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Record W2086968765 · doi:10.1190/1.1816660

Seismic interpretation of sonic logs

2001· article· it· W2086968765 on OpenAlexaff
Jonathan Bork, Lawrence C. Wood

Bibliographic record

Venuenot available
Typearticle
Languageit
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsGeologyInterpretation (philosophy)SeismologyGeophysicsPetroleum engineeringComputer science

Abstract

fetched live from OpenAlex

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2001Seismic interpretation of sonic logsAuthors: Jonathan BorkLawrence C. WoodJonathan BorkApache Corporation and Lawrence C. WoodGeophysical Consulting Inc.https://doi.org/10.1190/1.1816660 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1816660FiguresReferencesRelatedDetailsCited ByMultichannel Semi-blind Deconvolution (MSBD) of seismic signalsSignal Processing, Vol. 135A prestack basis pursuit seismic inversionRui Zhang, Mrinal K Sen, and Sanjay Srinivasan10 December 2012 | GEOPHYSICS, Vol. 78, No. 1A pre-stack basis pursuit seismic inversionRui Zhang, Mrinal K Sen, and Sanjay Srinivasan25 October 2012Seismic sparse-layer reflectivity inversion using basis pursuit decompositionGEOPHYSICS, Vol. 76, No. 6 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: 03 Jan 2005 CITATION INFORMATION Jonathan Bork and Lawrence C. Wood, (2001), "Seismic interpretation of sonic logs," SEG Technical Program Expanded Abstracts : 510-513. https://doi.org/10.1190/1.1816660 Plain-Language Summary PDF DownloadLoading ...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.002

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.

Opus teacher head0.024
GPT teacher head0.265
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2001
Admission routes1
Has abstractyes

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Same topicUnderwater Acoustics ResearchFrench-language works237,207