FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4229050416> ?p ?o ?g. }

• W4229050416 endingPage "1162" @default.
• W4229050416 startingPage "1143" @default.
• W4229050416 abstract "ABSTRACT Dictionary learning has been intensively applied to process multi‐channel seismic data due to its adaptively learned basis atoms that are data driven. Traditionally, dictionary learning is mostly used to attenuate random noise in the literature since the dictionary update operation is not sensitive to Gaussian noise. However, when dictionary learning is applied to seismic data containing strong erratic noise, which does not follow the Gaussian distribution, its performance greatly deteriorates. In this paper, we propose a novel robust dictionary learning method for dealing with both random and erratic noise. We formulate the dictionary‐learning‐sbased denoising problem as an iterative process. During each iteration, we gradually diminish the effect of the erratic noise and make the denoising problem more Gaussian type. Considering the computational overburden of the classic K‐singular value decomposition algorithm due to many iterations, we substitute the K‐singular value decomposition algorithm with an efficient algorithm, which does not require the singular value decomposition operation. We apply the proposed method to several synthetic and field datasets and obtain good performance, which demonstrates its potential for wide application." @default.
• W4229050416 created "2022-05-08" @default.
• W4229050416 creator A5035636323 @default.
• W4229050416 date "2022-06-16" @default.
• W4229050416 modified "2023-09-26" @default.
• W4229050416 title "Robust fast dictionary learning for seismic noise attenuation" @default.
• W4229050416 cites W1853998970 @default.
• W4229050416 cites W1981583278 @default.
• W4229050416 cites W1991265582 @default.
• W4229050416 cites W1996098514 @default.
• W4229050416 cites W2007221293 @default.
• W4229050416 cites W2011849692 @default.
• W4229050416 cites W2016572604 @default.
• W4229050416 cites W2020205028 @default.
• W4229050416 cites W2021647805 @default.
• W4229050416 cites W2036434572 @default.
• W4229050416 cites W2050551672 @default.
• W4229050416 cites W2050567721 @default.
• W4229050416 cites W2076393573 @default.
• W4229050416 cites W2082114499 @default.
• W4229050416 cites W2108473160 @default.
• W4229050416 cites W2118592408 @default.
• W4229050416 cites W2123046268 @default.
• W4229050416 cites W2138873374 @default.
• W4229050416 cites W2152582233 @default.
• W4229050416 cites W2160787559 @default.
• W4229050416 cites W2186316592 @default.
• W4229050416 cites W2281461674 @default.
• W4229050416 cites W2282024151 @default.
• W4229050416 cites W2329945299 @default.
• W4229050416 cites W2336215657 @default.
• W4229050416 cites W2403089413 @default.
• W4229050416 cites W2412205031 @default.
• W4229050416 cites W2483676302 @default.
• W4229050416 cites W2510593588 @default.
• W4229050416 cites W2552718926 @default.
• W4229050416 cites W2555981623 @default.
• W4229050416 cites W2608562242 @default.
• W4229050416 cites W2702787849 @default.
• W4229050416 cites W2744272590 @default.
• W4229050416 cites W2749520055 @default.
• W4229050416 cites W2771940404 @default.
• W4229050416 cites W2791699299 @default.
• W4229050416 cites W2794537117 @default.
• W4229050416 cites W2796445688 @default.
• W4229050416 cites W2897137065 @default.
• W4229050416 cites W2910355481 @default.
• W4229050416 cites W2912220525 @default.
• W4229050416 cites W2972711621 @default.
• W4229050416 cites W2975778935 @default.
• W4229050416 cites W2994652882 @default.
• W4229050416 cites W2999573048 @default.
• W4229050416 cites W3033329672 @default.
• W4229050416 cites W3033444937 @default.
• W4229050416 cites W3033557345 @default.
• W4229050416 cites W3048385479 @default.
• W4229050416 cites W3088102248 @default.
• W4229050416 cites W3091501369 @default.
• W4229050416 cites W3095311070 @default.
• W4229050416 cites W3116822623 @default.
• W4229050416 cites W3128525917 @default.
• W4229050416 cites W3129028618 @default.
• W4229050416 cites W3131639001 @default.
• W4229050416 cites W3158619653 @default.
• W4229050416 cites W3170168031 @default.
• W4229050416 cites W3196434791 @default.
• W4229050416 cites W4242453637 @default.
• W4229050416 doi "https://doi.org/10.1111/1365-2478.13217" @default.
• W4229050416 hasPublicationYear "2022" @default.
• W4229050416 type Work @default.
• W4229050416 citedByCount "1" @default.
• W4229050416 countsByYear W42290504162023 @default.
• W4229050416 crossrefType "journal-article" @default.
• W4229050416 hasAuthorship W4229050416A5035636323 @default.
• W4229050416 hasConcept C111919701 @default.
• W4229050416 hasConcept C11413529 @default.
• W4229050416 hasConcept C115961682 @default.
• W4229050416 hasConcept C121332964 @default.
• W4229050416 hasConcept C124066611 @default.
• W4229050416 hasConcept C153180895 @default.
• W4229050416 hasConcept C154771677 @default.
• W4229050416 hasConcept C154945302 @default.
• W4229050416 hasConcept C163294075 @default.
• W4229050416 hasConcept C163716315 @default.
• W4229050416 hasConcept C22789450 @default.
• W4229050416 hasConcept C41008148 @default.
• W4229050416 hasConcept C4199805 @default.
• W4229050416 hasConcept C62520636 @default.
• W4229050416 hasConcept C98045186 @default.
• W4229050416 hasConcept C99498987 @default.
• W4229050416 hasConceptScore W4229050416C111919701 @default.
• W4229050416 hasConceptScore W4229050416C11413529 @default.
• W4229050416 hasConceptScore W4229050416C115961682 @default.
• W4229050416 hasConceptScore W4229050416C121332964 @default.
• W4229050416 hasConceptScore W4229050416C124066611 @default.
• W4229050416 hasConceptScore W4229050416C153180895 @default.
• W4229050416 hasConceptScore W4229050416C154771677 @default.
• W4229050416 hasConceptScore W4229050416C154945302 @default.

• next