Introduction to this special section—Seismic Attributes
Bibliographic record
Abstract
Seismic attributes are a great aid in both the qualitative and quantitative mapping of subsurface geologic features. Even before classical interpretation of picking horizons and generating maps begins, animating through appropriate seismic attribute volumes can quickly define the structural fabric and delineate major stratigraphic features. Seismic attributes have proven useful in almost every geologic environment, from clastics through carbonates to volcanic, and from normal faulting through wrench faulting to reverse faulting. Thus, seismic attributes facilitate recognition of depositional environments and enhance the recognition of seismic facies. Once calibrated at well locations, attributes can be used to identify seismic facies and provide information about both lithology and fluids, with or without the use of reference horizons. For this reason, seismic attributes form an integral part of most interpretation projects completed today. A complete book on seismic attributes, Seismic Attributes for Prospect Identification and Reservoir Characterization, has been published by SEG recently.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.093 | 0.068 |
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".