Minimum weighted norm interpolation of seismic data with adaptive weights
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2001Minimum weighted norm interpolation of seismic data with adaptive weightsAuthors: Bin LiuMauricio D. SacchiBin LiuDepartment of Physics, University of Alberta and Mauricio D. SacchiDepartment of Physics, University of Albertahttps://doi.org/10.1190/1.1816511 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1816511FiguresReferencesRelatedDetailsCited ByPost-stack seismic data interpolation using a fast non-local similarity matching algorithm15 February 2021 | Studia Geophysica et Geodaetica, Vol. 65, No. 1Reconstruction of the near‐offset gap in marine seismic data using seismic interferometric interpolation1 December 2017 | Geophysical Prospecting, Vol. 66, No. S1Wavefield-based regularization of multicomponent seismic dataKhemraj Shukla and Priyank Jaiswal17 August 2017Multicomponent Seismic Complete Session17 August 2017Interpolation of near offset using surface-related multiples15 October 2011 | Applied Geophysics, Vol. 8, No. 320. Signal ProcessingP. M. Zwartjes, M. D. Sacchi, Sergey Fomel, Bill Dragoset, Eric Verschuur, Ian Moore, and Richard Bisley21 March 2012De‐alias seismic data reconstruction investigationYing Shi, Hong Liu, and Guofeng Liu14 October 2009Interpolation of near offsets using multiples and prediction‐error filtersWililam Curry and Guojian Shan15 December 2008Reconstruction of Seismic Data with Least Squares Inversion Based on Nonuniform Fast Fourier Transform31 May 2013 | Chinese Journal of Geophysics, Vol. 51, No. 1Shaping regularization in geophysical-estimation problemsSergey Fomel15 February 2007 | GEOPHYSICS, Vol. 72, No. 2Fourier reconstruction of nonuniformly sampled, aliased seismic dataP. M. Zwartjes and M. D. Sacchi29 December 2006 | GEOPHYSICS, Vol. 72, No. 1A Study on Reconstruction of De-Aliased Uneven Seismic Data31 May 2013 | Chinese Journal of Geophysics, Vol. 47, No. 2Simultaneous interpolation of 4 spatial dimensionsBin Liu, Mauricio D. Sacchi, and Daniel Trad3 January 2005Sparseness‐constrained least‐squares inversion: Application to seismic wave reconstructionYanghua Wang25 September 2003 | GEOPHYSICS, Vol. 68, No. 5 SEG Technical Program Expanded Abstracts 2001ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2001 Pages: 2135 publication data© 2001 Copyright © 2001 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 03 Jan 2005 CITATION INFORMATION Bin Liu and Mauricio D. Sacchi, (2001), "Minimum weighted norm interpolation of seismic data with adaptive weights," SEG Technical Program Expanded Abstracts : 1921-1924. https://doi.org/10.1190/1.1816511 Plain-Language Summary PDF DownloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".