Time and offset domain internal multiple prediction with nonstationary parameters
Bibliographic record
Abstract
Practical internal multiple prediction and removal is a high priority area of seismic processing technology, that has special significance for unconventional plays, where data are complex and sophisticated quantitative interpretation methods are apt to be applied. When the medium is unknown and/or complex, and move-out based discrimination is not possible, inverse scattering based prediction is the method of choice, but challenges remain for its application in certain environments. For instance, when generators are distributed up-shallow and within and below zones of interest, optimum prediction parameters are difficult to determine — in fact in some cases no stationary value of the search parameter ε can optimally predict all multiples without introducing damaging artifacts. A re-formulation and implementation in the time-domain permits time-nonstationarity to be enforced in ε, after which a range of possible data-driven and geology-driven criteria for selecting a ε(t) schedule can be analyzed. 1D and 1.5D versions of the time-nonstationary algorithm are easily derived and can be shown to add a new element of precision to prediction. Merging of these ideas with multidimensional plane-wave domain versions of the algorithm will provide 2D/3D extensions. Presentation Date: Monday, October 17, 2016 Start Time: 4:35:00 PM 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".