Resolution on multiples: Interpreters' perceptions, decision making, and multiple attenuation
Bibliographic record
Abstract
We investigated our ability to remove a specific short-period multiple from the Nisku and Blueridge formations in West Central Alberta, Canada. This problem is commercial in nature, and has persisted because it was believed that the multiple had too little moveout to be removed, rendering interpretation of the thin Blueridge zone impossible. Associated with this issue was the belief that the modern high-resolution Radon transforms do not materially affect the stack response of real data in this area despite their excellent performance on synthetics and on other data in the literature. Serious technical work seldom affords a discussion of “beliefs”, but this work is concerned with the decision-making of the interpreter. We show that in order to address a specific, real, short-period multiple problem, the interpreter was required to challenge previously held technical assumptions. This required the interpreter to consider the nature of the multiple itself, the nature and limitations of the multiple suppression technology used, and to objectively measure the level of success in suppressing the multiple.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".