Parabolic fitting method for quality factor estimation
Bibliographic record
Abstract
ABSTRACT The quality factor Q has a wide application range in seismic data processing and interpretation. Compared with the commonly used methods of Q estimation (e.g., the spectral ratio method and the centroid frequency shift method), the logarithmic spectral area difference (LSAD) method exhibits better noise immunity. However, the LSAD method uses the area difference in one frequency band, and the Q values estimated using this method are still unstable under the influence of noise. We have developed a new parabolic fitting (PF) method to estimate Q. This method is an extension of the LSAD method. We derive an analytical relationship between Q and area difference curve of the logarithmic amplitude spectra. Using the logarithmic area difference of multiple frequency bands to fit the parabola, we improve the accuracy and stability of Q estimation. In addition, the PF method does not require special assumptions regarding the source wavelet. We test the PF method in the presence of noise and at varying bandwidths and compare the results with those obtained using the LSAD method. The results of the theoretical examples indicate that the PF method is noise resistant and stable. Applying the PF method to real zero-offset vertical seismic profiling records also indicates that the proposed method can reasonably and stably estimate the Q value of the formation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".