Random forest forecasting of time to failure for Granite hydraulic fracturing using acoustic emission signals
Bibliographic record
Abstract
Abstract Geothermal energy is a key resource to support carbon–neutral targets for its high energy baseload production. However, its utilization often involves hydraulic fracturing that can induce earthquakes. Accurate forecasting of the timing of these fractures and associated seismicity can inform hazard mitigation strategies where traditional methods often fall in short. Here, we utilize acoustic emission (AE) signals obtained from a series of hydraulic fracturing experiments on Barre Granite under a variety of stress regime to develop a random forest model to forecast the time to failure. The failure time is defined by a large pressure drop denoting the propagation of unstable macro-scale hydraulic fractures to the edges of the specimen. We achieve a coefficient of determination R 2 of 0.97 on test data using 50% of the data as the training set. We find that the first 20 statistical features constitute 100% of the contribution to the forecast, where the top 4 features are mean, minimum and kurtosis of the first finite difference and skewness of the signal voltage. At our given injection rate, our model can accurately forecast 1000 to 1800 s before failure compared to the 18 to 69 s in advance when using a benchmark inverse AE rate model. Our results suggest that it is possible to forecast failure of a rock specimen prior to the onset of accelerated seismic release, with implications for managing induced seismicity hazards.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".