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 …
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 …
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 …
Ensemble smoother with multiple data assimilation to simultaneously estimate the source location and the release history of a contaminant spill in an aquifer
The source location and the time history of a pollutant released in an aquifer are very
relevant information for the design of effective remediation strategies. Usually, their …
relevant information for the design of effective remediation strategies. Usually, their …
Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection
Time-lapse electrical resistivity tomography (ERT) measurements provide
indirectobservations of hydrological processes in the Earth's shallow subsurface at high …
indirectobservations of hydrological processes in the Earth's shallow subsurface at high …
Using ensemble data assimilation to estimate transient hydrologic exchange flow under highly dynamic flow conditions
Quantifying dynamic hydrologic exchange flows (HEFs) within river corridors that experience
high‐frequency flow variations caused by dam regulations is important for understanding the …
high‐frequency flow variations caused by dam regulations is important for understanding the …
Efficient multiscale imaging of subsurface resistivity with uncertainty quantification using ensemble Kalman inversion
Electrical resistivity tomography (ERT) is widely used to image the Earth's subsurface and
has proven to be an extremely useful tool in application to hydrological problems …
has proven to be an extremely useful tool in application to hydrological problems …
Integration of soft data into geostatistical simulation of categorical variables
SF Carle, GE Fogg - Frontiers in Earth Science, 2020 - frontiersin.org
Uncertain or indirect “soft” data, such as geologic interpretation, driller's logs, geophysical
logs or imaging, offer potential constraints or “soft conditioning” to stochastic models of …
logs or imaging, offer potential constraints or “soft conditioning” to stochastic models of …
Estimating watershed subsurface permeability from stream discharge data using deep neural networks
Subsurface permeability is a key parameter in watershed models that controls the
contribution from the subsurface flow to stream flows. Since the permeability is difficult and …
contribution from the subsurface flow to stream flows. Since the permeability is difficult and …