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1.
Nonlinear groundwater flow models have the propensity to be overly complex leading to burdensome computational demands. Reduced modeling techniques are used to develop an approximation of the original model that has smaller dimensionality and faster run times. The reduced model proposed is a combination of proper orthogonal decomposition (POD) and the discrete empirical interpolation method (DEIM). Solutions of the full model (snapshots) are collected to represent the physical dynamics of the system and Galerkin projection allows the formulation of a reduced model that lies in a subspace of the full model. Interpolation points are added through DEIM to eliminate the reduced model's dependence on the dimension of the full model. POD is shown to effectively reduce the dimension of the full model and DEIM is shown to speed up the solution by further reducing the dimension of the nonlinear calculations. To show the concept can work for unconfined groundwater flow model, with added nonlinear forcings, one-dimensional and two-dimensional test cases are constructed in MODFLOW-OWHM. POD and DEIM are added to MODFLOW as a modular package. Comparing the POD and the POD-DEIM reduced models, the experimental results indicate similar reduction in dimension size with additional computation speed up for the added interpolation. The hyper-reduction method presented is effective for models that have fine discretization in space and/or time as well as nonlinearities with respect to the state variable. The dual reduction approach ensures that, once constructed, the reduced model can be solved in an equation system that depends only on reduced dimensions. 相似文献
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A new methodology is proposed for the development of parameter-independent reduced models for transient groundwater flow models. The model reduction technique is based on Galerkin projection of a highly discretized model onto a subspace spanned by a small number of optimally chosen basis functions. We propose two greedy algorithms that iteratively select optimal parameter sets and snapshot times between the parameter space and the time domain in order to generate snapshots. The snapshots are used to build the Galerkin projection matrix, which covers the entire parameter space in the full model. We then apply the reduced subspace model to solve two inverse problems: a deterministic inverse problem and a Bayesian inverse problem with a Markov Chain Monte Carlo (MCMC) method. The proposed methodology is validated with a conceptual one-dimensional groundwater flow model. We then apply the methodology to a basin-scale, conceptual aquifer in the Oristano plain of Sardinia, Italy. Using the methodology, the full model governed by 29,197 ordinary differential equations is reduced by two to three orders of magnitude, resulting in a drastic reduction in computational requirements. 相似文献
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A new model order reduction strategy adapted to nonlinear problems in earthquake engineering 
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下载免费PDF全文 Earthquake dynamic response analysis of large complex structures, especially in the presence of nonlinearities, usually turns out to be computationally expensive. In this paper, the methodical developments of a new model order reduction strategy (MOR) based on the proper orthogonal decomposition (POD) method as well as its practical applicability to a realistic building structure are presented. The seismic performance of the building structure, a medical complex, is to be improved by means of base isolation realized by frictional pendulum bearings. According to the new introduced MOR strategy, a set of deterministic POD modes (transformation matrix) is assembled, which is derived based on the information of parts of the response history, so‐called snapshots, of the structure under a representative earthquake excitation. Subsequently, this transformation matrix is utilized to create reduced‐order models of the structure subjected to different earthquake excitations. These sets of nonlinear low‐order representations are now solved in a fractional amount of time in comparison with the computations of the full (non‐reduced) systems. The results demonstrate accurate approximations of the physical (full) responses by means of this new MOR strategy if the probable behavior of the structure has already been captured in the POD snapshots. Copyright © 2016 The Authors. Earthquake Engineering & Structural Dynamics Published by John Wiley & Sons Ltd. 相似文献
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We present a methodology conducive to the application of a Galerkin model order reduction technique, Proper Orthogonal Decomposition (POD), to solve a groundwater flow problem driven by spatially distributed stochastic forcing terms. Typical applications of POD to reducing time-dependent deterministic partial differential equations (PDEs) involve solving the governing PDE at some observation times (termed snapshots), which are then used in the order reduction of the problem. Here, the application of POD to solve the stochastic flow problem relies on selecting the snapshots in the probability space of the random quantity of interest. This allows casting a standard Monte Carlo (MC) solution of the groundwater flow field into a Reduced Order Monte Carlo (ROMC) framework. We explore the robustness of the ROMC methodology by way of a set of numerical examples involving two-dimensional steady-state groundwater flow taking place within an aquifer of uniform hydraulic properties and subject to a randomly distributed recharge. We analyze the impact of (i) the number of snapshots selected from the hydraulic heads probability space, (ii) the associated number of principal components, and (iii) the key geostatistical parameters describing the heterogeneity of the distributed recharge on the performance of the method. We find that our ROMC scheme can improve significantly the computational efficiency of a standard MC framework while keeping the same degree of accuracy in providing the leading statistical moments (i.e. mean and covariance) as well as the sample probability density of the state variable of interest. 相似文献
5.
