本文選題：逆模擬　切入點(diǎn)：正態(tài)變換　出處：《中國地質(zhì)大學(xué)(北京)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：集合卡爾曼濾波(EnKF)已被證明是一種非常高效的能夠刻畫滿足非線性狀態(tài)方程和高斯分布條件下的水文滲透系數(shù)場的逆方法。但是它不足之處是在于其難于刻畫非高斯參數(shù)場。本論文的研究主題就是如何合理的刻畫非高斯水文滲透系數(shù)場。首先,我們提出了一種新的隨機(jī)逆方法-逆序貫?zāi)M(iSS)。iSS借鑒了序貫?zāi)M算法和正態(tài)卡爾曼濾波算法中的某些概念。該方法通過滲透系數(shù)場實(shí)現(xiàn)集合和承壓水位場實(shí)現(xiàn)集合來計算兩者之間的非平穩(wěn)交叉協(xié)方差,之后再利用序貫?zāi)M來生成參數(shù)變量實(shí)現(xiàn)。我們運(yùn)用正態(tài)轉(zhuǎn)換技術(shù)來確保變量呈邊緣高斯分布。之后,基于滲透系數(shù)條件點(diǎn)數(shù)據(jù)和承壓水位條件點(diǎn)數(shù)據(jù),用標(biāo)準(zhǔn)多變量序貫高斯條件模擬來更新滲透系數(shù)場實(shí)現(xiàn)。設(shè)定正態(tài)集合卡爾曼濾波(NS-EnKF)的作為參考基準(zhǔn),研究結(jié)果表明iSS是能夠合理的更新逆條件下的非高斯?jié)B透系數(shù)場實(shí)現(xiàn),對比iSS和NS-EnKF這兩種方法更新結(jié)果,可知這兩種方法更新的滲透系數(shù)場實(shí)現(xiàn)的質(zhì)量相仿。其次,我們進(jìn)一步研究了Hu et al.,2013提出的逆方法,并在此基礎(chǔ)上對其進(jìn)行了改進(jìn)。Hu et al.,2013是利用EnKF直接更新非相關(guān)的均勻隨機(jī)場(在序貫?zāi)M中,通過這些均勻隨機(jī)數(shù)據(jù),從局部條件邊緣分布中取值),不同于Hu et al.,2013的想法,新提出的改進(jìn)方法是用相關(guān)均勻隨機(jī)場作為參數(shù)對象地,類似于概率場模擬法(Froidevaux,1993)中均勻隨機(jī)場的運(yùn)用。這新舊兩種方法研究對比結(jié)果表明,在獲取滲透系數(shù)場空間結(jié)構(gòu)特征和減少實(shí)現(xiàn)不確定性方面,新的改進(jìn)方法比原先的方法要好很多。
[Abstract]:The ensemble Kalman filter has been proved to be a very efficient inverse method for characterizing the hydrological permeability coefficient field under the condition of nonlinear equation of state and Gao Si distribution. However, its shortcoming is that it is difficult to describe the non-permeability coefficient field. Gao Si parameter field. The research topic of this paper is how to describe the non-#china_person1# hydrological permeability coefficient field reasonably. First of all, In this paper, we propose a new stochastic inverse method-inverse sequential simulation. ISS uses some concepts of sequential simulation algorithm and normal Kalman filter algorithm for reference. This method realizes set by permeability coefficient field and sets by water level field under pressure. To calculate the nonstationary cross covariance between the two, Then we use sequential simulation to generate parameter variables. We use normal transformation technology to ensure that variables are distributed on the edge of Gao Si. Then, based on the data of permeability coefficient condition point and pressure water level condition point, we use normal transformation technology to ensure that the variables are distributed on the edge of Gao Si. The standard multivariable sequential Gao Si condition simulation is used to update the permeability coefficient field. The normal set Kalman filter (NS-EnKF) is set as the reference datum. The research results show that iSS can reasonably update the non-#china_person1# permeability coefficient field under the inverse condition. By comparing the results of iSS and NS-EnKF, we can see that the quality of permeability field of these two methods is similar. Secondly, we further study the inverse method proposed by Hu et al.2013. On the basis of this, we improve it. Hu et al. 2013 is to use EnKF to update the uncorrelated uniform random field directly. (in sequential simulation, through these uniform random data, we can get the value from the local conditional edge distribution, which is different from Hu et al. 2013. The improved method is to use the correlation uniform random field as the parameter object, similar to the application of the uniform random field in the probability field simulation method. The new improved method is much better than the original one in obtaining the spatial structure characteristics of the permeability coefficient field and reducing the uncertainty of the implementation.
【學(xué)位授予單位】：中國地質(zhì)大學(xué)(北京)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：P641.2

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