FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200316594> ?p ?o ?g. }

• W4200316594 abstract "Abstract Automatic fracture recognition from borehole images or outcrops is applicable for the construction of fractured reservoir models. Deep learning for fracture recognition is subject to uncertainty due to sparse and imbalanced training set, and random initialization. We present a new workflow to optimize a deep learning model under uncertainty using U-Net. We consider both epistemic and aleatoric uncertainty of the model. We propose a U-Net architecture by inserting dropout layer after every weighting layer. We vary the dropout probability to investigate its impact on the uncertainty response. We build the training set and assign uniform distribution for each training parameter, such as the number of epochs, batch size, and learning rate. We then perform uncertainty quantification by running the model multiple times for each realization, where we capture the aleatoric response. In this approach, which is based on Monte Carlo Dropout, the variance map and F1-scores are utilized to evaluate the need to craft additional augmentations or stop the process. This work demonstrates the existence of uncertainty within the deep learning caused by sparse and imbalanced training sets. This issue leads to unstable predictions. The overall responses are accommodated in the form of aleatoric uncertainty. Our workflow utilizes the uncertainty response (variance map) as a measure to craft additional augmentations in the training set. High variance in certain features denotes the need to add new augmented images containing the features, either through affine transformation (rotation, translation, and scaling) or utilizing similar images. The augmentation improves the accuracy of the prediction, reduces the variance prediction, and stabilizes the output. Architecture, number of epochs, batch size, and learning rate are optimized under a fixed-uncertain training set. We perform the optimization by searching the global maximum of accuracy after running multiple realizations. Besides the quality of the training set, the learning rate is the heavy-hitter in the optimization process. The selected learning rate controls the diffusion of information in the model. Under the imbalanced condition, fast learning rates cause the model to miss the main features. The other challenge in fracture recognition on a real outcrop is to optimally pick the parental images to generate the initial training set. We suggest picking images from multiple sides of the outcrop, which shows significant variations of the features. This technique is needed to avoid long iteration within the workflow. We introduce a new approach to address the uncertainties associated with the training process and with the physical problem. The proposed approach is general in concept and can be applied to various deep-learning problems in geoscience." @default.
• W4200316594 created "2021-12-31" @default.
• W4200316594 creator A5009325358 @default.
• W4200316594 creator A5059613649 @default.
• W4200316594 creator A5067974945 @default.
• W4200316594 creator A5072179993 @default.
• W4200316594 creator A5084996089 @default.
• W4200316594 date "2021-12-15" @default.
• W4200316594 modified "2023-09-27" @default.
• W4200316594 title "Uncertainty Quantification and Optimization of Deep Learning for Fracture Recognition" @default.
• W4200316594 cites W2021011596 @default.
• W4200316594 cites W2083467291 @default.
• W4200316594 cites W2126974166 @default.
• W4200316594 cites W2145830838 @default.
• W4200316594 cites W2146985112 @default.
• W4200316594 cites W2205745736 @default.
• W4200316594 cites W2803782485 @default.
• W4200316594 cites W2805500849 @default.
• W4200316594 cites W2919721891 @default.
• W4200316594 cites W2986037633 @default.
• W4200316594 cites W3021555480 @default.
• W4200316594 cites W3103992808 @default.
• W4200316594 cites W3175835099 @default.
• W4200316594 doi "https://doi.org/10.2118/204863-ms" @default.
• W4200316594 hasPublicationYear "2021" @default.
• W4200316594 type Work @default.
• W4200316594 citedByCount "1" @default.
• W4200316594 countsByYear W42003165942022 @default.
• W4200316594 crossrefType "proceedings-article" @default.
• W4200316594 hasAuthorship W4200316594A5009325358 @default.
• W4200316594 hasAuthorship W4200316594A5059613649 @default.
• W4200316594 hasAuthorship W4200316594A5067974945 @default.
• W4200316594 hasAuthorship W4200316594A5072179993 @default.
• W4200316594 hasAuthorship W4200316594A5084996089 @default.
• W4200316594 hasConcept C105795698 @default.
• W4200316594 hasConcept C108583219 @default.
• W4200316594 hasConcept C114466953 @default.
• W4200316594 hasConcept C119857082 @default.
• W4200316594 hasConcept C154945302 @default.
• W4200316594 hasConcept C199360897 @default.
• W4200316594 hasConcept C2776145597 @default.
• W4200316594 hasConcept C2781089630 @default.
• W4200316594 hasConcept C32230216 @default.
• W4200316594 hasConcept C33923547 @default.
• W4200316594 hasConcept C41008148 @default.
• W4200316594 hasConceptScore W4200316594C105795698 @default.
• W4200316594 hasConceptScore W4200316594C108583219 @default.
• W4200316594 hasConceptScore W4200316594C114466953 @default.
• W4200316594 hasConceptScore W4200316594C119857082 @default.
• W4200316594 hasConceptScore W4200316594C154945302 @default.
• W4200316594 hasConceptScore W4200316594C199360897 @default.
• W4200316594 hasConceptScore W4200316594C2776145597 @default.
• W4200316594 hasConceptScore W4200316594C2781089630 @default.
• W4200316594 hasConceptScore W4200316594C32230216 @default.
• W4200316594 hasConceptScore W4200316594C33923547 @default.
• W4200316594 hasConceptScore W4200316594C41008148 @default.
• W4200316594 hasLocation W42003165941 @default.
• W4200316594 hasOpenAccess W4200316594 @default.
• W4200316594 hasPrimaryLocation W42003165941 @default.
• W4200316594 hasRelatedWork W2964059111 @default.
• W4200316594 hasRelatedWork W3186919929 @default.
• W4200316594 hasRelatedWork W4220753921 @default.
• W4200316594 hasRelatedWork W4223943233 @default.
• W4200316594 hasRelatedWork W4280592718 @default.
• W4200316594 hasRelatedWork W4293152273 @default.
• W4200316594 hasRelatedWork W4312200629 @default.
• W4200316594 hasRelatedWork W4312863455 @default.
• W4200316594 hasRelatedWork W4360585206 @default.
• W4200316594 hasRelatedWork W582134693 @default.
• W4200316594 isParatext "false" @default.
• W4200316594 isRetracted "false" @default.
• W4200316594 workType "article" @default.