SEISMIC MULTI-FRACTAL CHARACTERISTICS IN MINES AND SEISMICITY PREDICTION
Bibliographic record
Abstract
The characteristics of the fractal of seismicity in mines play an important role in the establishment of a nonlinear dynamics of seismicity in mines for prediction of seismicity.With seismic data detected in Dongguashan Copper mine,the fractal dimensions of spatial distributions of seismic hypocenters were calculated in the generalized correlation integral,and the fractal dimensions of energy of seismic events were calculated in the relation between b-values in the Gutenberg-Richter law.The fractal structures of seismicity in the mine and their temporal spectra were researched by means of multi-fractal theory,combining with the mining activities and mining engineering structures.The results show that seismicity in the mine has multi-fractal,and the heterogeneity of fractal structure of seismicity is of mining activity and engineering structure.Only if the fractal structure of seismicity in a space for fractal calculation is relative homogeneous,its fractal dimensions can be adopted to effectively predict seismicity.This space for the fractal calculation generally has few sorts of mining activities and simplex engineering structure,and the fractal dimensions of spatial distribution of seismic epicenters reflect the state of the induced seismicity.The temporal spectrum D∞ has an obvious precursor of main shock and is suitable for seismicity prediction,but the D2-D∞ has no obvious precursor of main shock and is not suitable for seismicity prediction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".