Evaluation of burst liability in kimberlite using support vector machine

Due to the complex mechanisms of rockburst, there is no current effective method to reliably predict these events. A statistical learning method, support vector machine (SVM), is employed in this paper for kimberlite burst prediction. Four indicators $$\sigma_{\theta } ,\sigma_{c} ,\sigma_{t} ,W_{\text{ET}}$$σθ,σc,σt,WET are chosen as input indices for the SVM, which is trained using 108 groups of rockburst cases from around the world. Data uniformization is used to avoid negative impact of differing dimensions across the original data. Parameter optimization is embedded in the training process of the SVM to achieve optimized predictive ability. After training and optimization, the SVM reaches an accuracy of 95% in rock burst prediction for validation samples. The constructed SVM is then employed in kimberlite burst liability evaluation. The model indicated a moderate burst risk, which matches observed instances of rockburst at a diamond mine in north Canada. The SVM method ignores the focus on rockburst mechanisms, instead relying on representative indicators to develop a predictive model through self-learning. The prediction results show an excellent accuracy, which means this method has a potential application in rockburst prediction.