Matches in SemOpenAlex for { <https://semopenalex.org/work/W2902519853> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W2902519853 endingPage "815" @default.
- W2902519853 startingPage "811" @default.
- W2902519853 abstract "High-quality seismic data are the basis for stratigraphic imaging and interpretation, but the existence of random noise can greatly affect the quality of seismic data. At present, most understanding and processing of random noise still stay at the level of Gaussian white noise. With the reduction of resource, the acquired seismic data have lower signal-to-noise ratio and more complex noise natures. In particular, the random noise in the desert area has the characteristics of low frequency, non-Gaussian, nonstationary, high energy, and serious aliasing between effective signal and random noise in the frequency domain, which has brought great difficulties to the recovery of seismic events by conventional denoising methods. To solve this problem, an improved feed-forward denoising convolution neural network (DnCNN) is proposed to suppress random noise in desert seismic data. DnCNN has the characteristics of automatic feature extraction and blind denoising. According to the characteristics of desert noise, we modify the original DnCNN from the aspects of patch size, convolution kernel size, network depth, and training set to make it suitable for low-frequency and non-Gaussian desert noise suppression. Both simulation and practical experiments prove that the improved DnCNN has obvious advantages in terms of desert noise and surface wave suppression as well as effective signal amplitude preservation. In addition, the improved DnCNN, in contrast to existing methods, has considerable potential to benefit from large data sets. Therefore, we believe that it can open a new direction in the area of seismic data processing." @default.
- W2902519853 created "2018-12-11" @default.
- W2902519853 creator A5042967628 @default.
- W2902519853 creator A5044367029 @default.
- W2902519853 creator A5048033246 @default.
- W2902519853 creator A5087029680 @default.
- W2902519853 date "2019-05-01" @default.
- W2902519853 modified "2023-10-11" @default.
- W2902519853 title "Low-Frequency Noise Suppression Method Based on Improved DnCNN in Desert Seismic Data" @default.
- W2902519853 cites W1967469577 @default.
- W2902519853 cites W1971095230 @default.
- W2902519853 cites W2037133587 @default.
- W2902519853 cites W2312404985 @default.
- W2902519853 cites W2508457857 @default.
- W2902519853 cites W2510593588 @default.
- W2902519853 cites W2526357332 @default.
- W2902519853 cites W2592517375 @default.
- W2902519853 cites W2745993090 @default.
- W2902519853 cites W2769581371 @default.
- W2902519853 cites W2781854221 @default.
- W2902519853 cites W4240485910 @default.
- W2902519853 doi "https://doi.org/10.1109/lgrs.2018.2882058" @default.
- W2902519853 hasPublicationYear "2019" @default.
- W2902519853 type Work @default.
- W2902519853 sameAs 2902519853 @default.
- W2902519853 citedByCount "78" @default.
- W2902519853 countsByYear W29025198532019 @default.
- W2902519853 countsByYear W29025198532020 @default.
- W2902519853 countsByYear W29025198532021 @default.
- W2902519853 countsByYear W29025198532022 @default.
- W2902519853 countsByYear W29025198532023 @default.
- W2902519853 crossrefType "journal-article" @default.
- W2902519853 hasAuthorship W2902519853A5042967628 @default.
- W2902519853 hasAuthorship W2902519853A5044367029 @default.
- W2902519853 hasAuthorship W2902519853A5048033246 @default.
- W2902519853 hasAuthorship W2902519853A5087029680 @default.
- W2902519853 hasConcept C115961682 @default.
- W2902519853 hasConcept C121332964 @default.
- W2902519853 hasConcept C127313418 @default.
- W2902519853 hasConcept C154945302 @default.
- W2902519853 hasConcept C24890656 @default.
- W2902519853 hasConcept C41008148 @default.
- W2902519853 hasConcept C62649853 @default.
- W2902519853 hasConcept C99498987 @default.
- W2902519853 hasConceptScore W2902519853C115961682 @default.
- W2902519853 hasConceptScore W2902519853C121332964 @default.
- W2902519853 hasConceptScore W2902519853C127313418 @default.
- W2902519853 hasConceptScore W2902519853C154945302 @default.
- W2902519853 hasConceptScore W2902519853C24890656 @default.
- W2902519853 hasConceptScore W2902519853C41008148 @default.
- W2902519853 hasConceptScore W2902519853C62649853 @default.
- W2902519853 hasConceptScore W2902519853C99498987 @default.
- W2902519853 hasFunder F4320321001 @default.
- W2902519853 hasIssue "5" @default.
- W2902519853 hasLocation W29025198531 @default.
- W2902519853 hasOpenAccess W2902519853 @default.
- W2902519853 hasPrimaryLocation W29025198531 @default.
- W2902519853 hasRelatedWork W185733981 @default.
- W2902519853 hasRelatedWork W1984560925 @default.
- W2902519853 hasRelatedWork W2000794185 @default.
- W2902519853 hasRelatedWork W2032162268 @default.
- W2902519853 hasRelatedWork W2040499981 @default.
- W2902519853 hasRelatedWork W2089218326 @default.
- W2902519853 hasRelatedWork W2091474092 @default.
- W2902519853 hasRelatedWork W2100665530 @default.
- W2902519853 hasRelatedWork W2279382477 @default.
- W2902519853 hasRelatedWork W2992395496 @default.
- W2902519853 hasVolume "16" @default.
- W2902519853 isParatext "false" @default.
- W2902519853 isRetracted "false" @default.
- W2902519853 magId "2902519853" @default.
- W2902519853 workType "article" @default.