Small Magnitude Events Highlight the Correlation Between Hydraulic Fracturing Injection Parameters, Geological Factors, and Earthquake Occurrence
Bibliographic record
Abstract
Abstract Hydraulic fracturing (HF) operations are widely associated with induced seismicity in the Western Canadian Sedimentary Basin. This study correlates injection parameters of 12,903 HF stages in the Kiskatinaw area in northeast British Columbia with an enhanced catalog containing 40,046 earthquakes using a supervised machine learning approach. It identifies relevant combinations of geological and operational parameters related to individual HF stages in efforts to decipher fault activation mechanisms. Our results suggest that stages targeting specific geological units (here, the Lower Montney formation) are more likely to induce an earthquake. Additional parameters positively correlated with earthquake likelihood include target formation thickness, injection volume, and completion date. Furthermore, the COVID‐19 lockdown may have reduced the potential cumulative effect of HF operations. Our results demonstrate the value of machine learning approaches for implementation as guidance tools that help facilitate safe development of unconventional energy technologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".