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Record W1977217134 · doi:10.1139/t04-005

Analysis of dispersion curves of Rayleigh waves in the frequencywavenumber domain

2004· article· en· W1977217134 on OpenAlexvenueno aff
Laiyu Lu, Bixing Zhang

Bibliographic record

VenueCanadian Geotechnical Journal · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
FundersInstitute of Acoustics, Chinese Academy of Sciences
KeywordsRayleigh waveWavenumberDispersion (optics)AcousticsWavelengthRayleigh scatteringSurface waveFrequency domainOpticsComputational physicsMathematicsMechanicsPhysicsMathematical analysis

Abstract

fetched live from OpenAlex

The method of spectrum analysis of surface waves (SASW) is discussed briefly. The analysis of the dispersion curves of Rayleigh waves in the frequency–wavenumber (f–k) domain is suggested due to the problems encountered in SASW. Three models of the layered media are considered according to typical situations in practice. All the modes that can be effectively excited are analyzed. The excitation and propagation characteristics of the Rayleigh waves are investigated by numerical simulation. The effects of some parameters such as number of channels and distance (s) between the source and the first receiver on the dispersion curves are investigated in detail for three models. Some important results about the number of receiver channels, relative error, mode jumping, and other aspects are obtained. It is found that reliable dispersion curves can be obtained by f–k analyses when the distance between the first receiver and the source is greater than one half the wavelength (0.5λ).Key words: dispersion curves, Rayleigh waves, SASW, frequency–wavenumber domain, mode jumping.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.208
Teacher spread0.199 · 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 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

Citations29
Published2004
Admission routes1
Has abstractyes

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