Analysis of Geological Susceptibility to Induced Seismicity in the Montney Formation Using Supervised Machine Learning
Bibliographic record
Abstract
Summary This project aims to determine the most important geological factors influencing the susceptibility to induced seismicity in the Montney Formation geological and geomechanical characteristics including pressure gradient, distance to the Cordilleran foreland thrust and fold belt and known lineaments, proximity to the Precambrian basement and Debolt formation, variation of maximum horizontal stress direction and depth factor were investigated. Supervised machine learning methods including four different Tree-based methods (Decision Tree, Bagging, Random Forest and Gradient Boosting) were used to calculate the feature importance. Geological susceptibility analysis was performed using Logistic Regression, commonly used for the probability estimation. The analysis of the Tree-based algorithms suggests three types of characteristics having the biggest impact on the geological susceptibility to induced seismicity in the Montney Formation: (1) variance of the SHmax direction from the regional trend, (2) vertical distance to Precambrian basement and (3) depth of the injection relative to the Montney top. Pore pressure gradient and distance to the Debolt Formation were interpreted as least influencing the geological susceptibility distribution. The highest discrepancy in geological susceptibility levels was observed in the northern part of the formation. Moreover, the Lower Montney was determined as most susceptible to induced seismic activity of all units.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".