An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media
TLDR
The results show that TNNA‐AUS successfully reduces the inversion bias and improves the computational efficiency and inversion accuracy, compared with the global improvement strategy of adding training samples according to the prior distribution of model parameters.About:
This article is published in Water Resources Research.The article was published on 2021-11-10 and is currently open access. It has received 25 citations till now. The article focuses on the topics: Deep learning.read more
Citations
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An Integrated Inversion Framework for Heterogeneous Aquifer Structure Identification with Single-Sample Generative Adversarial Network
Chuanjun Zhan,Zhenxue Dai,Javier Samper,Shangxian Yin,Reza Ershadnia,Xiaoying Zhang,Yanwei Wang,Zhijie Yang,Xiaoyan Luan,Mohamad Reza Soltanian +9 more
TL;DR: In this paper , the authors developed an integrated inversion framework, which combines the geological single-sample generative adversarial network (GeoSinGAN), the deep octave convolution dense residual network (DOCRN), and the iterative local updating ensemble smoother (ILUES), named GeoSinGAN-DOCRN-ILUES, for more efficiently generating heterogeneous aquifer structures.
Journal ArticleDOI
An upscaling approach to predict mine water inflow from roof sandstone aquifers
Lulu Xu,Meifeng Cai,Shun Dong,Shangxian Yin,Ting Xiao,Zhenxue Dai,Yanwei Wang,Mohamad Reza Soltanian +7 more
TL;DR: Wang et al. as discussed by the authors developed a novel upscaling framework to predict roof water inflow by integrating the multiscale hydrogeological properties of roof aquifers, which can guide the development of methods for considering micropores and fractures simultaneously.
Journal ArticleDOI
An integrated experimental design framework for optimizing solute transport monitoring locations in heterogeneous sedimentary media
Zhenxue Dai,Ziqi Ma,Xiaoying Zhang,Junjun Chen,Reza Ershadnia,Xiaoyan Luan,Mohamad Reza Soltanian +6 more
TL;DR: In this article , an integrated framework is developed to guide the monitoring network optimization and duration selection for solute transport in heterogeneous sand tank experiments, which is designed based on entropy and data worth analysis.
Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework
TL;DR: In this paper , the authors extended a recently developed stage-wise stochastic deep learning inversion framework by coupling it with nonisothermal flow and transport simulations to estimate subsurface structures.
References
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Multi-objective parameter optimization of common land model using adaptive surrogate modeling
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Identifying geochemical processes by inverse modeling of multicomponent reactive transport in the Aquia aquifer
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Reactive Transport in Evolving Porous Media
TL;DR: In this paper, the authors proposed a reactive transport model to account for advection, diffusion, dispersion, and a multitude of biogeochemical reactions in porous media, including dissolution of mineral phases.
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Identification of sorption processes and parameters for radionuclide transport in fractured rock
Zhenxue Dai,Andrew V. Wolfsberg,Paul W. Reimus,Hailin Deng,Edward M. Kwicklis,Mei Ding,Doug S. Ware,Ming Ye +7 more
TL;DR: In this article, two sorption processes (equilibrium and kinetics) are considered for modeling neptunium and uranium sorption in fractured rock, based on different conceptualizations of the two processes occurring in fracture and matrix media, seven dual-porosity, multi-component reactive transport models are developed.
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Development of NOx reduction system utilizing artificial neural network (ANN) and genetic algorithm (GA)
TL;DR: In this paper, a surrogate model was developed by using an artificial neural network (ANN) algorithm based on the experimental data, and a genetic algorithm (GA) was integrated with the model to optimize the variables to maximize the removal of NOx in the flue gas.