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Record W2596814241 · doi:10.3997/2214-4609.201601570

Effects of Fracture Intersections on Seismic Dispersion - Theoretical Predictions Versus Numerical Simulations

2016· article· en· W2596814241 on OpenAlexaff
Junxin Guo, J. Germán Rubino, Boris Gurevich, Stanislav Glubokovskikh, Arcady Dyskin, Elena Pasternak

Bibliographic record

Venue78th EAGE Conference and Exhibition 2016 · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsMechanicsPorosityAnisotropyFracture (geology)Porous mediumSlip (aerodynamics)Matrix (chemical analysis)Limit (mathematics)Dispersion (optics)Low frequencyMaterials scienceGeotechnical engineeringGeologyMathematicsPhysicsMathematical analysisComposite materialThermodynamicsOptics

Abstract

fetched live from OpenAlex

Summary The objective of this study is to quantify the effects of fracture intersections on the frequency-dependent elastic properties of porous and fractured rocks. Three characteristic frequency ranges for fluid pressure communication are identified. In the low frequency limit, fractures are in full pressure communication with the porous matrix. In the high frequency limit, fractures are hydraulically isolated from the matrix and from each other. At intermediate frequencies, fractures are hydraulically isolated from the matrix porosity, but can be in hydraulic communication with each other, depending on whether fracture sets are intersecting. The theoretical solutions for each physical state are derived using linear-slip theory and anisotropic Gassmann equation. The theoretical predictions are then performed for two synthetic 2D samples each containing two orthogonal fracture sets, one with and the other without intersections. The results show good agreement with numerical simulations. The theoretical results are applicable not only to 2D but also to 3D fracture systems.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.999

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)0.0020.000

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 head0.011
GPT teacher head0.228
Teacher spread0.218 · 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.

Study designOther design
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

Citations2
Published2016
Admission routes1
Has abstractyes

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