Bibliographic record
Abstract
ABSTRACT The seismic quality factors Q used in many applications of exploration seismology are not automatically equivalent. We identified three groups of usage of the concept of a Q: (1) a measure of internal mechanical friction within rocks, as implied in petrophysical interpretations, (2) several types of apparent Q arising from attenuation measurements, and (3) axiomatic Q defined in the viscoelastic theory. These groups differ by their roles in the interpretation, sensitivity to model assumptions, frequency dependences, and particularly by the temporal and spatial resolution. Among all types of Q, those that are most robust and useful for characterizing the material are also strongly limited in resolution and accuracy. For example, from spectral coherency studies, it is known that to measure a Q of approximately 100 with modest accuracy of 30%, measurement time intervals of about 500 ms are required. Although several inversion techniques offer models of Q at much higher resolution, such detailed Q models are usually dominated by the effects of localized structures, such as “colored” transmission across boundaries, reflectivity, or scattering. Such types of Q can be called “structural,” and they differ from the Q-factor of the medium. Detailed Q images are also sensitive to theoretical models such as background geometric spreading and assumptions about the frequency dependence of the Q. Direct association of such Qs with material properties may be inaccurate and unreliable. Measurement of geometric spreading and averaging of the structural Q produce estimates of “geometric” and scattering attenuation; however, these estimates are also strongly limited in accuracy and resolution. The viscoelastic Q (group 3 above) heavily relies on a specific mathematical model. Despite producing detailed images, the spatial resolution of viscoelastic Q is inherently limited by the nature of its relation to the frequency-dependent velocity. This resolution limit is difficult to assess quantitatively.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".