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Record W2044995651 · doi:10.1520/gtj102231

Influence of Local Site Conditions on the Reliability of Fundamental-Mode Surface Wave Inversion Methods

2009· article· en· W2044995651 on OpenAlexaff
Brent L. Rosenblad, Jianhua Li

Bibliographic record

VenueGeotechnical Testing Journal · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsSurface waveWavelengthWave velocityInversion (geology)Dispersion (optics)Mode (computer interface)GeologyShear (geology)OpticsPhysicsSeismology

Abstract

fetched live from OpenAlex

Abstract The development of reliable shear wave velocity (VS) profiles from surface wave velocity measurements requires compatibility between the measured and theoretical dispersion curves used in the inversion procedure. The most common approach in geotechnical applications is to use a fundamental-mode theoretical dispersion curve based on the assumption that the measured dispersion curve is representative of the fundamental mode of surface wave propagation. Analyses of low-frequency surface wave measurements presented in this paper show that this widespread assumption is invalid at low frequencies (long wavelengths) for some profile conditions. Results are presented from measurements performed at two deep soil sites in the central United States where surface wave analyses were performed using both the common fundamental-mode approach as well as an “effective-velocity” approach. At one site these two methods produced essentially the same estimate of the VS profile, while at the second site the estimated VS values at depth differed by more than 40 %. Using soil profile information for these sites and simulations of surface wave propagation, it is shown that the shallower depth to a higher-velocity formation at the second site created dominant higher-mode energy at wavelengths of 300–600 m, resulting in an overestimation of deep VS values.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.244

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 head0.037
GPT teacher head0.301
Teacher spread0.264 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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