Improving AVO and reflection tomography through use of local dip and azimuth
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2001Improving AVO and reflection tomography through use of local dip and azimuthAuthors: Francis SherrillSylvestre CharlesMarta WoodwardM. K. SenguptaFrancis SherrillWesternGeco, Sylvestre CharlesWesternGeco, Marta WoodwardWesternGeco, and M. K. SenguptaWesternGecohttps://doi.org/10.1190/1.1816590 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1816590FiguresReferencesRelatedDetailsCited byData-driven tomographic velocity analysis in tilted transversely isotropic media: A 3D case history from the Canadian FoothillsSylvestre Charles, David R. Mitchell, Rob A. Holt, Jiwu Lin, and John Mathewson1 October 2008 | GEOPHYSICS, Vol. 73, No. 5Residual moveout estimation and application to AVO, stack enhancement, and tomographyFrancis Sherrill, Arturo Ramirez, Dave Nichols, and Kevin Bishop7 December 2005 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 Online: 03 Jan 2005 CITATION INFORMATION Francis Sherrill, Sylvestre Charles, Marta Woodward, and M. K. Sengupta, (2001), "Improving AVO and reflection tomography through use of local dip and azimuth," SEG Technical Program Expanded Abstracts : 273-276. https://doi.org/10.1190/1.1816590 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".