and quick estimation of the velocity's covariance. This is illustrated with synthetic examples.  相似文献   

6.
On the geostatistical approach to the inverse problem   总被引：5，自引：0，他引：5  
The geostatistical approach to the inverse problem is discussed with emphasis on the importance of structural analysis. Although the geostatistical approach is occasionally misconstrued as mere cokriging, in fact it consists of two steps: estimation of statistical parameters (“structural analysis”) followed by estimation of the distributed parameter conditional on the observations (“cokriging” or “weighted least squares”). It is argued that in inverse problems, which are algebraically undetermined, the challenge is not so much to reproduce the data as to select an algorithm with the prospect of giving good estimates where there are no observations. The essence of the geostatistical approach is that instead of adjusting a grid-dependent and potentially large number of block conductivities (or other distributed parameters), a small number of structural parameters are fitted to the data. Once this fitting is accomplished, the estimation of block conductivities ensues in a predetermined fashion without fitting of additional parameters. Also, the methodology is compared with a straightforward maximum a posteriori probability estimation method. It is shown that the fundamental differences between the two approaches are: (a) they use different principles to separate the estimation of covariance parameters from the estimation of the spatial variable; (b) the method for covariance parameter estimation in the geostatistical approach produces statistically unbiased estimates of the parameters that are not strongly dependent on the discretization, while the other method is biased and its bias becomes worse by refining the discretization into zones with different conductivity.  相似文献   

7.
To date, an outstanding issue in hydrologic data assimilation is a proper way of dealing with forecast bias. A frequently used method to bypass this problem is to rescale the observations to the model climatology. While this approach improves the variability in the modeled soil wetness and discharge, it is not designed to correct the results for any bias. Alternatively, attempts have been made towards incorporating dynamic bias estimates into the assimilation algorithm. Persistent bias models are most often used to propagate the bias estimate, where the a priori forecast bias error covariance is calculated as a constant fraction of the unbiased a priori state error covariance. The latter approach is a simplification to the explicit propagation of the bias error covariance. The objective of this paper is to examine to which extent the choice for the propagation of the bias estimate and its error covariance influence the filter performance. An Observation System Simulation Experiment (OSSE) has been performed, in which ground water storage observations are assimilated into a biased conceptual hydrologic model. The magnitudes of the forecast bias and state error covariances are calibrated by optimizing the innovation statistics of groundwater storage. The obtained bias propagation models are found to be identical to persistent bias models. After calibration, both approaches for the estimation of the forecast bias error covariance lead to similar results, with a realistic attribution of error variances to the bias and state estimate, and significant reductions of the bias in both the estimates of groundwater storage and discharge. Overall, the results in this paper justify the use of the traditional approach for online bias estimation with a persistent bias model and a simplified forecast bias error covariance estimation.  相似文献   

8.
Modern methods of geostatistics deliver an essential contribution to Environmental Impact Assessment (EIA). These methods allow for spatial interpolation, forecast and risk assessment of expected impact during and after mining projects by integrating different sources of data and information. Geostatistical estimation and simulation algorithms are designed to provide both, a most likely forecast as well as information about the accuracy of the prediction. The representativeness of these measures depends strongly on the quality of the inferred model parameters, which are mainly defined by the parameters of the variogram or the covariance function. Available data may be sparse, trend affected and of different data type making the inference of representative geostatistical model parameters difficult. This contribution introduces a new method for best fitting of the geostatistical model parameters in the presence of a trend, which utilizes the empirical and theoretical differences between Universal Kriging and trend-predictions. The method extends well known approaches of cross validation in two aspects. Firstly, the model evaluation is not only limited to sample data locations but is performed on any prediction locations of the attribute in the domain. Secondly, it extends the measure used in cross validation, based on a single point replacement by using error curves. These allow defining rings of influence representing errors resulting from separate variogram lags. By analyzing the different variogram lags the fit of the complete covariance can be assessed and the influence of the several model parameters separated. The use of the proposed method in an EIA context is illustrated in a case study related on the prediction of mining-induced ground movements.  相似文献   

