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Record W2770924695 · doi:10.1190/sbgf2017-118

Attenuating multiples with the restricted domain hyperbolic Radon transform

2017· article· en· W2770924695 on OpenAlexaff
Juan I. Sabbione, Mauricio D. Sacchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMultipleRadonComputer scienceDomain (mathematical analysis)Radon transformAlgorithmArithmeticMathematicsPhysicsMathematical analysisArtificial intelligence

Abstract

fetched live from OpenAlex

We present a study that illustrates how to attenuate multiples using the restricted domain hyperbolic Radon transform (RHRT). The classical hyperbolic Radon transform (HRT) entails solving an inverse problem with a large number Radon coefficients. As opposed to HRT, in RHRT a small subset of the velocity panel is used to model the data. This subset is defined adaptively for each commonmidpoint gather (CMP) using a velocity panel bitmap, which is determined by the amplitudes of the adjoint Radon coefficients. This strategy helps to speed up computation of the transform and to enhance the focusing on the model domain. A well-studied marine data set from the Gulf of Mexico is processed to assess the performance of the method. The data is severely contaminated with multiple reflections and peglegs due to a shallow salt body intrusion. The proposed method successfully removes most of the multiples energy revealing greater details in the final section and improving the continuity of the deep reflectors and diffractions.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.771

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.028
GPT teacher head0.278
Teacher spread0.250 · 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 designOther design
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

Citations1
Published2017
Admission routes1
Has abstractyes

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