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Record W2077537302 · doi:10.3997/2214-4609.20140999

An Interferometric Solution for Non-stationary Shear-wave Statics

2014· article· en· W2077537302 on OpenAlexaff
Raul Cova, David C. Henley, K. A. Innanen

Bibliographic record

VenueProceedings · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStaticsSurface (topology)Mathematical analysisInterferometrySurface waveShear (geology)TRACE (psycholinguistics)MathematicsGeometryGeologyPhysicsOpticsClassical mechanics

Abstract

fetched live from OpenAlex

Summary Converted-wave data processing requires the computation of shear-wave statics for the receiver side. Conventionally this is done under the assumption of surface consistency. However, if the velocity change between the low velocity layer (LVL) and the medium underneath is smooth, or if the base of the LVL is structurally complex, the vertical raypath assumption that supports the surface consistent approach is no longer valid. This feature results in a non-stationary change of the statics that needs to be addressed in order to properly solve the problem. In this work the radial-trace (R-T) transform is used for moving the data to a raypath consistent framework where the statics change was showed to be approximately stationary. In this domain traveltime interferometry was applied to retrieve the delays caused by the near surface. Cross-correlation of the delayed traces with a model trace free of statics was showed to return a cross-correlation function that carries the statics information. These functions were convolved with the original traces to remove the delay caused by the near surface. Stacked sections computed using surface-and raypath-consistent solutions showed how the latter one effectively removed the statics by addressing the non-stationarity of the problem.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.290

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)0.0000.000

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 head0.022
GPT teacher head0.235
Teacher spread0.213 · 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
Published2014
Admission routes1
Has abstractyes

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