Attenuating multiples with the restricted domain hyperbolic Radon transform
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".