Channel detection in 3-D seismic data using sweetness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.000 |
| 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 it