Self-supervised learning for efficient seismic facies classification
Bibliographic record
Abstract
ABSTRACT Seismic facies classification is an important task in seismic interpretation that allows the identification of rock bodies with similar physical characteristics. Manual labeling of seismic data is immensely time consuming, given the recent surge in data volumes. Self-supervised learning (SSL) enables models to learn powerful representations from unlabeled data, thereby improving performance in downstream tasks using limited labeled data. We investigate the effectiveness of SSL for efficient facies classification by evaluating various convolutional and vision transformer-based models. We pretrain the models on image reconstruction and fine-tune them on facies segmentation. Results on the southern North Sea F3 seismic block in the Netherlands and the Penobscot seismic volume in the Sable Subbasin, offshore Nova Scotia, Canada, show that SSL has comparable performance to supervised learning using only 5%–10% labeled data. Further, SSL exhibits stable domain adaptation on the Penobscot data set even with 5% labeled data, indicating an improved generalization compared with the supervised learning setup. The findings demonstrate that SSL significantly enhances model accuracy and data efficiency for seismic facies classification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".