Robust prestack <i>Q</i> -determination using surface seismic data: Part 1 — Method and synthetic examples
Bibliographic record
Abstract
ABSTRACT The accurate determination of seismic attenuation, or 1/Q, is useful for signal enhancement and reservoir characterization. To arrive at the necessary accuracy however, a number of issues must be addressed in the measurement technique. Specifically, spectral interference from closely spaced reflections is a major concern, in addition to the assumptions and errors associated with the raypath geometries of the reference and measured reflections. We have developed a robust method for measuring attenuation from prestack surface seismic gathers that helps minimize these issues. In our prestack Q-inversion technique; the presence of spectral interference was first reduced by making use of a variable-window time-frequency transform. To minimize the effects of the remaining interference, we then made use of an inversion scheme operating simultaneously in the frequency and traveltime-difference coordinates. A by-product of this inversion was a collection of the frequency-independent amplitude changes, which in the absence of geometric spreading, contains valuable amplitude variation with angle information, free from attenuation amplitude losses. Furthermore, under the assumption of locally 1D velocity and attenuation distributions, we made use of the τ-p transform to operate on traces of constant horizontal slowness. This allowed angle-dependent effects in the overburden such as attenuation anisotropy and source or receiver directivity to be eliminated. In the second part of our study, published separately, this technique was also demonstrated upon a shallow 3D seismic survey, and the measurements compared to another Q-estimation technique, as well as measurements from a vertical seismic profile.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".