Least-squares inversion of in-situ sonic <i>Q</i> measurements: Stability and resolution
Bibliographic record
Abstract
Abstract We minimize the effect of noise and increase both the reliability and the resolution of attenuation estimates obtained from multireceiver full-waveform sonics. Multiple measurements of effective attenuation were generated from full-waveform sonic data recorded by an eight-receiver sonic tool in a gas-bearing sandstone reservoir using two independent techniques: the logarithmic spectral ratio (LSR) and the instantaneous frequency (IF) method. After rejecting unstable estimates [receiver separation < 2 ft (0.61 m)], least-squares inversion was used to combine the multiple estimates into high-resolution attenuation logs. The procedure was applied to raw attenuation data obtained with both the LSR and IF methods, and the resulting logs showed that the attenuation estimates obtained for the maximum receiver separation of 3.5 ft (1.07 m) provide a smoothed approximation of the high-resolution measurements. The approximation is better for the IF method, with the normalized crosscorrelation factor between the low- and high-resolution logs being 0.90 for the IF method and 0.88 for the LSR method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".