Prediction of Blast-Induced Ground Vibrations in an Open-Pit Mine Using Data Analytic Techniques
Bibliographic record
Abstract
Blasting in surface mines generates ground vibrations that can destabilize pit slopes, posing risks to neighboring communities and project viability. Predicting blast-induced ground vibrations (BIGV) before a blast design is implemented is critical for controlling their effects. The peak particle velocity (PPV) from BIGV is determined by blast design parameters and geological factors such as rock type and faults, which influence blast wave propagation. Blast frequencies below the rockmass’s natural frequency tend to amplify ground vibrations and affect slope stability. This study uses a comprehensive dataset of geological and blast design parameters of an open pit gold mine and data analytics techniques to predict blast-induced ground vibration. The dataset includes 819 blast events from four stationary seismographs. The coefficient of determination (R2) and mean squared error (MSE) were used to evaluate the performance of predictive models. Results indicate that the predictive models provide a robust method for predicting BIGV, considering geological influences and blast frequency effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".