MétaCan
Menu
Back to cohort
Record W2067444807 · doi:10.1190/1.1620623

An inclusion-based model of elastic wave velocities incorporating patch-scale fluid pressure relaxation

2003· article· en· W2067444807 on OpenAlexafffund
Stephen R. Taylor, Rosemary Knight

Bibliographic record

VenueGeophysics · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsPorous mediumModuliMechanicsElastic modulusAttenuationFlow (mathematics)Relaxation (psychology)GeologyPorosityScale (ratio)Materials sciencePoromechanicsWave propagationPhysicsGeotechnical engineeringOpticsComposite material

Abstract

fetched live from OpenAlex

Abstract We consider elastic wave velocities in fluid-saturated porous media with pore fluids distributed in “patches” (i.e., heterogeneity much larger than the typical pore size). We model elastic properties of such materials using inclusion-based effective medium theory (IBEMT). The standard IBEMT formulation assumes insufficient time during the wave cycle for pore fluids to flow in response to wave-induced pressure gradients. Our approach accounts for this flow, incorporating wave-frequency dependent flow effects in the definition of effective elastic moduli for patches. Effective moduli are used in conjunction with IBEMT to estimate elastic moduli of the composite material. In the low- and high-frequency limits, the model reproduces previous theoretical results. At intermediate frequencies, it yields results qualitatively similar to other patch-scale models. We demonstrate this approach, estimating elastic P-wave velocities and attenuation in a porous rock that simultaneously contains fluid-saturated patches of different sizes.

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 categoriesnone
Consensus categoriesnone
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 score0.120
Threshold uncertainty score0.395

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)0.0000.000

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 head0.013
GPT teacher head0.200
Teacher spread0.186 · 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 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

Citations11
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueGeophysicsSame topicSeismic Imaging and Inversion TechniquesFrench-language works237,207