FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3198883669> ?p ?o ?g. }

• W3198883669 endingPage "13" @default.
• W3198883669 startingPage "1" @default.
• W3198883669 abstract "Seismic data denoising has always been an indispensable step in the seismic exploration workflow. The quality of the results directly affects the results of subsequent inversion and migration imaging. In this article, we proposed a fast and flexible convolutional neural network (FFCNN) based on DnCNN. In contrast to the existing DnCNN and other artificial intelligence (AI)-based denoisers, FFCNN enjoys several desirable properties: 1) downsampling and upscaling operations, which can sensibly reduce runtimes and memory requirements while maintaining the denoising performance, and 2) we introduced noised level maps, which can make that a single convolutional neural network (CNN) model is expected to inherit the flexibility of handling noise models with different parameters, even spatially variant noises by noting <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$M$ </tex-math></inline-formula> can be nonuniform. Another benefit of increasing the noise-level map is that we can preserve more useful seismic data information by controlling the tradeoff of noise removal effect and seismic data detail preservation. For real seismic data denoised work, the main work and advantages of this article are concentrated on the following two aspects: 1) we introduced a data augmentation strategy to overcome the lack of well-labeled samples and 2) transfer learning has been introduced to the training processing, which used the well-trained synthetic seismic data denoising network as a pretrained model. In this way, we can greatly accelerate and optimize the learning efficiency of the training network. Ultimately, we can greatly improve the computational efficiency and denoising performance based on this intelligent denoised network FFCNN. Finally, numerical experiments prove the effectiveness of our method in synthetic and real seismic data" @default.
• W3198883669 created "2021-09-13" @default.
• W3198883669 creator A5032764121 @default.
• W3198883669 creator A5057287134 @default.
• W3198883669 creator A5079665734 @default.
• W3198883669 date "2022-01-01" @default.
• W3198883669 modified "2023-10-15" @default.
• W3198883669 title "A Deep Learning Method for Denoising Based on a Fast and Flexible Convolutional Neural Network" @default.
• W3198883669 cites W1806891645 @default.
• W3198883669 cites W1853998970 @default.
• W3198883669 cites W1997837299 @default.
• W3198883669 cites W2013913026 @default.
• W3198883669 cites W2014311222 @default.
• W3198883669 cites W2020205028 @default.
• W3198883669 cites W2049567303 @default.
• W3198883669 cites W2076393573 @default.
• W3198883669 cites W2093762864 @default.
• W3198883669 cites W2103559027 @default.
• W3198883669 cites W2112029411 @default.
• W3198883669 cites W2112796928 @default.
• W3198883669 cites W2123451623 @default.
• W3198883669 cites W2136922672 @default.
• W3198883669 cites W2160814043 @default.
• W3198883669 cites W2170765642 @default.
• W3198883669 cites W2417420177 @default.
• W3198883669 cites W2601307685 @default.
• W3198883669 cites W2605232094 @default.
• W3198883669 cites W2618530766 @default.
• W3198883669 cites W2726304302 @default.
• W3198883669 cites W2891111066 @default.
• W3198883669 cites W2891932361 @default.
• W3198883669 cites W2904005001 @default.
• W3198883669 cites W2906893282 @default.
• W3198883669 cites W2912052494 @default.
• W3198883669 cites W2962793481 @default.
• W3198883669 cites W2995302838 @default.
• W3198883669 cites W3104725225 @default.
• W3198883669 cites W3105798281 @default.
• W3198883669 cites W3125476449 @default.
• W3198883669 cites W4242059867 @default.
• W3198883669 doi "https://doi.org/10.1109/tgrs.2021.3073001" @default.
• W3198883669 hasPublicationYear "2022" @default.
• W3198883669 type Work @default.
• W3198883669 sameAs 3198883669 @default.
• W3198883669 citedByCount "13" @default.
• W3198883669 countsByYear W31988836692022 @default.
• W3198883669 countsByYear W31988836692023 @default.
• W3198883669 crossrefType "journal-article" @default.
• W3198883669 hasAuthorship W3198883669A5032764121 @default.
• W3198883669 hasAuthorship W3198883669A5057287134 @default.
• W3198883669 hasAuthorship W3198883669A5079665734 @default.
• W3198883669 hasConcept C105795698 @default.
• W3198883669 hasConcept C108583219 @default.
• W3198883669 hasConcept C110384440 @default.
• W3198883669 hasConcept C115961682 @default.
• W3198883669 hasConcept C119857082 @default.
• W3198883669 hasConcept C153180895 @default.
• W3198883669 hasConcept C154945302 @default.
• W3198883669 hasConcept C163294075 @default.
• W3198883669 hasConcept C177212765 @default.
• W3198883669 hasConcept C2780598303 @default.
• W3198883669 hasConcept C33923547 @default.
• W3198883669 hasConcept C41008148 @default.
• W3198883669 hasConcept C50644808 @default.
• W3198883669 hasConcept C77088390 @default.
• W3198883669 hasConcept C81363708 @default.
• W3198883669 hasConcept C99498987 @default.
• W3198883669 hasConceptScore W3198883669C105795698 @default.
• W3198883669 hasConceptScore W3198883669C108583219 @default.
• W3198883669 hasConceptScore W3198883669C110384440 @default.
• W3198883669 hasConceptScore W3198883669C115961682 @default.
• W3198883669 hasConceptScore W3198883669C119857082 @default.
• W3198883669 hasConceptScore W3198883669C153180895 @default.
• W3198883669 hasConceptScore W3198883669C154945302 @default.
• W3198883669 hasConceptScore W3198883669C163294075 @default.
• W3198883669 hasConceptScore W3198883669C177212765 @default.
• W3198883669 hasConceptScore W3198883669C2780598303 @default.
• W3198883669 hasConceptScore W3198883669C33923547 @default.
• W3198883669 hasConceptScore W3198883669C41008148 @default.
• W3198883669 hasConceptScore W3198883669C50644808 @default.
• W3198883669 hasConceptScore W3198883669C77088390 @default.
• W3198883669 hasConceptScore W3198883669C81363708 @default.
• W3198883669 hasConceptScore W3198883669C99498987 @default.
• W3198883669 hasFunder F4320321001 @default.
• W3198883669 hasFunder F4320335765 @default.
• W3198883669 hasLocation W31988836691 @default.
• W3198883669 hasOpenAccess W3198883669 @default.
• W3198883669 hasPrimaryLocation W31988836691 @default.
• W3198883669 hasRelatedWork W2062399876 @default.
• W3198883669 hasRelatedWork W2281134365 @default.
• W3198883669 hasRelatedWork W2607795551 @default.
• W3198883669 hasRelatedWork W3029198973 @default.
• W3198883669 hasRelatedWork W3133861977 @default.
• W3198883669 hasRelatedWork W3155117723 @default.
• W3198883669 hasRelatedWork W3167935049 @default.
• W3198883669 hasRelatedWork W3193565141 @default.
• W3198883669 hasRelatedWork W4226493464 @default.
• W3198883669 hasRelatedWork W4312417841 @default.

• next