Instantaneous attributes: the what and the how
Bibliographic record
Abstract
Since their introduction by Nigel Anstey and Tury Taner in the 1970s, attributes have become an integral tool in the interpreter’s arsenal. At present, as emphasised by Taner, no direct relationships have been established between all attributes and physical and geological characteristics of the subsurface. Their discriminatory properties, however, allow very useful classifications to be performed. This paper deals with various attribute-related issues.First, we consider the theoretical and physical aspects concerning instantaneous attributes, particularly instantaneous phase. This attribute is of central importance since it describes the location of events in the seismic trace and leads to the computation of other instantaneous quantities. Second, we deal with the issue of information content. It has often been implied that attributes convey no more information than that present in the original seismic trace from which they are derived. This, however, is akin to claiming that David contains no more information than the raw marble from which Michelangelo freed him. A seismic attribute section provides that much more information. The attribute in time attempts to enhance resolution, whereas the attribute property in the spatial dimension emphasises continuity. These important and interesting issues will be dealt with theoretically and by example. Finally, we present and illustrate by synthetic and real data examples, a novel, hybrid attribute which has been constructed to provide high resolution information. We must point out that, as is always the case, our attribute is dependent on the phase of the source wavelet.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.016 | 0.037 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".