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Record W7100818631

Fresnel zones and the power of stacking used in the preparation of data for AVO analysis John C. Bancroft, and Shuang Sun, CREWES/University of Calgary Summary

2015· article· en· W7100818631 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndustrial Engineering and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPrestackFresnel zoneOffset (computer science)Reflection (computer programming)Aperture (computer memory)DiffractionAmplitudeAttenuation
DOInot available

Abstract

fetched live from OpenAlex

The concept of a generalized prestack Fresnel zone is presented to aid in defining a prestack migration aperture for AVO analysis. A large migration aperture requires prestack amplitude scaling that may introduce anomalous results in areas where the acquisition geometry varies. Limiting the prestack migration aperture to the area of specula reflection energy allows the data to be summed into a prestack migration gather and then divided by the fold to balance the amplitudes. The size of the offset Fresnel zone is based on the zero-offset case and represents that portion of the migration operator that sums across a half wavelength of the reflector. In the offset case, that diffraction shape is defined by the double-square-root equation. In the prestack volume (x, h, t) the Fresnel zone can be illustrated by the intersection between the hyperbolic plane of the reflection energy, and the surface defined by the double-square-root equation. Examples show the results of limited aperture gathering on modelled and real data for horizontal events.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.251
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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