Adaptive Gabor imaging using the lateral position error criterion
Bibliographic record
Abstract
We present a new seismic depth migration algorithm using the Gabor transform, also termed as the windowed Fourier transform, over the lateral spatial coordinates and the discrete Fourier transform over time. These transforms enable a wavefield depth extrapolation by laterally variable, frequency and wavenumber dependent, phase shift. The Gabor transform is implemented as a windowed discrete Fourier transform where the windows are confined to form a partition of unity (POU), meaning that they sum to one. For efficiency, an adaptive partitioning scheme that relates window width to the lateral velocity variation is developed, and defines an adaptive Gabor imaging scheme. Within each window, the Gabor method uses the familiar split‐step Fourier technique. The construction of the adaptive partition of unity is guided by an accuracy threshold that constrains the spatial positioning error, for each depth step. The spatial positioning error is estimated by comparing the Gabor method to a nonstationary phase shift that changes according to the local velocity at each position. We present the details of building the adaptive POU for both 2D and 3D imaging. The performance of Gabor depth imaging using this partitioning algorithm is illustrated with imaging results from prestack depth migration of the Marmousi dataset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".