What controls the maximum magnitude of injection-induced earthquakes?
Bibliographic record
Abstract
Abstract Various approaches have been proposed to forecast the maximum expected earthquake magnitude that may be induced by fluid injection in a given area. Proposed forecast methods include a geometrical approach based on inferred dimensions of the stimulated volume; a formula that predicts maximum magnitude based on a putative linear relationship between maximum seismic moment and net injected volume; and a probabilistic approach based on seismic-activity rate. In this study, the probabilistic approach is extended to include a tapered Gutenberg-Richter distribution, which accounts for the effects of finite-fault dimensions. Each method makes specific assumptions that impact the applicability of the maximum-magnitude forecast, leading to divergent implications for monitoring and mitigation. Starting from basic concepts from earthquake seismology, we outline the theory and applications of these forecasting methods and test the maximum-magnitude forecasts using published examples of induced earthquakes. The majority of published examples are consistent with the putative volumetric limit, but a number of anomalous hydraulic-fracturing-induced events suggest that maximum magnitude is ultimately limited by geology (i.e., fault dimensions) rather than operational factors (e.g., net injected volume). Progress in understanding maximum magnitude may contribute to improved public communication and a stronger scientific foundation for traffic light criteria.
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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 | 0.015 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".