FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3212838683> ?p ?o ?g. }

• W3212838683 endingPage "M58" @default.
• W3212838683 startingPage "M43" @default.
• W3212838683 abstract "Estimating rock and fluid properties in the subsurface from geophysical measurements is a computationally and memory-intensive inverse problem. For nonlinear problems with non-Gaussian variables, analytical solutions are generally not available, and the solutions of those inverse problems must be approximated using sampling and optimization methods. To reduce the computational cost, model and data can be reparameterized into low-dimensional spaces where the solution of the inverse problem can be computed more efficiently. Among the potential dimensionality reduction methods, deep-learning algorithms based on deep generative models provide an efficient approach to reduce the dimension of the model and data vectors. However, such dimension reduction might lead to information loss in the reconstructed model and data, reduction of the accuracy and resolution of the inverted models, and under- or overestimation of the uncertainty of the predicted models. To comprehensively investigate the impact of model and data dimension reduction with deep generative models on uncertainty quantification, we compare the prediction uncertainty in nonlinear inverse problem solutions obtained from Markov chain Monte Carlo and ensemble-based data assimilation methods implemented in lower dimensional data and model spaces using a deep variational autoencoder. Our workflow is applied to two geophysical inverse problems for the prediction of reservoir properties: prestack seismic inversion and seismic history matching. The inversion results consist of the most likely model and a set of realizations of the variables of interest. The application of dimensionality reduction methods makes the stochastic inversion more efficient." @default.
• W3212838683 created "2021-11-22" @default.
• W3212838683 creator A5047415531 @default.
• W3212838683 creator A5056979330 @default.
• W3212838683 creator A5083974016 @default.
• W3212838683 date "2021-12-27" @default.
• W3212838683 modified "2023-10-15" @default.
• W3212838683 title "Uncertainty quantification in stochastic inversion with dimensionality reduction using variational autoencoder" @default.
• W3212838683 cites W15242090 @default.
• W3212838683 cites W1983452151 @default.
• W3212838683 cites W2004124997 @default.
• W3212838683 cites W2018353504 @default.
• W3212838683 cites W2029164135 @default.
• W3212838683 cites W2082667383 @default.
• W3212838683 cites W2098550720 @default.
• W3212838683 cites W2518891195 @default.
• W3212838683 cites W2547561522 @default.
• W3212838683 cites W2584866998 @default.
• W3212838683 cites W2762902720 @default.
• W3212838683 cites W2783586977 @default.
• W3212838683 cites W2883423724 @default.
• W3212838683 cites W2917737173 @default.
• W3212838683 cites W2947856336 @default.
• W3212838683 cites W2950337752 @default.
• W3212838683 cites W2963162215 @default.
• W3212838683 cites W2964169269 @default.
• W3212838683 cites W2969410691 @default.
• W3212838683 cites W2970903678 @default.
• W3212838683 cites W2981785001 @default.
• W3212838683 cites W2987357275 @default.
• W3212838683 cites W3012431717 @default.
• W3212838683 cites W3029050805 @default.
• W3212838683 cites W3080352642 @default.
• W3212838683 cites W3080353413 @default.
• W3212838683 cites W3082717723 @default.
• W3212838683 cites W3090539257 @default.
• W3212838683 cites W3123551284 @default.
• W3212838683 cites W4211024465 @default.
• W3212838683 cites W4231204432 @default.
• W3212838683 doi "https://doi.org/10.1190/geo2021-0138.1" @default.
• W3212838683 hasPublicationYear "2021" @default.
• W3212838683 type Work @default.
• W3212838683 sameAs 3212838683 @default.
• W3212838683 citedByCount "11" @default.
• W3212838683 countsByYear W32128386832022 @default.
• W3212838683 countsByYear W32128386832023 @default.
• W3212838683 crossrefType "journal-article" @default.
• W3212838683 hasAuthorship W3212838683A5047415531 @default.
• W3212838683 hasAuthorship W3212838683A5056979330 @default.
• W3212838683 hasAuthorship W3212838683A5083974016 @default.
• W3212838683 hasConcept C101738243 @default.
• W3212838683 hasConcept C108583219 @default.
• W3212838683 hasConcept C109007969 @default.
• W3212838683 hasConcept C111030470 @default.
• W3212838683 hasConcept C111335779 @default.
• W3212838683 hasConcept C11413529 @default.
• W3212838683 hasConcept C119857082 @default.
• W3212838683 hasConcept C121332964 @default.
• W3212838683 hasConcept C126255220 @default.
• W3212838683 hasConcept C127313418 @default.
• W3212838683 hasConcept C134306372 @default.
• W3212838683 hasConcept C135252773 @default.
• W3212838683 hasConcept C151730666 @default.
• W3212838683 hasConcept C153294291 @default.
• W3212838683 hasConcept C154945302 @default.
• W3212838683 hasConcept C158622935 @default.
• W3212838683 hasConcept C1893757 @default.
• W3212838683 hasConcept C24552861 @default.
• W3212838683 hasConcept C2524010 @default.
• W3212838683 hasConcept C32230216 @default.
• W3212838683 hasConcept C33923547 @default.
• W3212838683 hasConcept C41008148 @default.
• W3212838683 hasConcept C62520636 @default.
• W3212838683 hasConcept C70518039 @default.
• W3212838683 hasConceptScore W3212838683C101738243 @default.
• W3212838683 hasConceptScore W3212838683C108583219 @default.
• W3212838683 hasConceptScore W3212838683C109007969 @default.
• W3212838683 hasConceptScore W3212838683C111030470 @default.
• W3212838683 hasConceptScore W3212838683C111335779 @default.
• W3212838683 hasConceptScore W3212838683C11413529 @default.
• W3212838683 hasConceptScore W3212838683C119857082 @default.
• W3212838683 hasConceptScore W3212838683C121332964 @default.
• W3212838683 hasConceptScore W3212838683C126255220 @default.
• W3212838683 hasConceptScore W3212838683C127313418 @default.
• W3212838683 hasConceptScore W3212838683C134306372 @default.
• W3212838683 hasConceptScore W3212838683C135252773 @default.
• W3212838683 hasConceptScore W3212838683C151730666 @default.
• W3212838683 hasConceptScore W3212838683C153294291 @default.
• W3212838683 hasConceptScore W3212838683C154945302 @default.
• W3212838683 hasConceptScore W3212838683C158622935 @default.
• W3212838683 hasConceptScore W3212838683C1893757 @default.
• W3212838683 hasConceptScore W3212838683C24552861 @default.
• W3212838683 hasConceptScore W3212838683C2524010 @default.
• W3212838683 hasConceptScore W3212838683C32230216 @default.
• W3212838683 hasConceptScore W3212838683C33923547 @default.
• W3212838683 hasConceptScore W3212838683C41008148 @default.
• W3212838683 hasConceptScore W3212838683C62520636 @default.
• W3212838683 hasConceptScore W3212838683C70518039 @default.

• next