Bibliographic record
Abstract
Abstract Sweetness is a seismic attribute that, especially when used in conjunction with coherency, can be very useful for channel detection in deep-water clastic and coastal-plain settings. Although the attribute is not new, previous documentation of its utility and derivation has been mostly lacking. In this article, I present images of channels from three-dimensional seismic volumes that were derived using sweetness, and discuss the physical basis of the attribute. Furthermore, the modeling presented here suggests that sweetness could be used in a semiquantitative way to predict net-to-gross ratio in channel systems. Sweetness is derived by dividing reflection strength by the square root of instantaneous frequency. This mathematical definition captures attribute relationships that seismic interpreters have been using qualitatively for many years: isolated sand bodies in shale successions tend to generate stronger, broader reflections than the surrounding shale. Sweetness becomes less useful for channel detection when acoustic impedance contrasts between sands and shales are low or when sands and shales are highly interbedded.
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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 | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".