Simultaneous acquisition of 3D surface seismic and 3D VSP data — Processing and integration
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2002Simultaneous acquisition of 3D surface seismic and 3D VSP data — Processing and integrationAuthors: Satinder ChopraEmil BliasAbhi ManerikarAndrew KryzanLioudmila ChavinaVlaidmir AlexeevGlen LarsenSatinder ChopraScott Pickford, Calgary, Emil BliasScott Pickford, Calgary, Abhi ManerikarConoco Canada Resources Ltd, Calgary, Andrew KryzanConoco Canada Resources Ltd, Calgary, Lioudmila ChavinaScott Pickford, Calgary, Vlaidmir AlexeevScott Pickford, Calgary, and Glen LarsenScott Pickford, Calgaryhttps://doi.org/10.1190/1.1817183 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1817183FiguresReferencesRelatedDetailsCited ByGeophysical Prediction Technology Based on Organic Carbon Content in Source Rocks of the Huizhou Sag, the South China Sea13 September 2017 | Polish Maritime Research, Vol. 24, No. s23D VSP and surface full-azimuth seismic integrated study in northeastern ChinaYanpeng Li, Yun Ling, Ximing Wang, Yuanzhong Chen, and Junjun Wu5 August 2014 | The Leading Edge, Vol. 33, No. 8Combining borehole and surface seismic data for velocity field estimation through slope tomographyGeophysical Journal International, Vol. 176, No. 3 SEG Technical Program Expanded Abstracts 2002ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2002 Pages: 2478 publication data© 2002 Copyright © 2002 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 03 Jan 2005 CITATION INFORMATION Satinder Chopra, Emil Blias, Abhi Manerikar, Andrew Kryzan, Lioudmila Chavina, Vlaidmir Alexeev, and Glen Larsen, (2002), "Simultaneous acquisition of 3D surface seismic and 3D VSP data — Processing and integration," SEG Technical Program Expanded Abstracts : 2337-2340. https://doi.org/10.1190/1.1817183 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".