DGSA: A Matlab toolbox for distance-based generalized sensitivity analysis of geoscientific computer experiments
Sensitivity analysis plays an important role in geoscientific computer experiments, whether
for forecasting, data assimilation or model calibration. In this paper we focus on an extension …
for forecasting, data assimilation or model calibration. In this paper we focus on an extension …
Stochastic simulation by image quilting of process-based geological models
Process-based modeling offers a way to represent realistic geological heterogeneity in
subsurface models. The main limitation lies in conditioning such models to data. Multiple …
subsurface models. The main limitation lies in conditioning such models to data. Multiple …
Geomodeling using generative adversarial networks and a database of satellite imagery of modern river deltas
E Nesvold, T Mukerji - Petroleum Geostatistics 2019, 2019 - earthdoc.org
Several studies on deep generative models for use in geomodeling show encouraging
results with binary training data. An important question is what type of training data to use …
results with binary training data. An important question is what type of training data to use …
[图书][B] Building informative priors for the subsurface with generative adversarial networks and graphs
E Nesvold - 2019 - search.proquest.com
Uncertainty quantification of flow in porous media in the subsurface is important for
applications such as aquifer management, CO 2 storage, hydrocarbon production and …
applications such as aquifer management, CO 2 storage, hydrocarbon production and …
[图书][B] Morphodynamic Analysis and Statistical Synthesis of Geomorphic Data
JH Mendes - 2018 - search.proquest.com
Many Earth-surface processes are studied using field, experimental, or numerical modeling
data sets that, although realistic, only represent a small subset of possible outcomes …
data sets that, although realistic, only represent a small subset of possible outcomes …
[图书][B] Uncertainty Quantification and Sensitivity Analysis of Geoscientific Predictions with Data-Driven Approaches
J Park - 2019 - search.proquest.com
Uncertainty quantification in the Earth Sciences forms an integral component in decision
making. Such decision has different objectives depending on the subsurface system. For …
making. Such decision has different objectives depending on the subsurface system. For …
A Bayesian Framework for Quantifying Fault Network Uncertainty Using a Marked Point Process Model
O Aydin - 2017 - search.proquest.com
Faults are one of the building-blocks for subsurface modeling studies. Incomplete
observations of subsurface fault networks lead to uncertainty pertaining to location …
observations of subsurface fault networks lead to uncertainty pertaining to location …