地下水污染源解析的貝葉斯監(jiān)測設(shè)計與參數(shù)反演方法
[Abstract]:Groundwater is one of the most important drinking water sources for human beings. However, human activities often cause the groundwater system to be polluted by various pollutants. In order to better manage the groundwater and evaluate the environmental risk of groundwater pollution, we need to use numerical simulation to determine the direction of the pollutant. The key parameters of the groundwater solute transport model, such as the location of the pollution source, the intensity of the pollution source, the permeability coefficient of the aquifer and so on, are often difficult to be obtained directly. It is necessary to obtain the observation data based on the monitoring well to obtain the estimation of it by solving the inverse problem. How to do the optimal design of the monitoring well network It is a hot spot for groundwater hydrology to provide the most valuable observation values, and thus accurately estimate the parameters of the model. In addition, the monitoring design and parameter inversion often require tens of thousands of model calls, which will cause extremely high calculation costs in large scale problems. The source identification of water pollutant migration is the research goal, and the Bayesian uncertainty analysis method based on the alternative system is developed to carry out efficient, accurate monitoring design and parameter inversion. (1) in order to maximize the value of the observed data, we carry out the expectation of the prior to the posterior relative entropy to the objective function. The optimal design of the monitoring well network, in which the maximum sampling position of the target function is the optimal sampling scheme, we use the Markoff Montecalo (Markov chain Monte Carlo, MCMC) method to inverse the unknown model parameters after using the optimal sampling scheme. In order to improve the computational efficiency, we use the self We adapt to the sparse lattice interpolation (adaptivesparsegridinterpolation) method to construct a polynomial substitution system in the prior space of parameters, and apply it to the monitoring design and parameter inversion. It avoids the repeated solution of the original model, that is the control square of the groundwater flow and solute transport. In order to eliminate the error caused by the alternative system, we adopt the method. A two stage MCMC simulation is used to invert the unknown parameters, that is, an alternative system is used to fully explore the posterior distribution of the parameters, and then the original model is used to accurately sample the parameter posterior. The numerical example shows that the method proposed by us can identify the pollution effectively and accurately under the condition of permeability coefficient heterogeneity. Source parameters and osmotic coefficient parameters. (2) in the second stage of the two phase MCMC simulation, we still need to solve the original model many times, so the amount of computation required for the two phase MCMC simulation is still high. In order to further reduce the computational cost, we propose the idea of constructing the alternative system adaptively in the parameter posterior space. Here, I We use the Gauss process (Gaussianprocess, GP) to construct an alternative system, and combine the MCMC simulation with the substitutes in the inversion of the parameters, and increase the accuracy of the alternative system in the parametric posterior space by self adaptively increasing the base point near the posterior. In addition, we are able to quantify the substitution line due to the excellent properties of the GP. The error of the system is reflected in the posterior distribution of the parameters. The results of the numerical simulation show that the process based on the posterior substitution system is more efficient and accurate than the process based on the prior alternative system. (3) in the high dimensional problem, the construction of the alternative system and the effect of the MCMC inversion are not good. And the problem of parameter inversion, we propose a method based on the set (ensemble). We use the data value analysis (data-worth analysis) to find the maximum information sampling scheme, and then use the set smoother (ensemble smoother, ES) to retrieve the model parameters. In order to verify the effect of the method, we tested one of the methods. In this case, we consider 8 unknown pollution source parameters and 3321 unknown osmotic coefficient parameters in this example. Through the design of 12 monitoring time steps, we obtain 24 optimal sampling positions. We can use the 3329 unknown parameters by using the concentration and water head observed on the 24 optimal sampling locations. (4) (4) although the ES algorithm is suitable for the high dimensional case, it is based on the linear estimation theory, and can not solve the inverse problem of multi peak in the parameter distribution. In order to solve the problem of parameter inversion in the case of high dimensional non Gauss, we propose a kind of iterative local updating ensemble smoother called iterative local update set smoothing. In the process of implementing this algorithm, we do not update each sample in the set directly, instead, we update the local sample set of each sample to fully explore the possible multi peak distribution. In addition, in the nonlinear problem, in order to improve the inversion effect, we use one kind in the ILUES algorithm. In simple iterative process,.ILUES algorithm can identify the multi peak distribution of parameters without clustering analysis. In order to verify the effect of the ILUES algorithm, we tested five numerical examples, taking into account the different peaks of the parameters, the posterior multi peak and the high dimension of the parameters respectively. These examples all show the ILUES well. The effect of the algorithm in the parameter inversion of the complex model. Compared with the common MCMC algorithm, the ILUES algorithm has the significant advantage of the computational complexity. (5) because the structure of the replacement system is very inefficient in the high dimensional problem, it greatly limits the application scope of the alternative system. In order to solve this problem, we propose a construction of the substitutes for the substitutes and the substitutes. The idea of combining it and applying it to the assessment and analysis of the risk of groundwater pollution. When estimating the failure probability (i.e. the probability of exceeding the risk value), the direct Monte Carlo (MonteCarlo, MC) simulation usually requires a large number of system models. In order to reduce the cost of the failure probability analysis, people often make the MC simulation Using alternative systems. However, it is very difficult to construct a substituting system directly for the high dimensional groundwater model. Moreover, the use of the alternative system will inevitably introduce errors. In order to solve the above problems, we have proposed a two stage MC simulation method to accurately and effectively carry out the failure probability analysis. In the first stage, we combine the Karhunen-Loe The ve expansion and the piecewise inverse regression (sliced inverseregression) method can fully reduce the parameters of the permeability coefficient of spatial heterogeneity, and on this basis, a more accurate replacement system is constructed by using the chaotic polynomial expansion (polynomial chaos expansion). By using this alternative system, we can effectively calculate the attention of a large number of samples. (quantity of interest, QoI); in the second stage, in order to eliminate the error introduced by the alternative system, we recalculated the QoI value of the samples near the failure boundary with the original model. In this way, we can eliminate the error introduced by the alternative system and get the accurate estimation of the failure probability. In order to verify the effect of the above method, we apply it In the example of a high dimensional groundwater pollutant migration simulation, and the amount of total pollutants flowing downstream as a result of QoI., the two stage MC simulation can be calculated at a cost of less than 1%, and the result is completely consistent with the simulation based on the original model.
【學(xué)位授予單位】:浙江大學(xué)
【學(xué)位級別】:博士
【學(xué)位授予年份】:2017
【分類號】:X523;X832
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