Time‐frequency attribute of seismic data using continuous wavelet transform
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2003Time‐frequency attribute of seismic data using continuous wavelet transformAuthors: Satish K. SinhaPartha S. RouthPhil D. AnnoJohn P. CastagnaSatish K. SinhaSchool of Geology and Geophysics, University of Oklahoma, Norman, OK, Partha S. RouthDept. of Geosciences, Boise State University, Boise, ID, Phil D. AnnoSeismic Imaging and Prediction, ConocoPhillips., Houston, TX, and John P. CastagnaInst. of Exploration and Development Geosciences, University of Oklahoma, Norman, OKhttps://doi.org/10.1190/1.1817573 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1817573FiguresReferencesRelatedDetailsCited ByEnhancing geological features delineation by combining a relative geological time model with the matching pursuit spectral decompositionFabien Cubizolle, Benjamin Durot, and Lory Evano15 August 2022Improved W-Transform Incorporating Fast Matching Pursuit DecompositionIEEE Geoscience and Remote Sensing Letters, Vol. 19Lithologic Reservoir Prediction Using Relative Energy Change Rate31 January 2020 | Arabian Journal for Science and Engineering, Vol. 45, No. 6Wavelet-domain reverse time migration image enhancement using inversion-based imaging conditionHong Liang and Houzhu Zhang12 August 2019 | GEOPHYSICS, Vol. 84, No. 5Broadband technology; history and remaining challenges from an end-user perspectiveJoseph M. Reilly19 August 2015High frequency recovery via spectral match between surface seismic and crosswell seismic dataXiaolong Zhao, Guochen Wu, Xingyao Yin, and Danping Cao19 August 2013Spectral decomposition for mapping old oil sand channel, Alberta, CanadaMohammed Farfour and Wang Jung Yoon29 October 2012Spectral-decomposition response to reservoir fluids from a deepwater West Africa reservoirGanglin Chen, Gianni Matteucci, Bill Fahmy, and Chris Finn24 October 2008 | GEOPHYSICS, Vol. 73, No. 66. Spectral Decomposition and Wavelet Transforms21 March 2012Scale attributes from continuous wavelet transformSatish K. Sinha, Partha S. Routh, Phil D. Anno, and John P. Castagna7 December 2005Matching pursuit decomposition using Morlet waveletsJianlei Liu and Kurt J. Marfurt7 December 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: 03 Jan 2005 CITATION INFORMATION Satish K. Sinha, Partha S. Routh, Phil D. Anno, and John P. Castagna, (2003), "Time‐frequency attribute of seismic data using continuous wavelet transform," SEG Technical Program Expanded Abstracts : 1481-1484. https://doi.org/10.1190/1.1817573 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".