Generalized Nearest-Neighbor Distance and Hawkes Point Process Modeling Applied to Mining-Induced Seismicity
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Bibliographic record
Abstract
Abstract Modeling seismic activity rates and clustering plays an important role in studies of induced seismicity associated with mining and other resource extraction operations. This is critical for understanding the physical and statistical characteristics of seismicity and assessing the associated hazard. In this work, we introduce the generalization of the nearest-neighbor distance (NND) method by incorporating an arbitrary distribution function for the frequency–magnitude statistics of seismic events. Operating within a rescaled hyperspace that includes spatial, temporal, and magnitude domains, the NND method provides an effective framework for examining seismic clustering. By integrating a mixture of the two tapered Pareto distributions, the generalized NND approach accommodates deviations from standard frequency–magnitude scaling when studying the clustering properties of seismicity. In addition, the application of the temporal Hawkes process to model the mining seismicity rate reveals that the seismicity is primarily driven by external factors and lacks pronounced interevent triggering. A case study from a potash mine in Saskatchewan is presented to illustrate the application of the generalized NND method and the Hawkes process to estimate the clustering properties and occurrence rates of induced microseismicity. The implications of observed temporal variations and clustering behavior are discussed, providing insights into the nature of induced seismicity within mining environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it