Generalizable deep learning models for predicting laboratory earthquakes
Bibliographic record
Abstract
Machine learning models can predict laboratory earthquakes using Acoustic emission, the lab equivalent of microseismicity, and changes in fault zone elastic properties during the lab seismic cycle. Applying them to natural earthquakes requires testing their generalizability across lab settings and stress conditions. Here, we show a fine-tuned convolutional neural network (CNN) model effectively transfer across different conditions. Our model employs techniques from natural language processing, including decoder techniques, to capture the relationship between AE and fault stress. We fine-tune the regression head of a deep CNN while fixing the decoder’s weights and successfully predict lab seismic events for a range of conditions. With fine-tuning, CNN models trained on one lab fault configuration predict time to failure and shear stress for another configuration at varying fault slip rates. These results demonstrate the potential of extending lab-based methods to different conditions that could eventually include tectonic earthquakes and seismic forecasting. A deep convolutional neural network model enables the time to failure and shear stress to be predicted across different types of laboratory earthquakes under a range of materials and conditions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".