Quantitative Evaluation of the Competing Effects of Wastewater Disposal and Hydraulic Fracturing on Causing Induced Earthquakes: A Case Study of an M3.1 Earthquake Sequence in Western Canada
Bibliographic record
Abstract
Abstract Previous studies mainly attribute injection‐induced earthquakes (IIE) to hydraulic fracturing (HF) operations in the Western Canada Sedimentary Basin (WCSB), whereas the role of wastewater disposal (WD) has been largely overlooked. One particular reason is that HF operations usually exhibit a clearer spatiotemporal relationship to IIE than WD. However, ignoring the effects from WD when investigating the seismogenisis of IIE in the WCSB may not be appropriate, especially when both types of injection activities are present in the vicinity of the epicentral area. Here, we conduct a case study on an M3.1 earthquake sequence located in the WCSB that can be spatiotemporally correlated with both active HF and WD operations. We first build an enhanced catalog consisting of 256 events to delineate the relationship between the occurrence of IIE and injection history. We then investigate the source parameters of the mainshock in detail. Finally, we build a numerical model to calculate the Coulomb stress change caused by each type of injections on the two nodal planes of the derived focal mechanism. The result suggests that the M3.1 event probably occurred on a near‐horizontal nodal plane. In addition, the pore pressure diffusion from WD and the poroelastic stress transfer from HF could work collaboratively to cause an IIE. Therefore, stress perturbation caused by long‐term WD should also be considered in the seismogenic process of an IIE, especially when both active HF and WD are in the immediate vicinity of the IIE.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".