MétaCan
Menu
← Back to cohort
Record W2320493319 · doi:10.1190/segam2015-5852794.1

Estimation of fast and slow shear wave velocities from P-wave data: A Montney case study

2015· article· en· W2320493319 on OpenAlexaboutno aff
Evan Mutual, David Cho, Mark Norton, David S. Miller

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyShear (geology)

Abstract

fetched live from OpenAlex

Summary Optimization of hydraulic fracture treatments in tight gas reservoirs requires an in-depth understanding of the reservoir conditions. Elastic properties estimated with standard isotropic amplitude variation with offset (AVO) inversions can offer insight into areas that may be more susceptible to brittle deformation, but do not provide information regarding natural fracture networks or differential stress fields in the rock mass. A more complete characterization therefore requires estimates of anisotropy to account for these phenomena. Azimuthal AVO inversion makes use of changes in reflection amplitudes with angle and azimuth to extract estimates of anisotropic parameters from seismic data. This case study of the Lower Triassic Montney Formation in NE British Columbia, Canada, showcases an anisotropic extension to the isotropic AVO inversion to include estimates of the fast and slow shear wave velocities. The corresponding ratio of fast and slow shear wave velocities then provides an indication of anisotropy, from which inferences can be made regarding the presence of natural fractures or differential stress fields within the reservoir.

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)0.002
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 score0.512
Threshold uncertainty score0.981

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.262
Teacher spread0.170 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicSeismic Imaging and Inversion Techniques→French-language works237,207→