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
More filters
Journal ArticleDOI
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
More filters
Journal ArticleDOI
Deep Learning for Daily Precipitation and Temperature Downscaling
Journal ArticleDOI
Water quality parameter estimation in a distribution system under dynamic state.
TL;DR: The simulation-optimization model presented in this paper provides an effective tool to simplify the chlorine decay model calibration process that is often tedious and is based on the weighted-least-squares method solved by Gauss-Newton technique.
Journal ArticleDOI
Reactive transport modeling of geochemical controls on secondary water quality impacts at a crude oil spill site near Bemidji, MN
Gene Hua Crystal Ng,Barbara A. Bekins,Isabelle M. Cozzarelli,Mary Jo Baedecker,Philip C. Bennett,Richard T. Amos,William N. Herkelrath +6 more
TL;DR: In this article, the authors implemented a comprehensive model that simulates secondary plumes of depleted dissolved O2 and elevated concentrations of Mn2+, Fe2+, CH4, and Ca2+ over a two-dimensional cross section for 30 years following a crude oil spill.
Journal ArticleDOI
Relating reactive solute transport to hierarchical and multiscale sedimentary architecture in a Lagrangian-based transport model: 2. Particle displacement variance
TL;DR: In this article, a Lagrangian-based transport model for plume dispersion in groundwater is proposed, which is based on the particle displacement variance, X11R(t).
Journal ArticleDOI
Scale dependence of sorption coefficients for contaminant transport in saturated fractured rock
TL;DR: In this article, a scaling methodology was developed to upscale matrix sorption coefficients for fractured-rock systems by characterizing both the tortuosity field (physical heterogeneity) and retardation factor field (chemical heterogeneity) in the rock matrix.