Sub-daily Probabilistic Forecasting of High-Energy Seismic Events in Underground Mining Using Deep Temporal Classifiers: A Case Study at West African Gold Mine

This study applies deep temporal classifiers to probabilistic seismic hazard assessment at a West African underground hard-rock gold mine. The analysis evaluates five forecasting horizons (1, 3, 6, 12, and 24 hours) across six model architectures, that is Gated Recurrent Unit (GRU), Transformer, Logistic Regression (LR), Gradient Boosted Machines (GBM), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN). Training of the models is performed based on weighted binary cross-entropy and evaluation is done using ROC-AUC, PR-AUC, Brier score, Expected Calibration Error (ECE), partial area under the curve (pAUC) for low false positive rate, Matthews Correlation Coefficient (MCC) and balanced class accuracy. Confidence intervals are computed using the Moving Block Bootstrap (MBB; block size b = 51; 2,000 iterations) procedure which accounts for the autocorrelation of seismic events in time. The GRU obtains a maximum ROC-AUC score of 0.727 after 12 hours whereas the Transformer network obtains 0.941 at 24 hours. In the operationally important 3-hour horizon, both deep learning architectures provide ROC-AUC scores close to 0.64 and PR-AUC scores close to 0.74, outperforming all the shallow baselines. Integrated Gradients and GradientSHAP attributions at 3 hours show that the inter-event time (Δt) is the key predictive feature in both the models, corroborating the Omori-type short memory clustering hypothesis. Threshold sensitivity analysis at the 90th, 95th and 97.5th percentile of high energy events shows that the 95th percentile threshold gives the best stability of the discrimination task.