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Record W2139182202 · doi:10.1190/1.1512799

Estimation of quality factors from CMP records

2002· article· en· W2139182202 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGeophysics · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of British ColumbiaBP (Canada)
FundersUniversity of British Columbia
KeywordsGeologyAmplitudeAnelastic attenuation factorOffset (computer science)Vertical seismic profileSeismologyWaveletGeodesyQuality (philosophy)Factor (programming language)Seismic waveComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract Estimates of the quality, Q, factor are commonly obtained from vertical seismic data or stacked surface seismic data. This paper describes a method that allows Q-factor to be estimated directly from common midpoint (CMP) gathers. Absorption of the wavefield is dependent on three parameters: frequency, traveltime in the medium, and medium Q-factor. Assuming that the amplitude spectrum of the seismic source signature may be modeled by that of a Ricker wavelet, we derive an analytical relation between Q-factor and seismic data peak frequency variation both along offset and vertical time direction. The Q-factor is estimated from CMP gathers using a layer-stripping approach.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.999

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)0.0020.000

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 head0.034
GPT teacher head0.237
Teacher spread0.203 · 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