9.
Gravity field and steady-state Ocean Circulation Explorer (GOCE) is the first satellite mission that observes gravity gradients from the space, to be primarily used for the determination of high precision global gravity field models. However, the GOCE gradients, having a dense data distribution, may potentially provide better predictions of the regional gravity field than those obtained using a spherical harmonic Earth Geopotential Model (EGM). This is investigated in Auvergne test area using Least Squares Collocation (LSC) with GOCE vertical gravity gradient anomalies (Tzz), removing the long wavelength part from EGM2008 and the short wavelength part by residual terrain modelling (RTM). The results show that terrain effects on the vertical gravity gradient are significant at satellite altitude, reaching a level of 0.11 E?tv?s unit (E.U.) in the mountainous areas. Removing the RTM effects from GOCE Tzz leads to significant improvements on the LSC predictions of surface gravity anomalies and quasigeoid heights. Comparison with ground truth data shows that using LSC surface free air gravity anomalies and quasi-geoid heights are recovered from GOCE Tzz with standard deviations of 11 mGal and 18 cm, which is better than those obtained by using GOCE EGMs, demonstrating that information beyond the maximal degree of the GOCE EGMs is present. Investigation of using covariance functions created separately from GOCE Tzz and terrestrial free air gravity anomalies, suggests that both covariance functions give almost identical predictions. However, using covariance function obtained from GOCE Tzz has the effect that the predicted formal average error estimates are considerably larger than the standard deviations of predicted minus observed gravity anomalies. Therefore, GOCE Tzz should be used with caution to determine the covariance functions in areas where surface gravity anomalies are not available, if error estimates are needed.  相似文献   

10.
The objective of this note is to assess the accuracy of the half-power bandwidth method for the estimation of damping ratios in single- and multi-degree-of-freedom structures with linear viscous damping including those that do not possess classical normal modes. This is done by performing some numerical experiments involving single- and multi-degree-of-freedom structures. It is found that the use of the half-power bandwidth method in its classical form may lead to significant errors, while a third order correction to this classical form provides conservative and more reliable results. It is also found in conjunction with this correction that application of the half-power bandwidth method should be performed to the acceleration frequency response transfer function of the structure on a mode-to-mode basis and that its damping ratio estimates for higher modes should always be viewed with caution.  相似文献   

11.
ABSTRACT

The estimation of recharge and boundary flux is an important problem in deterministic groundwater modelling since these quantities are often difficult to measure directly in the field. A new method (inferred recharge) is proposed for this estimation, based upon water level and transmissivity observations; it is particularly applicable to those semiarid regions where a steady state formulation can be used and recharge is confined to certain known areas. The inferred recharge method which assumes that recharge occurs in these areas only is compared with an alternative method which does not use this information, and is found to be superior. In the example of the inferred recharge method applied to the Oman coastal plain, random normal errors have been added to both water levels and transmissivities in simulation experiments to assess the effect of errors in the data. The results of the simulations have been used to test the reliability of variance estimates derived from the theory. In addition, statistical tests have been used to examine the inferred recharge results.  相似文献   

12.
Vertical gravity gradient anomalies from the Gravity and steady-state Ocean Circulation Explorer (GOCE) DIR-3 model have been used to determine gravity anomalies in mid-west Greenland by using Least-Squares Collocation (LSC) and the Reduced Point Mass (RPM) method. The two methods give nearly identical results. However, compared to LSC, the RPM method needs less computational time as the number of equations to be solved in LSC equals the number of observations. The advantage of the LSC, however, is the acquired error estimates. The observation periods are winter 2009 and summer 2012. In order to enhance the accuracy of the calculated gravity anomalies, ground gravity data from West Greenland is used over locations where the gravity change resulting from ice mass changes is negligible, i.e. over solid rock. In the period considered, the gravity anomaly change due to changes in ice mass varies from ?5 mGal to 4 mGal. It is negative over the outlet glacier Jacobshavn Isbræ, where the mass loss corresponds to a gravity change of approximately ?4 mGal. When using only GOCE vertical gravity gradients, the error estimates range from 5 mGal at the coast to 17 mGal over the ice sheet. Introducing the ground gravity data from West Greenland in the prediction reduces the errors to range from 2 to 10 mGal.  相似文献   

