Multicomponent inverse scattering series internal multiple prediction in the τ-p domain
Bibliographic record
Abstract
Internal multiples constitute unique signal in seismic records, which can negatively impact subsurface imaging and subsequent amplitude analysis, or, potentially, enhance illumination due to their distinct reflection angles and longer ray-paths. Both conventional migration using primaries only, and improved imaging approaches involving internal multiples, benefit from the precise identification and separation of internal multiples from primaries. Elastic versions of the inverse scattering series internal multiple attenuation algorithm extend prediction capacity to include wave-mode conversion and other elastic effects. In spite of having been published in the 1990s, however, little to no numerical analysis of multicomponent versions of the algorithm has been presented, possibly because of the difficulty of finding integral limits which completely suppress artifacts. We present a plane-wave (τ-p) formulation, which admits, possibly uniquely, a sufficiently aggressive limitation on integration limits to create artifact-free P-P and P-S predictions. The process is illustrated with 1.5D synthetic data. Presentation Date: Tuesday, September 26, 2017 Start Time: 10:35 AM Location: 370A 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".