High-frequency recovery of surface seismic data from autoregressive spectral extrapolation and <i>Q</i>-compensation
Bibliographic record
Abstract
Autoregressive spectral extrapolation and Q-compensation allow for the recovery of the high-frequency component of the wavelet spectrum that has been attenuated by the Earth filtering effect. Autoregressive spectral extrapolation utilizes a predictive function while Q-processing uses a backward propagation process to recovery high-frequency. Despite the fact that the two methods are distinctive they share a common goal that turns out be complementary. Here both methods are combined into a single algorithm. First, the higher end (~30-75 Hz) of the frequency spectrum that generally has a lower signal-to-noise ratio is predicted from the lower part (~10-35 Hz) of the spectrum having a higher signal-to-noise ratio using an autoregressive spectral extrapolation. Then, seismic traces are Q-compensated using attenuation values that are estimated from seismic data. The combination of both methods improves the temporal resolution. The increase of seismic energy in the high-frequency band can be up to 17% and on average 10% when compared to data that are solely Q-compensated allowing the resolution of more subtle seismic stratigraphic features.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".