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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Journal ArticleDOI
Modeling of non-isothermal multi-component reactive transport in field scale porous media flow systems
TL;DR: TransQUI as discussed by the authors is a general 2D finite element multi-component reactive transport code, which is well suited to deal with complex real-world thermo-hydro-geochemical problems for single-phase variably water saturated porous media flow systems.
Journal ArticleDOI
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
TL;DR: It is demonstrated that the physics-informed deep neural networks used for estimating space-dependent hydraulic conductivity, hydraulic head, and concentration fields from sparse measurements are significantly more accurate than standard data-driven DNNs when the training set consists of sparse data.
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Inverse problem of multicomponent reactive chemical transport in porous media: Formulation and applications
TL;DR: In this paper, a general methodology for solving the inverse problem of multicomponent reactive solute transport in porous media is presented, which relies on hydraulic heads, aqueous and total concentrations, water fluxes and water contents.
ReportDOI
TOUGHREACT User's Guide: A Simulation Program for Non-isothermal Multiphase Reactive Geochemical Transport in Variably Saturated Geologic Media, V1.2.1
TL;DR: The TOUGHREACT as discussed by the authors is a comprehensive non-isothermal multi-component reactive fluid flow and geochemical transport simulator to investigate geologic systems and environmental problems, including geothermal systems, diagenetic and weathering processes, subsurface waste disposal, acid mine drainage remediation, contaminant transport, and groundwater quality.
Journal ArticleDOI
Deep learning of subsurface flow via theory-guided neural network
TL;DR: Numerical results demonstrate that the Theory-guided Neural Network model achieves much better predictability, reliability, and generalizability than ANN models due to the physical/engineering constraints in the former.