Separating Hydraulic Fracturing Microseismicity From Induced Seismicity by Bayesian Inference of Non‐Linear Pressure Diffusivity
Bibliographic record
Abstract
Abstract Microseismic data acquired during hydraulic stimulations is routinely used to characterize hydraulic fracture (HF) propagation away from a wellbore. When the data include induced seismicity (IS) related to induced fault slip, separating HF‐related and IS‐related events is essential for HF treatment optimization and IS mitigation. Linear and non‐linear analytical diffusivity models can be used to interpret microseismic data and quantify the propagation of HFs, but their accuracy reduces when significant induced microseismicity is present. A Bayesian quantile regression is used to extend these existing diffusivity models to data contaminated with IS. A plausible ellipsoid filters events that are clearly anomalous prior to the quantile regression. The regression effectively separates HF‐related microseismic events from induced events in a case study for all stages without interpretative bias. This reveals faults that are directed connected to HFs, as well as those solely related with induced seismicity.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".