Geo-guided deep learning for spatial downscaling of solute transport in heterogeneous porous media
Resolving solute transport in heterogeneous porous media is a complex task, because of the sparse experimental data and the high computational cost of numerical simulations. This work proposes a unique two-stage deep learning architecture comprising a dual-branch autoencoder and a geo-guided super-r...
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| Vydané v: | Computers & geosciences Ročník 188; číslo C; s. 105599 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United Kingdom
Elsevier Ltd
01.06.2024
Elsevier |
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| ISSN: | 0098-3004, 1873-7803 |
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| Abstract | Resolving solute transport in heterogeneous porous media is a complex task, because of the sparse experimental data and the high computational cost of numerical simulations. This work proposes a unique two-stage deep learning architecture comprising a dual-branch autoencoder and a geo-guided super-resolution generative adversarial network (Gg-SRGAN) to address this dual challenge. The dual-branch autoencoder addresses the issue of sparsity by constructing a continuous, but coarse representation of concentration and pressure profiles from a sparse, discontinuous profile with up to 85% missing data points. The Gg-SRGAN is then employed to generate a finer representation of field variables from the outputs generated by the dual-branch autoencoder (i.e., downscaling). We train and test our framework using six solute transport cases with varying levels of heterogeneity and compare the results with standalone methods, namely the vanilla autoencoder and vanilla SRGAN, in addition to ground truth profiles generated by the finite element method (FEM). The comparisons are performed based on several statistical metrics, such as absolute point error (APE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS). The first four cases are used for training, evaluation, and testing. The last two cases are utilized for blind testing to determine the generalizability of the framework. Our results show that the dual-branch autoencoder outperforms the vanilla autoencoder, and the Gg-SRGAN outperforms the SRGAN during both the training and evaluation phases. Moreover, the proposed framework can successfully construct the fine representation of concentration profiles, compared to FEM, using the coarse representation of the pressure, concentration, and domain permeability fields. When tested using the two blind test cases, the proposed dual-branch autoencoder and Gg-SRGAN exhibit superior performance compared to their counterparts in terms of all evaluation metrics.
•A two-stage deep learning framework is developed to resolve solute transport in porous media.•The framework addresses the dual challenge of data sparsity and spatial downscaling.•The framework incorporates geological information to comprehend the underlying physics.•The framework outperforms conventional algorithms in terms of accuracy. |
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| AbstractList | Resolving solute transport in heterogeneous porous media is a complex task, because of the sparse experimental data and the high computational cost of numerical simulations. This work proposes a unique two-stage deep learning architecture comprising a dual-branch autoencoder and a geo-guided super-resolution generative adversarial network (Gg-SRGAN) to address this dual challenge. The dual-branch autoencoder addresses the issue of sparsity by constructing a continuous, but coarse representation of concentration and pressure profiles from a sparse, discontinuous profile with up to 85% missing data points. The Gg-SRGAN is then employed to generate a finer representation of field variables from the outputs generated by the dual-branch autoencoder (i.e., downscaling). We train and test our framework using six solute transport cases with varying levels of heterogeneity and compare the results with standalone methods, namely the vanilla autoencoder and vanilla SRGAN, in addition to ground truth profiles generated by the finite element method (FEM). The comparisons are performed based on several statistical metrics, such as absolute point error (APE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS). The first four cases are used for training, evaluation, and testing. The last two cases are utilized for blind testing to determine the generalizability of the framework. Our results show that the dual-branch autoencoder outperforms the vanilla autoencoder, and the Gg-SRGAN outperforms the SRGAN during both the training and evaluation phases. Moreover, the proposed framework can successfully construct the fine representation of concentration profiles, compared to FEM, using the coarse representation of the pressure, concentration, and domain permeability fields. When tested using the two blind test cases, the proposed dual-branch autoencoder and Gg-SRGAN exhibit superior performance compared to their counterparts in terms of all evaluation metrics. Resolving solute transport in heterogeneous porous media is a complex task, because of the sparse experimental data and the high computational cost of numerical simulations. This work proposes a unique two-stage deep learning architecture comprising a dual-branch autoencoder and a geo-guided super-resolution generative adversarial network (Gg-SRGAN) to address this dual challenge. The dual-branch autoencoder addresses the issue of sparsity by constructing a continuous, but coarse representation of concentration and pressure profiles from a sparse, discontinuous profile with up to 85% missing data points. The Gg-SRGAN is then employed to generate a finer representation of field variables from the outputs generated by the dual-branch autoencoder (i.e., downscaling). We train and test our framework using six solute transport cases with varying levels of heterogeneity and compare the results with standalone methods, namely the vanilla autoencoder and vanilla SRGAN, in addition to ground truth profiles generated by the finite element method (FEM). The comparisons are performed based on several statistical metrics, such as absolute point error (APE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS). The first four cases are used for training, evaluation, and testing. The last two cases are utilized for blind testing to determine the generalizability of the framework. Our results show that the dual-branch autoencoder outperforms the vanilla autoencoder, and the Gg-SRGAN outperforms the SRGAN during both the training and evaluation phases. Moreover, the proposed framework can successfully construct the fine representation of concentration profiles, compared to FEM, using the coarse representation of the pressure, concentration, and domain permeability fields. When tested using the two blind test cases, the proposed dual-branch autoencoder and Gg-SRGAN exhibit superior performance compared to their counterparts in terms of all evaluation metrics. •A two-stage deep learning framework is developed to resolve solute transport in porous media.•The framework addresses the dual challenge of data sparsity and spatial downscaling.•The framework incorporates geological information to comprehend the underlying physics.•The framework outperforms conventional algorithms in terms of accuracy. |
| ArticleNumber | 105599 |
| Author | Soltanmohammadi, Ramin Pawar, Nikhil M. Faroughi, Salah A. Faroughi, Shirko |
| Author_xml | – sequence: 1 givenname: Nikhil M. orcidid: 0000-0002-1161-3289 surname: Pawar fullname: Pawar, Nikhil M. organization: Geo-Intelligence Laboratory, Ingram School of Engineering, Texas State University, San Marcos, TX, 78666, USA – sequence: 2 givenname: Ramin orcidid: 0000-0002-4423-8884 surname: Soltanmohammadi fullname: Soltanmohammadi, Ramin organization: Geo-Intelligence Laboratory, Ingram School of Engineering, Texas State University, San Marcos, TX, 78666, USA – sequence: 3 givenname: Shirko orcidid: 0000-0002-6489-0562 surname: Faroughi fullname: Faroughi, Shirko organization: Department of Mechanical Engineering, School of Engineering, Urmia University of Technology, Urmia, 8352-25382, Iran – sequence: 4 givenname: Salah A. orcidid: 0000-0002-6543-1691 surname: Faroughi fullname: Faroughi, Salah A. email: salah.faroughi@txstate.edu organization: Geo-Intelligence Laboratory, Ingram School of Engineering, Texas State University, San Marcos, TX, 78666, USA |
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| Keywords | Super-resolution Solute transport Data imputation Autoencoders Generative adversarial network Spatial downscaling Heterogeneous porous media Geo-guided deep learning |
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