A visual data-mining methodology for seismic facies analysis: Part 2 — Application to 3D seismic data
Bibliographic record
Abstract
Abstract A visual data-mining approach to unsupervised clustering analysis can be an effective tool for visualizing and understanding patterns inherent in seismic data (i.e., seismic facies). The unsupervised clustering analysis is completely data-driven, requiring no external information (e.g., well logs) to guide the seismic-trace classification. We demonstrate the application of the visual data-mining approach to seismic facies analysis on a real 3D seismic data volume. We select two stratigraphic intervals, the first including a Devonian pinnacle reef system and the second containing a Jurassic siliciclastic channel system. Both analyses show major stratigraphic features that can be defined in horizon slices or other types of visualization. However, the visual data-mining approach creates seismic facies maps with improved visual detail, distinguishing seismic trace-shape variability in the data. We also compare the facies maps with those obtained from a commercial package for seismic facies classification. Both approaches created similar facies maps, but the visual strategy better depicts subtle stratigraphic changes in the bodies being imaged, offering insight into the nature of these features.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".