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Record W2147543241 · doi:10.1177/1464419313488465

Three-dimensional shear and compressional wave propagation of multiple point sources in fluid-saturated elastic porous media

2013· article· en· W2147543241 on OpenAlexaff
Lu Han, Liming Dai

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part K Journal of Multi-body Dynamics · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsLongitudinal wavePoint sourceShear (geology)MechanicsShear wavesWave propagationPorous mediumDisplacement (psychology)PhysicsPorosityGeologyGeotechnical engineeringOptics

Abstract

fetched live from OpenAlex

A multiple point source model is developed in this research for studying both shear and compressional spherical wave propagation in a non-viscous fluid-saturated elastic porous medium. Relative displacement between the fluid and solid of the medium is quantified by the spherical wave governing equations, such that the waves described are more representative to that in engineering practices. The shear wave has shown significant influences on the characteristics of superposed shear and compressional waves generated by multiple point sources. Utilization of multiple point sources shows higher efficiency and effectiveness in generating desired waves, in comparing with that of single source. Specifically, the multiple sources model is more energy effective in comparing with the single source model by always producing larger magnitudes of relative displacement than a single source with the same energy, which becomes more significant when the distance between the source and the considered geological particles increases. Multiple point sources also show advantages on duration, direction control and magnitude adjustment for the waves generated. Numerical analyses are performed for comparing different shear, compressional and the superposed wave responses under single and multiple sources.

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)0.001
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 score0.553
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.012
GPT teacher head0.196
Teacher spread0.183 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Mechanical Engineers Part K Journal of Multi-body DynamicsSame topicSeismic Imaging and Inversion TechniquesFrench-language works237,207