Characterizing intrinsic and stratigraphic Q in VSP data with information measures
Bibliographic record
Abstract
The problem of distinguishing between intrinsic Q and stratigraphic filtering is a classical example of non-uniqueness in seismic data analysis. Two very different mechanisms affecting propagating waves – reverberations between thin layers and transformation of mechanical energy to heat – produce almost identical effects. A version of the Shannon entropy, defined on snapshots of a VSP wave field, has been proposed to analyze and compare these two influences. It has been used to argue that the distinction is not as meaningful as we might think, but here we take the opposite view: that with it we may separate the two effects. Besides the basic entropy measure, we further extend the entropy “attribute” to include conditional entropy, which incorporates correlations of seismic data points with their neighbouring values. Synthetic VSP data built from a range of well logs are used to investigate the separability of intrinsic/extrinsic sources of attenuation and dispersion. Progress of this kind may have impact on aspects of reservoir characterization such as viscosity estimation. Presentation Date: Tuesday, September 26, 2017 Start Time: 11:00 AM Location: 371A Presentation Type: ORAL
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".