Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2003P‐S converted‐wave AVOAuthors: David GrayDavid GrayVeritas DGChttps://doi.org/10.1190/1.1817623 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1817623FiguresReferencesRelatedDetailsCited byQuasielastic least-squares reverse time migration of PS reflectionsZongcai Feng and Lianjie Huang10 March 2022 | GEOPHYSICS, Vol. 87, No. 3SH-SH wave inversion for S-wave velocity and densityFucai Dai, Feng Zhang, and Xiangyang Li23 February 2022 | GEOPHYSICS, Vol. 87, No. 3Quasi-elastic least-squares reverse-time migration of PS reflectionsZongcai Feng and Lianjie Huang30 September 2020A preliminary look at joint PP-PS-SS inversion in native time domainBenjamin Roure and Brian Russell10 August 2019Pre-stack seismic density inversion in marine shale reservoirs in the southern Jiaoshiba area, Sichuan Basin, China26 July 2018 | Petroleum Science, Vol. 15, No. 3SV-P: A potential viable alternative to mode-converted P-SV seismic data for reservoir characterizationMenal Gupta and Bob Hardage25 September 2017 | Interpretation, Vol. 5, No. 4Prestack density inversion based on integrated norm regularization in shale reservoirYuanyin Zhang, Zhijun Jin, Yequan Chen, and Xiwu Liu17 August 2017Reservoir quality indicators from well logs and 3D–3-C seismic in the Marcellus ShaleFabiola Ruiz and Robert Stewart1 September 2016Reservoir Characterization II Complete Session1 September 201612. Imaging Oil-Sands Reservoir Heterogeneities Using Wide-Angle Prestack Seismic InversionBaishali Roy, Phil Anno, and Michael Gurch21 March 2012Inversion of velocity ratio Vp/Vs from converted‐wave AVOTiansheng Chen and Xiucheng Wei14 September 2007Prediction of Shale Plugs between Wells in Heavy Oil Sands using Seismic Attributes8 December 2006 | Natural Resources Research, Vol. 15, No. 2Wide‐angle inversion for density: Tests for heavy‐oil reservoir characterizationBaishali Roy, Phil Anno, and Michael Gurch6 October 2006A proposed workflow for reservoir characterization using multicomponent seismic dataPaul F. Anderson, Louis Chabot, and F. David Gray7 December 2005Examination of wide‐angle, multi‐component, AVO attributes for prediction of shale in heavy oil sands: A case study from the Long Lake Project, Alberta, CanadaDavid Gray, Paul Anderson, and Jay Gunderson3 January 2005 SEG Technical Program Expanded Abstracts 2003ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2003 Pages: 2452 publication data© 2003 Copyright © 2003 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 03 Jan 2005 CITATION INFORMATION David Gray, (2003), "P‐S converted‐wave AVO," SEG Technical Program Expanded Abstracts : 165-168. https://doi.org/10.1190/1.1817623 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.169 | 0.072 |
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".