13.
The transient flowmeter test (TFMT) provides more information about the well–aquifer system than the traditional quasi-steady-state flowmeter test (QFMT). The TFMT duration may be much shorter than that of a QFMT, which is desirable at highly contaminated sites where the extracted water has to be treated as hazardous waste. Here we present the TFMT model that accounts for inter-layer crossflow, a thick skin surrounding the well, and wellbore storage. The model is derived under the simplifying assumptions of the pseudo-steady-state inter-layer crossflow and the uniform wellface flux within each layer. The semi-analytic solution is inverted numerically from the Laplace domain to the time domain. Layer and skin parameters are estimated from the TFMT data via the modified Levenberg–Marquardt algorithm. The estimation is robust when the initial parameter guesses are close to their true values. Otherwise, a computationally expensive search among the local minima of the objective function is necessary to find the parameter estimates. The modeling errors and the associated parameter estimation errors are evaluated in a number of synthetic TFMTs and compared to the corresponding results obtained with a general numerical model that relaxes the two simplifying assumptions. The TFMT provides reasonably accurate estimates of hydraulic conductivities for the aquifer layers and the damaged skins and order-of-magnitude estimates of layer specific storativities and hydraulic conductivities for the normal skin. The skin specific storativities should not be estimated from a TFMT. Multi-rate TFMTs with a step-variable pumping rate yield significantly more accurate parameters than constant-pumping-rate TFMTs. The calculated modeling errors may be useful in estimating the magnitude of parameter estimation errors from the TFMT. Our field tests in a coastal aquifer at the Lizzie Site in North Carolina (USA) demonstrate the feasibility of a TFMT for aquifer characterization. The downhole hydraulic conductivity profiles from our field and synthetic TFMTs are consistent with the corresponding profiles from QFMTs.  相似文献   

14.
The transformation from the gravimetric to the GPS/levelling-derived geoid using additional gravity information for the covariance function of geoid height differences has been investigated in a test area in south-western Canada. A “corrector surface” model, which accounts for datum inconsistencies, long-wavelength geoid errors, vertical network distortions and GPS errors, has been constructed using least-squares collocation. The local covariance function of geoid height differences is usually obtained from residual values between the GPS/levelling and gravimetric geoid heights after the elimination of all known systematic distortions. If additional gravity data (in the form of gravity anomalies) are available, the covariance function of geoid height differences can be determined by the following steps: (1) transforming the GPS/levelling-derived geoid heights into gravity anomalies; (2) forming differences between the computed in step 1 and given gravity anomalies; (3) determining the parameters of the local covariance function of the gravity anomaly differences; (4) constructing an analytical covariance model for the geoid height differences from the covariance function of the gravity anomaly differences using the parameters derived in step 3. The advantage of the proposed method stems from the great number of gravity data used to derive the empirical covariance function. A comparison with the least-squares adjustment shows that the standard deviation of the residuals of the predicted geoid height differences with respect to the control point values decreases by 2.4 cm.  相似文献   

15.
Hydrological model and observation errors are often non-Gaussian and/or biased, and the statistical properties of the errors are often unknown or not fully known. Thus, determining the true error covariance matrices is a challenge for data assimilation approaches such as the most widely used Kalman filter (KF) and its extensions, which assume Gaussian error nature and need fully known error statistics. This paper introduces H-infinite filter (HF) to hydrological modeling and compares HF with KF under various model and observation error conditions. HF is basically a robust version of KF. When model performance is not well known, or changes unpredictably, HF may be preferred over KF. HF is especially suitable for the cases where the estimation performance in the worst error case needs to be guaranteed. Through the application of HF to a hypothetical hydrologic model, this paper shows that HF is less sensitive to the uncertainty in the initial condition, corrects system bias more effectively, and converges to true state faster after interruptions than KF. In particular, HF performs better in dealing with instant human inputs (irrigation is used as an example), which are characterized by non-stationary, non-Gaussian and not fully known errors. However HF design can be more difficult than KF design due to the sensitivity of HF performance to design parameters (weights for model and observation error terms). Through sensitivity analysis, this paper shows the existence of a certain range of those parameters, in which the “best” value of the parameters is located. The tuning of HF design parameters, which can be based on users’ prior knowledge on the nature of model and observation errors, is critical for the implementation of HF.  相似文献   

16.
The properties of linear spatial interpolators of single realizations and trend components of regionalized variables are examined in this work. In the case of the single realization estimator explicit and exact expressions for the weighting vector and the variances of estimator and estimation error were obtained from a closed-form expression for the inverse of the Lagrangian matrix. The properties of the trend estimator followed directly from the Gauss-Markoff theorem. It was shown that the single realization estimator can be decomposed into two mutually orthogonal random functions of the data, one of which is the trend estimator. The implementation of liear spatial estimation was illustrated with three different methods, i.e., full information maximum likelihood (FIML), restricted maximum likelihood (RML), and Rao's minimum norm invariant quadratic unbiased estimation (MINQUE) for the single realization case and via generalized least squares (GLS) for the trend. The case study involved large correlation length-scale in the covariance of specific yield producing a nested covariance structure that was nearly positive semidefinite. The sensitivity of model parameters, i.e., drift and variance components (local and structured) to the correlation length-scale, choice of covariance model (i.e., exponential and spherical), and estimation method was examined. the same type of sensitivity analysis was conducted for the spatial interpolators. It is interesting that for this case study, characterized by a large correlation length-scale of about 50 mi (80 km), both parameter estimates and linear spatial interpolators were rather insensitive to the choice of covariance model and estimation method within the range of credible values obtained for the correlation length-scale, i.e., 40–60 mi (64–96 km), with alternative estimates falling within ±5% of each other.  相似文献   

