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
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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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Physics-Informed Neural Networks With Monotonicity Constraints for Richardson-Richards Equation: Estimation of Constitutive Relationships and Soil Water Flux Density From Volumetric Water Content Measurements
TL;DR: Water retention curves (WRCs) and hydraulic conductivity functions (HCFs) are critical soil-specific characteristics necessary for modeling the movement of water in soils using the Richardson-Richa... as mentioned in this paper.
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Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization
TL;DR: A machine learning methodology is illustrated to attack the inverse design problem concerning the optimization of the dispersion properties characterizing a novel layered mechanical metamaterial, conceived starting from the bi-tetrachiral periodic topology.
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An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions
TL;DR: In this article, an iterative local updating ensemble smoother (ILUES) algorithm is proposed to explore multimodal distributions of model parameters in nonlinear hydrologic systems, which works by updating local ensembles of each sample with ensemble smoother to explore possible multimodical distributions.
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Reactive Transport Modeling: A Key Performance Assessment Tool for the Geologic Disposal of Nuclear Waste
Laurent De Windt,Nicolas Spycher +1 more
TL;DR: De Windt et al. as mentioned in this paper used reactive transport models to understand and assess how thermal, hydrological, and geochemical processes are coupled in these containment barriers, which are expected to experience a range of temperatures, geochemical conditions, yet, must maintain their integrity for millions of years.
Reactive solute transport in an asymmetrical fracture-rock matrix system
Renjie Zhou,Hongbin Zhan +1 more
TL;DR: In this paper, the authors considered the transport problem in a single fracture-rock matrix system with asymmetric distribution of transport properties in the rock matrixes and developed mathematical models to analyze the spatio-temporal concentration and mass distribution in the fracture and rock matrix with the help of Laplace transform technique and de Hoog numerical inverse Laplace algorithm.