A Study on Reconstruction of De‐Aliased Uneven Seismic Data
Bibliographic record
Abstract
Abstract Seismic data spatial trace interpolation is one of the most important issues in seismic data processing. In this paper, a novel Fourier transform based algorithm is proposed, which can reconstruct both uneven and aliased seismic data. We formulate band‐limited data reconstruction as a minimum norm least squares inverse problem where an adaptive DFT‐weighted norm regularization term is used. The inverse problem is solved by the preconditioned conjugate gradient algorithm, which makes the solutions stable and convergence quick. Based on the assumption that local seismic data are consisted of finite linear events, from the sampling theorem, aliased events can be attenuated via LS weighting prediction linearly from low frequency. Three application cases are discussed on even gap trace interpolation, uneven gap filling and high frequency trace reconstruction from low frequency data traces constrained by a few high frequency traces. Both synthetic and real data numerical examples show that the proposed method is valid, efficient and applicable. This research is valuable to seismic data regularization and cross well seismic exploration.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".