Characterization of interbedded thin beds using zero-crossing-time amplitude stratal slices
Bibliographic record
Abstract
ABSTRACT We have developed a novel method to characterize thin beds using zero-crossing-time (ZCT) amplitude slices based on an interesting phenomenon we have noticed. Because of the reflection interferences and wavelet overlap, it is almost impossible to identify each thin bed from the composite response of interbedded thin beds. However, the seismic response of one thin bed does not always interfere with other thin beds at all times. At its ZCTs, a thin bed’s reflection makes no contribution to the composite response. Therefore, unlike standard amplitude stratal slices, the imprint of a thin bed does not appear in its ZCT amplitude stratal slices. The latter makes the interference pattern of ZCT amplitude stratal slices different from those of other amplitude stratal slices. Based on this difference, the ZCT amplitude stratal slices of each thin bed can be identified from consecutive amplitude slices, and the thickness, depth, and lateral distribution of each thin bed, as well as the vertical spacing between them, can be estimated from their ZCTs. This interpretive method has been systematically tested with synthetic 3D data and has also been applied to field seismic data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".