MétaCan
Menu
Back to cohort
Record W2759093379 · doi:10.1190/tle36100852.1

Azimuthal AVO inversion of an elliptic orthorhombic medium

2017· article· en· W2759093379 on OpenAlexaboutno aff
David Cho, Klaus Bolding Rasmussen, Evan Mutual

Bibliographic record

VenueThe Leading Edge · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAnisotropyAmplitude versus offsetIsotropyAzimuthGeologyAmplitudeInversion (geology)Mathematical analysisComputational physicsGeophysicsGeometryPhysicsSeismologyMathematicsOptics

Abstract

fetched live from OpenAlex

Abstract Anisotropic information regarding the subsurface is important for various geophysical applications, including fracture characterization and estimation of the in situ stress field. Conventional techniques used to invert seismic amplitudes for anisotropic parameters estimate quantities such as the anisotropic gradient, which is not easily interpretable due to the amalgamation of various elastic stiffness coefficients. To gain an improved understanding of the anisotropic parameters estimated from azimuthal amplitude variation with offset (AVO) inversion, we simplify the PP reflection coefficient for a medium with arbitrary anisotropy by imposing an elliptic orthorhombic constraint. This results in a reduced form of the reflection coefficient in which the azimuthally dependent components are only a function of the horizontal P- and vertical S-wave velocities in a rotated coordinate frame. Furthermore, the anisotropic components are parameterized as direct perturbations from the isotropic solution, which requires no a priori information regarding the anisotropy and stabilizes the inversion. We demonstrate the utility of the elliptic orthorhombic approximation through an example in the Montney tight-gas play in northeast British Columbia, Canada, where we perform an azimuthal AVO inversion and estimate the crack density and differential stress ratio.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.031
GPT teacher head0.261
Teacher spread0.230 · 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 teacher head, 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe Leading EdgeSame topicSeismic Imaging and Inversion TechniquesFrench-language works237,207