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Global Inversion of Surface Wave Dispersion Curves Based on Improved Weighted Average Velocity Method

2013· article· en· W2036364784 on OpenAlexfundno aff
Eng‐Choon Leong, A.M.W. Aung

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
FundersDefence Science and Technology Agency - SingaporePetroleum Technology Research Centre
KeywordsSurface waveGeologyGeotechnical engineeringWave velocityInversion (geology)Dispersion (optics)SeismologyOpticsPhysicsShear (geology)

Abstract

fetched live from OpenAlex

In this paper, a global inversion algorithm for surface wave dispersion curve is proposed. An improved weighted average velocity (Wave) method is used as the forward model and generalized reduced gradient (GRG) method is used for minimization of misfit function. The development of the proposed algorithm is detailed. The proposed inversion algorithm is then used to invert synthetic Rayleigh wave dispersion curves of known one-dimensional (1D) soil profiles and results show that the initial soil profile population size of 10,000 is sufficient to obtain the global minimum solution. Finally, field Rayleigh wave dispersion curves are inverted to show that the 1D shear wave velocity (Vs) profiles from the proposed inversion algorithm agree well with independently measured Vs data.

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 score0.110
Threshold uncertainty score0.610

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)0.0010.000

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 head0.005
GPT teacher head0.176
Teacher spread0.171 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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