Seismic attenuation compensation with spectral-shaping regularization
Bibliographic record
Abstract
Because of the viscoelasticity of the subsurface medium, seismic waves will inherently attenuate during propagation, which lowers the resolution of the acquired seismic records. Inverse-Q filtering, as a typical approach to compensating for seismic attenuation, can efficiently recover high-resolution seismic data from attenuation. Whereas most efforts are focused on compensating for high-frequency energy and improving the stability of amplitude compensation by inverse-Q filtering, low-frequency leakage may occur as the high-frequency component is boosted. In this article, we propose a compensation scheme that promotes the preservation of low-frequency energy in the seismic data. We constructed an adaptive shaping operator based on spectral-shaping regularization by tailoring the frequency spectra of the seismic data. We then performed inverse-Q filtering in an inversion scheme. This data-driven shaping operator can regularize and balance the spectral-energy distribution for the compensated records and can maintain the low-frequency ratio by constraining the overcompensation for high-frequency energy. Synthetic tests and applications on prestack common-reflection-point gathers indicated that the proposed method can preserve the relative energy of low-frequency components while fulfilling stable high-frequency compensation.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".