传统的伪谱(PS)方法,采用傅里叶变换(FT)计算空间导数具有很高的精度,每个波长仅需要两个采样点,而时间导数采用有限差分(FD)近似因而精度较低.当采用大时间步长时,由于时空精度不平衡,PS法存在不稳定性问题.原始的k-space方法可以有效地克服这些问题但是却无法适用于非均匀介质.为了提高原始k-space方法模拟非均匀介质波动方程的精度,我们提出了一种新的k-space算子族.它是用非均匀介质的变速度代替原k-space算子中的常数补偿速度构造得到,引入低秩近似可以高效求解.我们将构造的新的k-space算子应用于耦合的二阶位移波动方程,而不是交错网格一阶速度应力波动方程,使模拟弹性波的计算存储量减少.我们从数学上证明了基于二阶波动方程的k-space方法与基于一阶波动方程的k-space方法是等价的.数值模拟实验表明,与传统的PS、交错网格PS和原始的k-space方法相比,我们的新方法可以在时间和空间步长较大的均匀和非均匀介质中,为弹性波的传播提供更精确的数值解.在保持稳定性和精度的同时,采用较大的时空采样间隔,可以大大降低数值模拟的计算成本. 相似文献
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小波束域利用局部余弦基(local cosine bases)的偏移成像方法具有很高的计算效率和成像质量.然而局部余弦基小波束通常沿垂直方向具有两个对称波瓣.由于缺乏单一定义的方向性,在某些强变速介质中利用局部余弦基的波传播方法会产生一定的误差,同时也使得一些在局部角度域的操作变得非常不便.于是我们提出了利用局部谐和基进行波场外推的方法.局部谐和基(local harmonic base)是由局部余弦基和局部正弦基线性组合而成,具有单一定义的方向性,同时具备快速算法.局部谐和基与Gabor-Daubechies标架类似,都具有单一定义的方向性,但比Gabor-Daubechies标架效率更高.局部谐和基保持了与局部余弦基的偏移成像方法相同的计算效率,但在成像精度上有了显著的提高.通过二维SEG/EAGE模型和BP模型的叠前深度域偏移成像说明了该方法的有效性. 相似文献
7.
在数值模拟中,隐式有限差分具有较高的精度和稳定性.然而,传统隐式有限差分算法大多由于需要求解大型矩阵方程而存在计算效率偏低的局限性.本文针对一阶速度-应力弹性波方程,构建了一种优化隐式交错网格有限差分格式,然后将改进格式由时间-空间域转换为时间-波数域,利用二范数原理建立目标函数,再利用模拟退火法求取优化系数.通过对均匀模型以及复杂介质模型进行一阶速度-应力弹性波方程数值模拟所得单炮记录、波场快照分析表明:这种优化隐式交错网格差分算法与传统的几种显式和隐式交错网格有限差分算法相比不但降低了计算量,而且能有效的压制网格频散,使弹性波数值模拟的精度得到有效的提高. 相似文献
8.
We develop a new Proper Orthogonal Decomposition (POD) reduced order model for saturated groundwater flow, and apply that model to an inverse problem for the hydraulic conductivity field. We use sensitivities as the POD basis. We compare the output when the optimizer uses the reduced order model against results obtained with a full PDE based model. The solutions generated using the POD reduced model are comparable in residual norm to the solutions formed using only the full-scale model. The material parameters are similarly comparable. The time to solution when using the reduced model is reduced by at least an order of magnitude, as are the number of calls to the full model. 相似文献
9.
