Subsurface sedimentary structure identification using deep learning: A review
The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
Data‐worth analysis for heterogeneous subsurface structure identification with a stochastic deep learning framework
Reliable characterization of subsurface structures is essential for earth sciences and related
applications. Data assimilation‐based identification frameworks can reasonably estimate …
applications. Data assimilation‐based identification frameworks can reasonably estimate …
Deep autoregressive neural networks for high‐dimensional inverse problems in groundwater contaminant source identification
Identification of a groundwater contaminant source simultaneously with the hydraulic
conductivity in highly heterogeneous media often results in a high‐dimensional inverse …
conductivity in highly heterogeneous media often results in a high‐dimensional inverse …
Stage‐wise stochastic deep learning inversion framework for subsurface sedimentary structure identification
The stochastic models and deep‐learning models are the two most commonly used
methods for subsurface sedimentary structures identification. The results from the stochastic …
methods for subsurface sedimentary structures identification. The results from the stochastic …
An integrated inversion framework for heterogeneous aquifer structure identification with single-sample generative adversarial network
Generating reasonable heterogeneous aquifer structures is essential for understanding the
physicochemical processes controlling groundwater flow and solute transport better. The …
physicochemical processes controlling groundwater flow and solute transport better. The …
Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non‐Gaussian hydraulic conductivities
Inverse modeling for the estimation of non‐Gaussian hydraulic conductivity fields in
subsurface flow and solute transport models remains a challenging problem. This is mainly …
subsurface flow and solute transport models remains a challenging problem. This is mainly …
Solving inverse problems using conditional invertible neural networks
Inverse modeling for computing a high-dimensional spatially-varying property field from
indirect sparse and noisy observations is a challenging problem. This is due to the complex …
indirect sparse and noisy observations is a challenging problem. This is due to the complex …
An improved tandem neural network architecture for inverse modeling of multicomponent reactive transport in porous media
Parameter estimation for reactive transport models (RTMs) is important in improving their
predictive capacity for accurately simulating subsurface hydrogeochemical processes. This …
predictive capacity for accurately simulating subsurface hydrogeochemical processes. This …
Variational autoencoder or generative adversarial networks? a comparison of two deep learning methods for flow and transport data assimilation
Groundwater modeling is an important tool for water resources management and aquifer
remediation. However, the inherent strong heterogeneity of the subsurface and scarcity of …
remediation. However, the inherent strong heterogeneity of the subsurface and scarcity of …
An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers
Accurately estimating high-dimensional permeability (k) fields through data assimilation is
critical for minimizing uncertainties in groundwater flow and solute transport simulations …
critical for minimizing uncertainties in groundwater flow and solute transport simulations …