Quantifying the Impact of Fault Networks on Induced Seismicity Potential - Geomechanical Tools to Support Social License
Bibliographic record
Abstract
Summary A workflow is demonstrated which uses numerical modeling to mechanically quantify the induced seismic potential of a given area as a function of differential stress and shear components. With properly constrained and conditioned geologic and geophysical inputs, a representative fracture model may be simulated to deliver geomechanical outputs which correlate well with seismicity where such information is available. Near Oklahoma City, fault data from recent Oklahoma Geologic Survey publications is simulated using realistic regional stress and rock properties which produce a geomechanical proxy map that clearly outlines regions of high and low stress/shear which demonstrate similar trends to seismicity. This same workflow is applied to the largescale regional faults collected into GIS data by the Alberta Geological Survey. When simulated using realistic regional conditions, certain areas and geometries of fault blocks result in areas of escalated stress/shear even away from the large faults bounding the blocks. The ability of the workflow to realistically capture the mechanical effect of faults at a regional scales, and simulate the interaction of several faults within an evolving stress environment, provides a quantitative measure of the mechanical potential for high pressure injection wells to trigger a large seismic event in a given area.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".