Yong Zhang Dongbao Zhou Maosheng Yin HongGuang Sun Wei Wei Shiyin Li Chunmiao Zheng 《水文研究》2020,34(25):5104-5122
Modelling pollutant transport in water is one of the core tasks of computational hydrology, and various physical models including especially the widely used nonlocal transport models have been developed and applied in the last three decades. No studies, however, have been conducted to systematically assess the applicability, limitations and improvement of these nonlocal transport models. To fill this knowledge gap, this study reviewed, tested and improved the state-of-the-art nonlocal transport models, including their physical background, mathematical formula and especially the capability to quantify conservative tracers moving in one-dimensional sand columns, which represents perhaps the simplest real-world application. Applications showed that, surprisingly, neither the popular time-nonlocal transport models (including the multi-rate mass transfer model, the continuous time random walk framework and the time fractional advection-dispersion equation), nor the spatiotemporally nonlocal transport model (ST-fADE) can accurately fit passive tracers moving through a 15-m-long heterogeneous sand column documented in literature, if a constant dispersion coefficient or dispersivity is used. This is because pollutant transport in heterogeneous media can be scale-dependent (represented by a dispersion coefficient or dispersivity increasing with spatiotemporal scales), non-Fickian (where plume variance increases nonlinearly in time) and/or pre-asymptotic (with transition between non-Fickian and Fickian transport). These different properties cannot be simultaneously and accurately modelled by any of the transport models reviewed by this study. To bypass this limitation, five possible corrections were proposed, and two of them were tested successfully, including a time fractional and space Hausdorff fractal model which minimizes the scale-dependency of the dispersion coefficient in the non-Euclidean space, and a two-region time fractional advection-dispersion equation which accounts for the spatial mixing of solute particles from different mobile domains. Therefore, more efforts are still needed to accurately model transport in non-ideal porous media, and the five model corrections proposed by this study may shed light on these indispensable modelling efforts. 相似文献
10.
Flow and transport models in heterogeneous geological formations are usually large-scale with excessive computational complexity and uncertain characteristics. Uncertainty quantification for predicting subsurface flow and transport often entails utilizing a numerical Monte Carlo framework, which repeatedly simulates the model according to a random field parameter representing hydrogeological characteristics of the aquifer. The physical resolution (e.g. spatial grid resolution) for the simulation is customarily chosen based on recommendations in the literature, independent of the number of Monte Carlo realizations. This practice may lead to either excessive computational burden or inaccurate solutions. We develop an optimization-based methodology that considers the trade-off between the following conflicting objectives: time associated with computational costs, statistical convergence of the model prediction and physical errors corresponding to numerical grid resolution. Computational resources are allocated by considering the overall error based on a joint statistical–numerical analysis and optimizing the error model subject to a given computational constraint. The derived expression for the overall error explicitly takes into account the joint dependence between the discretization error of the physical space and the statistical error associated with Monte Carlo realizations. The performance of the framework is tested against computationally extensive simulations of flow and transport in spatially heterogeneous aquifers. Results show that modelers can achieve optimum physical and statistical resolutions while keeping a minimum error for a given computational time. The physical and statistical resolutions obtained through our analysis yield lower computational costs when compared to the results obtained with prevalent recommendations in the literature. Lastly, we highlight the significance of the geometrical characteristics of the contaminant source zone on the optimum physical and statistical resolutions. 相似文献
11.
烃类储集层是一种复合多相介质，在固体颗粒的空隙中含有气体或液体. 研究弹性波在该类地层中的传播规律对于油气勘探开发，特别对于全波列声波测井有重要意义. 为了提高孔隙弹性介质数值模拟的计算效率，本文采用改进显式交错网格有限差分算法取代常用的空间域四阶和时间域二阶的速度 - 应力有限差分算法，算法的空间域为八阶、时间域为二阶. 虽然计算的时间步长略小于空间域四阶的情形，但高阶有限差分算法可以选择较粗糙的网格，因此补偿了计算的低效；同时高阶交错网格有限差分算法的空间频散性比低阶算法小. 利用该算法计算了一个两层模型的波场，同时还模拟了等效弹性和孔隙弹性模型中波的传播. 结果表明慢波及其影响明显，尽管慢波衰减很快，但被某一界面反射后，转换形成的P波和S波仍以正常的方式传播，且比慢波衰减小. 相似文献
12.
