Migration-induced noise reduction using fast discrete curvelet transform
Bibliographic record
Abstract
Summary Seismic data are unavoidably contaminated with noise. Theissue becomes worse for applications in mineralexplorations in hardrock environments where the complexnear- and sub-surface conditions result in variable signalamplitudes and attenuations (Eaton et al., 2003). Meanwhile, the quality of processing is usuallyuncontrollable, consequently, yielding remnant local noisyevents as singularities on stacked seismic images. Thesesingularities perform as backscattering sources during themigration process to create artificial features interferingwith interpretation. No post-migration processing techniquehas yet demonstrated effectiveness to reduce this artificialnoise. This problem can be solved by the curvelet transformfor the efficiency of representing directional features onseismic images. We investigate the elimination of themigration-induced noise using a 2D fast discrete curvelettransform (FDCT) threshold applied to a synthetic Stolttime-migrated impulse response. The result shows asignificant removal of migration-induced noise. Thisimprovement is also applied to a 3D post-stack time-migrated (PoSTM) seismic cube, resulting in anenhancement of the original signal and a suppression of theinduced noise.
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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".