Numerical modelling of spatially and temporally distributed on-fault induced seismicity: implication for seismic hazards
Bibliographic record
Abstract
Abstract Induced seismicity is strongly related to various engineering projects that cause anthropogenic in-situ stress change at a great depth. Hence, there is a need to estimate and mitigate the associated risks. In the past, various simulation methods have been developed and applied to induced seismicity analysis, but there is still a fundamental difference between simulation results and field observations in terms of the spatial distribution of seismic events and its frequency. The present study aims to develop a method to simulate spatially distributed on-fault seismicity whilst reproducing a complex stress state in the fault zone. Hence, an equivalent continuum model is constructed, based on a discrete fracture network within a fault damage zone, by employing the crack tensor theory. A fault core is simulated at the center of the model as a discontinuous plane. Using the model, a heterogeneous stress state with stress anomalies in the fault zone is first simulated by applying tractions on the model outer boundaries. Subsequently, the effective normal stress on the fault plane is decreased in a stepwise manner to induce slip. The simulation result is validated in terms of theb-value and other seismic source parameters, hence demonstrating that the model can reproduce spatially and temporally distributed on-fault seismicity. Further analysis on the parameters shows the variation of frequency-magnitude distribution before the occurrence of large seismic events. This variation is found to be consistent with field observations, thus suggesting the potential use of this simulation method in evaluating the risk for seismic hazards in various engineering projects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".