The significance of incorporating a 3-D point source in the inverse scattering series internal multiple attenuator for a 1-D subsurface
Bibliographic record
Abstract
Summary In this paper, the 3-D inverse scattering series (ISS) internal multiple attenuation algorithm (Araújo et al., 1994; Weglein et al., 1997, 2003) is modified for a one-dimensional subsur-face to incorporate a 3-D point source in multiple predictions, for improved realism and effectiveness. The new algorithm, which assumes the earth is only varying in the z-direction (1-D subsurface/earth, reasonable in many circumstances in Central North sea (Duquet et al., 2013), on-shore Canada, and the Middle East), represents more than a small increase in effectiveness of predicting the shape and amplitude of multiples, compared to a frequently employed 1.5-D ISS internal multiple attenuator (assuming a 2-D line source and a 1-D earth). The numerical tests are performed on a 3-D source synthetic data set from a 1-D subsurface. The results demonstrate that the new algorithm incorporating a 3-D point source can change the prediction from 'causing harm' to 'providing benefit' in comparison to an internal multiple attenuation algorithm that assumes a 1-D earth and a line source.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".