Seismic characterization with unsupervised machine learning applications for facies classification
Bibliographic record
Abstract
Abstract Machine learning (ML) techniques hold significant promise in identifying and delineating heterogeneous 3D seismic facies because they allow us to integrate the information content from multiple seismic attribute volumes. ML methods can be broadly categorized into four types: unsupervised learning, which uses the seismic attributes themselves as the training data and the data to be analyzed; supervised learning, which relies on labeled data — classified using well control, production data, or expert interpretation — for training; hybrid learning, which combines aspects of supervised and unsupervised approaches; and deep learning, which uses neural networks trained on large, labeled datasets to extract complex patterns from seismic data. However, great care must be taken not only in selecting but also in properly scaling the attributes to be used. Sometimes, these exercises are carried out mechanically, leading to compromised interpretations and discouraging results. In this study, we examined two well-established unsupervised ML techniques: principal component analysis and self-organizing mapping, applied to a seismic data volume from New Zealand. We found that these ML methods can enhance spatial resolution and, therefore, assist in seismic interpretation. However, it is crucial for the interpreter to ensure that the appropriate ML method is chosen, the assumptions underlying the applied techniques are met, and any available options, such as weighting of input attributes, are considered and used effectively.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".