Etienne Mémin 《地球物理与天体物理流体动力学》2014,108(2):119-146
We present a derivation of a stochastic model of Navier Stokes equations that relies on a decomposition of the velocity fields into a differentiable drift component and a time uncorrelated uncertainty random term. This type of decomposition is reminiscent in spirit to the classical Reynolds decomposition. However, the random velocity fluctuations considered here are not differentiable with respect to time, and they must be handled through stochastic calculus. The dynamics associated with the differentiable drift component is derived from a stochastic version of the Reynolds transport theorem. It includes in its general form an uncertainty dependent subgrid bulk formula that cannot be immediately related to the usual Boussinesq eddy viscosity assumption constructed from thermal molecular agitation analogy. This formulation, emerging from uncertainties on the fluid parcels location, explains with another viewpoint some subgrid eddy diffusion models currently used in computational fluid dynamics or in geophysical sciences and paves the way for new large-scales flow modeling. We finally describe an applications of our formalism to the derivation of stochastic versions of the Shallow water equations or to the definition of reduced order dynamical systems. 相似文献
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14.
Survey sinking migration downward continues the entire surface observed multi‐shot data to the subsurface step by step recursively. Reflected energy from reflectors at current depth appear at zero time and zero offset in the extrapolated wavefield. The data (seismic records) of t > 0 at this depth are equivalent to the data acquired by a survey system deployed at this depth. This is the reason to name the process ‘survey sinking’. The records of negative time need not to be further propagated since they carry no information to image structures beneath the new survey system. In this paper, we combine survey sinking with dreamlet migration. The dreamlet migration method decomposes the seismic wavefield and one‐way wave propagator by complete time‐space localized bases. The localization on time gives flexibility on time‐varying operations during depth extrapolation. In dreamlet survey sinking migration, it only keeps the data for imaging the structures beneath the sunk survey system and gets rid of the data already used to image structures above it. The deeper the depth is, the shorter is the valid time records of the remaining data and less computation is needed for one depth step continuation. For data decomposition, in addition to time axis, dreamlet survey sinking also decomposes the data for source and receiver gathers, which is a fully localized decomposition of prestack seismic data. A three‐scatter model is first used to demonstrate the computational feature and principle of this method. Tests on the two‐dimensional SEG/EAGE salt model show that with reduced data sets the proposed method can still obtain good imaging quality on complex geology structures and a strong velocity contrast environment. 相似文献
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Increasing salinity in Urmia Lake, located in the north-west of Iran, has turned into a critical issue, particularly because the lake is the habitat of a unique multi-cellular organism called Artemia Urmiana. During the past decades, several anthropogenic changes have taken place in the lake, which have resulted in increased salinity. This study introduces a reduced-order framework based on MIKE3 simulation model and proper orthogonal decomposition (POD) to simulate salinity patterns in Urmia Lake. Spatio-temporal variations of salinity in the lake firstly were simulated by MIKE3, and close matches were observed between salinity estimates from MIKE3 and those of the field data. Thereafter, 365 daily snapshots were taken from MIKE3 simulations, and subsequently 365 POD basis modes were computed. Due to high percentage of conserved energy of the lake system (salinity of lake) within the first ten POD basis modes, these modes were considered to develop a reduced-order salinity model (ROSM). Finally, results from MIKE3 were compared with the ROSM. It was shown that the first ten modes (among 365 modes) obtained by the POD conserved approximately more than 99.8% of the energy of the system. Moreover, using the first ten modes resulted in an error in magnitude of less than 0.01. Therefore, the ROSM could successfully capture the variations of salinity in the lake via its first ten modes. 相似文献
17.
