Unsupervised contrastive learning for seismic facies characterization
Bibliographic record
Abstract
ABSTRACT Seismic facies characterization plays a key role in hydrocarbon exploration and development. The existing unsupervised methods are mostly waveform-based and involve multiple steps. We have developed a method to leverage unsupervised contrastive learning to automatically analyze seismic facies. To obtain a stable result, we use 3D seismic cubes instead of seismic traces or their variants as inputs of networks to improve lateral consistency. In addition, we treat seismic attributes as geologic constraints and feed them into the network along with the seismic cubes. These different seismic and multiattribute cubes from the same position are regarded as positive pairs and the cubes from a different position are treated as negative pairs. A contrastive learning framework is used to maximize the similarities of positive pairs and minimize the similarities of negative pairs. In this way, we can enforce the samples with similar features to get close while pushing the samples with different features to be separated in the space where we make the seismic facies clustering. This contrastive learning framework is a one-stage, end-to-end, and unsupervised fashion without any manual labels. We have determined the effectiveness of this method by using it to a turbidite channel system in the Canterbury Basin, offshore New Zealand. The obtained facies map is continuous, resulting in a stable and reliable classification.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".