Robust prestack <i>Q</i> -determination using surface seismic data: Part 2 — 3D case study
Bibliographic record
Abstract
ABSTRACT In part one of this paper, we introduced a novel method of measuring seismic attenuation from prestack surface seismic data. This prestack Q-inversion (PSQI) employed methods to reduce the presence of spectral interference and then minimize the influence of interference that remains. By changing the domain of operation, angle-dependent effects in the overburden were eliminated when a locally 1D medium was assumed. To demonstrate the suitability of the PSQI method, we applied it to a 3D seismic survey from Western Canada. Preprocessing of the data was carried out to reduce noise, regularize amplitudes, and transform the seismic gathers into the τ-p domain prior to attenuation measurements. In addition to the seismic gathers, we also incorporated velocity data and zero-offset traveltime information. The result was that we were able to generate a map of 1/Q values which showed coherent patterns that could be explained by the presence of top gas in the reservoir. These measurements were correlated to independent amplitude attributes. We performed a number of other analyses to establish the appropriateness of the attenuation measurements. We compared 1/Q measurements with the predecessor QVO method, and showed that the PSQI measurements were less influenced by artifacts and were more robust to changes in the bandwidth of analysis. We also analyzed a VSP survey in the same geological setting and showed that the resulting measurement (1/Q = 0.0271) is very close to the mean PSQI value for the 3D survey (1/Q = 0.0312). This case study thus showed how the described PSQI method overcame many of the difficulties associated with obtaining accurate attenuation measurements from surface seismic data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".