提高计算精度和运算效率是所有波场正演方法所追求的目标,本文通过将速度 （应力）对时间的奇数阶高阶寻数转化为应力（速度）对空间的导数,运用时间和空间差分精度 均可达任意阶的高阶差分法,通过交错网格技术,对一阶速度－应力弹性波方程进行了数值求 解．波场快照以及实际模型的正演结果表明,这种求解一阶弹性波方程的高阶差分解法,和 常规的差分法相比网格频散显著减小,精度明显提高,而且可以取较大的空间步长,提高计算 效率。 相似文献
18.
Yanadet Sripanich Sergey Fomel Junzhe Sun Jiubing Cheng 《Geophysical Prospecting》2017,65(5):1231-1245
The goal of wave‐mode separation and wave‐vector decomposition is to separate a full elastic wavefield into three wavefields with each corresponding to a different wave mode. This allows elastic reverse‐time migration to handle each wave mode independently. Several of the previously proposed methods to accomplish this task require the knowledge of the polarisation vectors of all three wave modes in a given anisotropic medium. We propose a wave‐vector decomposition method where the wavefield is decomposed in the wavenumber domain via the analytical decomposition operator with improved computational efficiency using low‐rank approximations. The method is applicable for general heterogeneous anisotropic media. To apply the proposed method in low‐symmetry anisotropic media such as orthorhombic, monoclinic, and triclinic, we define the two S modes by sorting them based on their phase velocities (S1 and S2), which are defined everywhere except at the singularities. The singularities can be located using an analytical condition derived from the exact phase‐velocity expressions for S waves. This condition defines a weight function, which can be applied to attenuate the planar artefacts caused by the local discontinuity of polarisation vectors at the singularities. The amplitude information lost because of weighting can be recovered using the technique of local signal–noise orthogonalisation. Numerical examples show that the proposed approach provides an effective decomposition method for all wave modes in heterogeneous, strongly anisotropic media. 相似文献
19.
Up–down wavefield decomposition is effectuated by a scaled addition or subtraction of the pressure and vertical particle velocity, generally on horizontal or vertical surfaces, and works well for data given on such surfaces. The method, however, is not applicable to decomposing a wavefield when it is given at one instance in time, i.e. on snapshots. Such situations occur when a wavefield is modelled with methods like finite-difference techniques, for the purpose of, for example, reverse time migration, where the entire wavefield is determined per time instance. We present an alternative decomposition method that is exact when working on snapshots of an acoustic wavefield in a homogeneous medium, but can easily be approximated to heterogeneous media, and allows the wavefield to be decomposed in arbitrary directions. Such a directional snapshot wavefield decomposition is achieved by recasting the acoustic system in terms of the time derivative of the pressure and the vertical particle velocity, as opposed to the vertical derivative in up–down decomposition for data given on a horizontal surface. As in up–down decomposition of data given at a horizontal surface, the system can be eigenvalue decomposed and the inverse of the eigenvector matrix decomposes the wavefield snapshot into fields of opposite directions, including up–down decomposition. As the vertical particle velocity can be rotated at will, this allows for decomposition of the wavefield into any spatial direction; even spatially varying directions are possible. We show the power and effectiveness of the method by synthetic examples and models of increasing complexity. 相似文献
20.
This work presents a random field model of disease attribute (incidence, mortality etc.) that transfers the study of the attribute distribution from the original spatiotemporal domain onto a lower-dimensionality traveling domain that moves along the direction of disease velocity. The partial differential equations connecting the disease attribute covariances in the original and the traveling domain are derived with coefficients that are functions of the disease velocity. These equations offer epidemiologic insight concerning the strength of the space–time dependence between the disease attribute values in the two domains. The traveling disease model has certain theoretical and computational advantages in the study and prediction of space–time disease attribute distributions in conditions of uncertainty. Estimates of the disease attribute are derived in the traveling domain and then used to generate maps of space–time disease attribute distribution in the original domain. The theoretical model is illustrated and additional insight is gained by means of a numerical mortality simulation study, which shows that the proposed model is at least as accurate but computationally more efficient than mainstream mapping techniques of higher dimensionality. These findings concerning the very good predictability of the proposed model also strongly support its adequacy to represent the space–time mortality distribution. 相似文献