Multi-attribute processing techniques for the enhancement and interpretation of seismic data
Bibliographic record
Abstract
Summary form only given. Seismic data are collected, displayed, and interpreted in the time-distance domain (t-x). Local attributes of seismic data can be grouped into conventional single trace attributes (x=constant). The extraction of multitrace attributes is based on a computer-efficient implementation of localized slant stacking (beamforming) and median filtering. Image processing techniques are then applied to support the interpretation of migrated reflection seismic data whereby a seismic section is treated as a two-dimensional image. Local multitrace attributes have been used in a fast and robust coherency enhancement process for noisy seismic data. In a related application, multitrace attributes have provided the required independent data for successful multispectral image enhancement of seismic data. Multiattribute displays are well suited for the structural interpretation of migrated seismic data: this technique can be used for imaging of steeply dipping structures, analyzing uniformities and possible lithological boundaries, and highlighting focusing of diffracted energy and basin bounding faults.>
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.016 |
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".