Using well logs to estimate the effect of fine layering on Q estimation
Bibliographic record
Abstract
Summary The long-standing prediction that a seismic wave propagating in a finely layered earth model displays an apparent attenuation is investigated. Called stratigraphic filtering, this effect looks much like constant-Q attenuation and adds to intrinsic attenuation to produce effective attenuation. Using a 1D synthetic seismogram algorithm, the seismic response is calculated for a sequence of finely layered models derived from well logs with assigned Q values. The models all have a finely-layered Q structure, representing intrinsic attenuation, derived from measured density and sonic logs by an empirical relation. The model properties are all sampled at 0.5 m intervals and averaged into constant thickness layers. Using 0.5 m layers, when the Q value is carefully measured using the spectral-ratio technique, the measured Q is always lower than that expected from the specified model. In a series of experiments in which various physical effects are turned off and on again, it is demonstrated conclusively that this Q bias is due to internal multiples. Using a series of models derived from the same logs but with progressively thicker layers (each model has constant thickness layers and each is sampled a 0.5m) it is demonstrated that there is significant measurement bias for layer thicknesses less than 10m but for thicknesses greater than this the bias disappears. The feasibility of estimating stratigraphic Q from such experiments and using these measurements to correct measurements from field data is suggested.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".