Inferring rock strength and fault activation from high-resolution in situ Vp/Vs estimates surrounding induced earthquake clusters
Bibliographic record
Abstract
Fluid injection/extraction activity related to hydraulic fracturing can induce earthquakes. Common mechanisms attributed to induced earthquakes include elevated pore pressure, poroelastic stress change, and fault loading through aseismic slip. However, their relative influence is still an open question. Estimating subsurface rock properties, such as pore pressure distribution, crack density, and fracture geometry can help quantify the causal relationship between fluid-rock interaction and fault activation. Inferring rock properties by means of indirect measurement may be a viable strategy to help identify weak structures susceptible to failure in regions where increased seismicity correlates with industrial activity, such as the Western Canada Sedimentary Basin. Here we present in situ estimates of Vp/Vs for 34 induced earthquake clusters in the Kiskatinaw area in northeast British Columbia. We estimate significant changes of up to ±4.5% for nine clusters generally associated with areas of high injection volume. Predominantly small spatiotemporal Vp/Vs variations suggest pore pressure increase plays a secondary role in initiating earthquakes. In contrast, computational rock mechanical models that invoke a decreasing fracture aspect ratio and increasing fluid content in a fluid-saturated porous medium that are consistent with the treatment pressure history better explain the observations.
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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 | 0.001 |
| 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) | 0.001 | 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".