Refocusing migrated seismic images in absorptive media
Bibliographic record
Abstract
Abstract In seismic exploration, received seismic signals usually experience absorption during their propagation. However, seismic migration algorithms seldom take into account seismic absorption in their implementations. We have investigated the blurring effect in migrated images that occurs when using a regular migration algorithm to migrate those seismic data with absorption. The blurring functions can be calculated using a numerical method; and for layered media, a fast algorithm exists for updating the blurring function from one time step to another. The deblurring process is formulated as a problem of multidimensional nonstationary deconvolution. We use a least-squares inverse scheme to remove the absorption blurring effect and in turn refocus migrated images. The refocusing algorithm is stable, and convergence is achieved with a few iterations at each wavenumber. Experiments on synthetic and real data show that our refocusing technique is valid when compensating for seismic absorption after migration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".