Bibliographic record
Abstract
Knowledge of Q is desirable for improving seismic resolution through inverse Q filtering, facilitating amplitude analysis and seismic interpretation. However, the question of reliable Q estimation remains, especially in case of unfavorable signal-to-noise ratio (SNR). In addition, estimating Q from VSP data or even reflection data in presence of moderate noise with sufficient accuracy is still very challenging. To address this problem, a match-filter method for Q estimation is proposed and evaluated using synthetic 1D, 2D data and field data in this paper. Given two narrow time windows as might be used in the spectral-ratio method, we compute minimum phase equivalent wavelets for each window and then, by direct search over a broad Q range, we find the optimal forward Q filter that best matches the shallow wavelet to the deeper one. Testing results show that the proposed method is, compared to the spectral-ratio method, more robust to noise and more suitable for the Q estimation from reflection data, and has the potential to indentify a localized low Q zone of the subsurface, which can be used as a gas indicator.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".