Gabor deconvolution of seismic data for source waveform and Q correction
Bibliographic record
Abstract
We present a novel approach to nonstationary seismic deconvolution using the Gabor transform. This nonstationary transform represents a signal as a superposition of sinusoids that are localized by time-shifted windows. The resulting time-frequency decomposition is a suite of local Fourier transforms that facilitates nonstationary spectral analysis or filtering. In a result that generalizes the seismic convolutional model, we show that the Gabor transform of a nonstationary seismic signal is the product of source signature, Q filter, and reflectivity effects. We use this spectral factorization theorem as a basis for a new deconvolution algorithm in the Gabor domain. We estimate the Gabor spectrum of the underlying reflectivity directly from the Gabor spectrum of an attenuated seismic signal. Tests on synthetic and real data show that our method works well and combines the effects of sourcesignature inversion and a data-driven inverse Q filter. In comparison with a stationary Wiener deconvolution, our Gabor deconvolution is similar within the Wiener design gate and superior elsewhere.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".