Modelling seismicity based on fault geometry: maximum magnitudes and magnitude-frequency distributions.
Bibliographic record
Abstract
Investigating clusters of events and geophysical screening often provides limited constraints on fault geometries. This imaging issue prevents the integration of realistic fault zone geometry into earthquake studies, which can affect our capacity to evaluate the role of pre-existing faults on seismicity. This study describes a modelling strategy accounting for realistic fault zone geometries. Our approach uses stochastic methods underpinned by quantitative fault zone parameterization, followed by an assessment of seismicity from simulations of rupture dynamics controlled by fault geometry. This method is used to investigate the role of fault maturity on seismicity for two case studies, including seismicity associated with the reactivation of a pre-existing fault network due to hydraulic fracturing in Harrison County, Ohio, from 2013 to 2015, as well as the natural seismicity associated with the Yushu-Ganzi left-lateral strike-slip fault system in central-eastern Tibet. In the Harrison County case, we analyze the effect of vertical variability in fault maturity and show how more mature faults in the deeper crystalline basement generate higher magnitude seismicity than shallow, immature faults in younger sedimentary rocks. In the Yushu-Ganzi case study, we show how lateral variability in structural maturity, arising from long-term fault propagation and strain rates, leads to different seismicity on individual fault segments. Our findings indicate that fault geometry determines seismic patterns, with rupture length controlled by fault zone geometry rather than fault lengths, and favour the adoption of a structural geological perspective for the integration of realistic fault geometry into seismicity prediction strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".