Enhancing seismic resolution by multiattribute analysis: A case study featuring the resolution of density
Bibliographic record
Abstract
Pre-stack inversion is used to generate p-impedance, s-impedance, density and VP / VS volumes as well as other volumes. However, a density volume that is produced from a pre-stack inversion project is often not reliable because the range of incident angles within the input CDP gathers does not reach sufficiently far above 35 degrees where density has an identifiable effect on the reflections. Lithologic density is frequently a critical property in characterizing hydrocarbon reservoirs. Since density logs are available at many well locations, they can be used to correlate with a range of seismic attributes at each well location on a sample-by-sample basis. This correlation can be used to “predict” density at other well locations and to generate a 3D volume of density for a given prospect. The seismic resolution derived from this process can be enhanced further by using neural network-type processes. Presentation Date: Monday, October 17, 2016 Start Time: 3:45:00 PM Location: Lobby D/C Presentation Type: POSTER
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".