Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026082 |
Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non-Gaussian Hydraulic Conductivities | |
Mo, Shaoxing1,2; Zabaras, Nicholas2; Shi, Xiaoqing1; Wu, Jichun1 | |
2020-02-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2020 |
卷号 | 56期号:2 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | Inverse modeling for the estimation of non-Gaussian hydraulic conductivity fields in subsurface flow and solute transport models remains a challenging problem. This is mainly due to the non-Gaussian property, the nonlinear physics, and the fact that many repeated evaluations of the forward model are often required. In this study, we develop a convolutional adversarial autoencoder (CAAE) to parameterize non-Gaussian conductivity fields with heterogeneous conductivity within each facies using a low-dimensional latent representation. In addition, a deep residual dense convolutional network (DRDCN) is proposed for surrogate modeling of forward models with high-dimensional and highly complex mappings. The two networks are both based on a multilevel residual learning architecture called residual-in-residual dense block. The multilevel residual learning strategy and the dense connection structure ease the training of deep networks, enabling us to efficiently build deeper networks that have an essentially increased capacity for approximating mappings of very high complexity. The CAAE and DRDCN networks are incorporated into an iterative ensemble smoother to formulate an inversion framework. The numerical experiments performed using 2-D and 3-D solute transport models illustrate the performance of the integrated method. The obtained results indicate that the CAAE is a robust parameterization method for non-Gaussian conductivity fields with different heterogeneity patterns. The DRDCN is able to obtain accurate approximations of the forward models with high-dimensional and highly complex mappings using relatively limited training data. The CAAE and DRDCN methods together significantly reduce the computation time required to achieve accurate inversion results. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000535672800033 |
WOS关键词 | PRINCIPAL COMPONENT ANALYSIS ; LOW-DIMENSIONAL REPRESENTATION ; BAYESIAN EXPERIMENTAL-DESIGN ; ITERATIVE ENSEMBLE SMOOTHER ; ENCODER-DECODER NETWORKS ; MONTE-CARLO-SIMULATION ; CONTAMINANT SOURCE ; POLYNOMIAL CHAOS ; SUBSURFACE FLOW ; DIFFERENTIABLE PARAMETERIZATION |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/280545 |
专题 | 资源环境科学 |
作者单位 | 1.Nanjing Univ, Sch Earth Sci & Engn, Minist Educ, Key Lab Surficial Geochem, Nanjing, Peoples R China; 2.Univ Notre Dame, Ctr Informat & Computat Sci, Notre Dame, IN 46556 USA |
推荐引用方式 GB/T 7714 | Mo, Shaoxing,Zabaras, Nicholas,Shi, Xiaoqing,et al. Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non-Gaussian Hydraulic Conductivities[J]. WATER RESOURCES RESEARCH,2020,56(2). |
APA | Mo, Shaoxing,Zabaras, Nicholas,Shi, Xiaoqing,&Wu, Jichun.(2020).Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non-Gaussian Hydraulic Conductivities.WATER RESOURCES RESEARCH,56(2). |
MLA | Mo, Shaoxing,et al."Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non-Gaussian Hydraulic Conductivities".WATER RESOURCES RESEARCH 56.2(2020). |
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