Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2000Robust refraction tomographyAuthors: Konstantin OsypovKonstantin OsypovWestern Geophysical Co., Denver. Coloradohttps://doi.org/10.1190/1.1815841 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1815841FiguresReferencesRelatedDetailsCited byDirectional Total Variation Regularized High-Resolution Prestack AVA InversionIEEE Transactions on Geoscience and Remote Sensing, Vol. 60High-resolution seismic velocity analysis by sign-based weighted semblanceM. Javad Khoshnavaz30 September 2021 | GEOPHYSICS, Vol. 86, No. 6Geological Structure-Guided Initial Model Building for Prestack AVO/AVA InversionIEEE Transactions on Geoscience and Remote Sensing, Vol. 59, No. 2Application of tomographic static correction method without ray tracing in Piedmont area of Western ChinaJie Wu, Shengtao Zang, Xiaowei Wang, Jiaqing Sun, and Huan Yuan10 August 2019CMP-based near-surface velocity model building for mountainous areaWen Peng, Jianpeng Yu, and Dan Chen27 August 2018Geological structure guided well log interpolation for high-fidelity full waveform inversion14 September 2016 | Geophysical Journal International, Vol. 207, No. 2Pitfalls in processing near-surface reflection-seismic data: Beware of static corrections and migrationW. Frei, R. Bauer, Ph. Corboz, and D. Martin3 November 2015 | The Leading Edge, Vol. 34, No. 11Velocity analysis using similarity-weighted semblanceYangkang Chen, Tingting Liu, and Xiaohong Chen10 June 2015 | GEOPHYSICS, Vol. 80, No. 4Velocity analysis using similarity-weighted semblanceTingting Liu*, Xiaohong Chen, and Yangkang Chen5 August 2014First-break traveltime tomography with the double-square-root eikonal equationSiwei Li, Alexander Vladimirsky, and Sergey Fomel9 October 2013 | GEOPHYSICS, Vol. 78, No. 6Prestack first-break traveltime tomography using the double-square-root eikonal equationSiwei Li, Sergey Fomel, and Alexander Vladimirsky25 October 2012Refraction tomography statics without ray tracing for rugged topographyHu Ziduo, Wang Xiwen, Wang Shujiang, Wang Yuchao, and Yong Yundong21 October 2010Data-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. 5An integrated workflow for imaging below shallow gas: A Trinidad case studyMehmet C. Tanis, Ole J. Askim, Steve Lancaster, Gavin Ward, Miro Gainski, Vishal Nagassar, Chung‐Chi Shih, and Luis Canales6 October 2006 SEG Technical Program Expanded Abstracts 2000ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2000 Pages: 2484 publication data© 2000 Copyright © 2000 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 04 Jan 2005 CITATION INFORMATION Konstantin Osypov, (2000), "Robust refraction tomography," SEG Technical Program Expanded Abstracts : 2032-2035. https://doi.org/10.1190/1.1815841 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".