17.
Mapping geomorphic variables geostatistically, specifically by kriging, runs into difficulties when there is trend. The reason is that the variogram required for the kriging must be of residuals from any trend, which in turn cannot be estimated optimally by the usual method of trend surface analysis because the residuals are correlated. The difficulties can be overcome by the use of residual maximum likelihood (REML) to estimate both the trend and the variogram of the residuals simultaneously. We summarize the theory of REML as it applies to kriging in the presence of trend. We present the equations to show how estimates of the trend are combined with kriging of residuals to give empirical best linear unbiased predictions (E‐BLUPs). We then apply the method to estimate the height of the sub‐Upper‐Chalk surface beneath the Chiltern Hills of southeast England from 238 borehole data. The variogram of the REML residuals is substantially different from that computed by ordinary least squares (OLS) analysis. The map of the predicted surface is similar to that made from kriging with the OLS variogram. The variances, however, are substantially larger because (a) they derive from a variogram with a much larger sill and (b) they include the uncertainty of the estimate of the trend. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

18.
The errors-in-variables (EIV) model is a nonlinear model, the parameters of which can be solved by singular value decomposition (SVD) method or the general iterative algorithm. The existing formulae for covariance matrix of total least squares (TLS) parameter estimates don’t fully consider the randomness of quantities in iterative algorithm and the biases of parameter estimates and residuals. In order to reflect more reasonable precision information for TLS adjustment, the derivative-free unscented transformation with scaled symmetric sampling strategy, i.e. scaled unscented transformation (SUT), is introduced and implemented. In this contribution, we firstly discuss the existing various solutions of TLS adjustment and covariance matrices of TLS parameter estimates and derive the general first-order approximate cofactor matrices of random quantities in TLS adjustment. Secondly, based on the combination of TLS iterative algorithm and calculation process of SUT, we design the two SUT algorithms to calculate the biases and the second-order approximate covariance matrices. Finally, the straight line fitting model and plane coordinate transformation model are used to demonstrate that applying SUT for precision estimation of TLS adjustment is feasible and effective.  相似文献   

19.
Incorporating flow in the covariance function is important for geostatistical water quality estimation that accounts for hydrological transport. Very few studies have successfully incorporated flow due to various reasons including implementation difficulties. To address this critical issue, we introduce here the first implementation of a flow weighted covariance model that uses gradual flow, and we use this model in a novel hybrid Euclidean/Gradual-flow covariance model to estimate fecal coliform along the Haw and Deep rivers from 2006 to 2010. The hybrid Euclidean/Gradual-flow model results in a 12.4% reduction in estimation mean square error compared to the Euclidean model, indicating that this is the first study to successfully incorporate gradual flow and demonstrate an improvement in estimation accuracy over the purely Euclidean approach. Furthermore, results show that the Euclidean/Gradual-flow model is more accurate and easier to implement than the Euclidean/Pipe-flow model. Our assessment found that 96 river miles were detected as being impaired according to the Euclidean/Gradual-flow method, which is more than twice the 39 river miles found according to the Euclidean estimate. These results demonstrate that the Euclidean/Gradual-flow model substantially increase the sensitivity in detecting fecal impairment, which provide critical new information for watershed management and public health protection measures.  相似文献   

20.
建立了利用扰动重力梯度张量Tzz分量和Txx+Tyy、Tzz-Txx-Tyy组合分量确定地球重力场的调和分析法模型,进一步推导了扰动重力梯度张量对角线三分量的自协方差和互协方差函数的级数展开式,推导了单分量、组合分量与重力位系数之间协方差函数的实用计算公式,给出了利用单分量和组合分量解算地球重力场模型的最小二乘配置法基本原理公式.结果表明,最小二乘配置法具有一定的抗差能力,随着观测数据误差的不断增大,其恢复的重力场模型有效阶次不断降低,精度也不断下降;Tzz-Txx-Tyy组合分量解算重力场模型的精度最高,其次为Tzz分量,Txx+Tyy组合分量最差.  相似文献   

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