Near-surface Q estimation: an approach using the up-going wave-field in vertical seismic profile data
Bibliographic record
Abstract
Summary Understanding the wavelet evolution with depth is the key to estimate Q values in the subsurface. Knowing these values is important to enhance the vertical resolution of seismic data, improve seismic-well ties and can also be used for reservoir characterization. The direct down-going wavefield recorded in VSP data is the conventional input for Q estimation. However, estimation for the shallow layers may be problematic. In this study, we show that the up-going wavefield is an alternative to have more reliable estimations especially in the near-surface layers. Combining both estimations from the down-going and up-going wavefield provides the optimum understanding of Q variation with depth. Q values are estimated from synthetic VSP down-going and up-going wavefields by using the dominant frequency matching method. We also estimated Q from field VSP data by using the spectral-ratio method as well as the dominant frequency matching method. From the up-going wavefield, we obtained that QP values range from 20-28 from 66-250m depth. For the deeper layers, using down-going wavefield, the estimated QP values range from 51-61 from 250-500m depth. In comparison, we used the direct down-going shear wavefield for QS estimation and found values ranging from 21-34 from 200-420m depth.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".