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Record W2184234697

Velocity Anisotropy from Core Measurements, VSP and Sonic Logs Analysis in a Single Well Study in NE Alberta

2014· article· en· W2184234697 on OpenAlexaffabout
Judith Chan, Douglas R. Schmitt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnisotropyBoreholeGeologyIsotropyVertical seismic profileSeismic anisotropySeismic velocitySeismologyCore (optical fiber)Core sampleMineralogyGeophysicsGeotechnical engineeringOpticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

Summary The presence of fractures and textures of the subsurface rocks can cause imaging problems in the conventional seismic processing routine as a result of seismic anisotropy. Problems arise from the directional dependence of wave propagation that was unaccounted for in the original isotropic velocity model. To quantify the degree of seismic anisotropy in the subsurface, one method is by estimating the anisotropic parameters from laboratory measurements performed on a core sample. However, with the limited cores available from our borehole of interest in NE Alberta, additional field measurements of the walk-away vertical seismic profile (VSP) and sonic scanner were performed to obtain anisotropy measurements for the rest of the borehole. This case study examines the result of seismic anisotropy from three different types of data which indicates the presence of intrinsic anisotropy at various depth intervals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.233
Teacher spread0.198 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2014
Admission routes2
Has abstractyes

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