An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers.

Autor: Cao M; College of Construction Engineering, Jilin University, Changchun, China; Institute of Intelligent Simulation and Early Warning for Subsurface Environment, Jilin University, Changchun, China., Dai Z; College of Construction Engineering, Jilin University, Changchun, China; Institute of Intelligent Simulation and Early Warning for Subsurface Environment, Jilin University, Changchun, China; School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China., Chen J; National and Local Joint Engineering Laboratory of Internet Application Technology on Mine, China University of Mining and Technology, Xuzhou, China. Electronic address: junjunchen@cumt.edu.cn., Yin H; College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China; Plant & Environmental Sciences Department, New Mexico State University, Las Cruces, NM 88003, USA., Zhang X; College of Construction Engineering, Jilin University, Changchun, China; Institute of Intelligent Simulation and Early Warning for Subsurface Environment, Jilin University, Changchun, China., Wu J; Key Laboratory of Surficial Geochemistry of Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing, China., Thanh HV; Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam; Applied Science Research Center, Applied Science Private University, Amman, Jordan., Soltanian MR; Departments of Geosciences and Environmental Engineering, University of Cincinnati, Cincinnati, OH, USA.
Jazyk: angličtina
Zdroj: Water research [Water Res] 2025 Jan 01; Vol. 268 (Pt B), pp. 122706. Date of Electronic Publication: 2024 Oct 30.
DOI: 10.1016/j.watres.2024.122706
Abstrakt: Accurately estimating high-dimensional permeability (k) fields through data assimilation is critical for minimizing uncertainties in groundwater flow and solute transport simulations. However, designing an effective monitoring network to obtain diverse system responses in heterogeneous aquifers for data assimilation presents significant challenges. To investigate the influence of different measurement types (hydraulic heads, solute concentrations, and permeability) and monitoring strategies on the accuracy of permeability characterization, this study integrates a deep learning-based surrogate modeling approach and the entropy-based maximum information minimum redundancy (MIMR) monitoring design criterion into a data assimilation framework. An ensemble MIMR-optimized method is developed to provide more comprehensive monitoring information and avoid missing key information due to the randomness of stochastic response datasets in entropy analysis. A numerical case of solute transport with log-Gaussian permeability fields is presented, with twelve scenarios designed by combining different measurement types and monitoring strategies. The results demonstrated that the proposed ensemble MIMR-optimized method significantly improved the k-field estimates compared to the conventional MIMR method. Additionally, high prediction accuracy in forward modeling is essential for ensuring reliable inversion results, especially for observation data with strong nonlinearity. The findings of this study enhance our understanding and management of k-field estimation in heterogeneous aquifers, contributing to the development of more robust inversion frameworks for general data assimilation tasks.
Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest regarding the publication of this article.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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