On the calibration of a fast S‐transform with application to AVF inversion of anelastic reflectivity
Bibliographic record
Abstract
The S- transform is a time- frequency decomposition technique with a wide range of applications in seismic signal analysis. However it has been under- used due to the high computational cost needed to employ it. In this paper we present a fast, non-redundant S- transform (FST). The ability of the FST to provide high fidelity estimates of reflection seismic amplitudes is tested in this paper. Where the FST cannot provide accurate estimates of amplitudes, we calibrate it by normalizing the unit impulse response of the algorithm. Strongly dispersive reflection coefficients associated with highly absorptive, hydrocarbon charged targets, have been observed in seismic data. A frequency by frequency method (AVF) for determining Q of a highly absorptive target from measurements of the dispersive reflection coefficient has been developed to invert data variations of this kind. In order to implement the AVF technique to invert for Q, it is necessary that we have a method of estimating the local spectrum of the reflection coefficient. We develop a method of implementing AVF inversion by using the calibrated FST to estimate the local spectrum of dispersive reflection coefficients. We test the effectiveness of the FST for estimating the spectrum of reflection coefficients by comparing with the analytic reflection coefficient as calculated by a fast Fourier transform. Using forward modeling to generate synthetic traces containing absorptive reflection coefficients, we observe accurate results of the AVF inversion for a range of Q values.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".