Inverse-scattering series internal-multiple prediction in the double plane-wave domain
Bibliographic record
Abstract
The inverse scattering series (ISS) multiple prediction and attenuation algorithm, developed by Weglein and collaborators in the 1990s, estimates multiples from subevents which satisfy the lower-higher-lower criterion in the pseudo-depth or vertical traveltime domain. Prediction results that (1) have a high degree of numerical accuracy, (2) have a relatively stationary optimum search parameter, and (3) represent a significantly reduced computational burden, can be achieved in the plane wave domain. ISS prediction carried out in this domain may therefore be well suited as part of an effort to demultiple, effectively in, e.g., complex land environments, where such features become increasingly important. In this paper, we discuss and examine 2D internal multiple predictions calculated by an implementation of the ISS algorithm in the double plane wave domain. This requires a properly formulated prediction algorithm and data transformed to the coupled τ-ps-pg domain as input. A synthetic 2D case is examined to highlight some of the numerical features of this implementation. Presentation Date: Tuesday, October 18, 2016 Start Time: 11:10:00 AM Location: 142 Presentation Type: ORAL
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".