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Record W3196901741 · doi:10.1111/1365-2478.13142

Porosity variation of elastic wave velocities in clean sandstones

2021· article· en· W3196901741 on OpenAlexaff
Colin M. Sayers, Lennert D. den Boer

Bibliographic record

VenueGeophysical Prospecting · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsCalgary Construction Association
Fundersnot available
KeywordsPorosityElasticity (physics)Elastic modulusGeologyMineralogyMaterials scienceComposite materialGeotechnical engineering

Abstract

fetched live from OpenAlex

ABSTRACT The mechanical properties of sandstones, including porosity, density and elastic moduli, can be estimated non‐destructively through elastic wave‐velocity measurements. Here, the variation of elastic wave velocity with porosity in sandstones is modelled using Maxwell's effective field theory, extended to the elasticity of heterogeneous media by Sevostianov and coworkers. Comparing measured and predicted elastic wave velocities shows that on deposition, pores in sandstones are less stiff than spherical pores, but that their stiffness increases as porosity decreases. This suggests that concavity of pores in sandstone decreases with decreasing porosity. This interpretation is confirmed by the simple model of Sevostianov and Giraud in which concave pores are represented as superspherical pores, defined by a shape parameter that allows the effect of pore concavity on elastic wave velocities to be investigated. Inversion of measured velocities for this parameter indicates that pore concavity decreases with decreasing porosity. Moreover, values of the shape parameter obtained by inverting measured P‐velocities alone are found to give a good prediction of both P‐ and S‐wave velocities, confirming the applicability of the model.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.677

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 head0.206
Teacher spread0.193 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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