Stochastic Configuration Networks for Laboratory Seismic Time-to-Failure Prediction

Seismic activity prediction is usually considered an infeasible and impossible task due to its intricate underlying mechanism. However, the rapid development of high-performance monitoring systems and artificial intelligence techniques for complex data modeling has reignited interest in exploring failure precursors and predicting seismic events. In this paper, we employ Stochastic Configuration Networks for problem-solving. To explore laboratory seismic activity deeply, we apply SCN to learn essential seismic information for the time-to-failure (i.e., time remaining before the occurrence of seism) prediction with acoustic emission signals. To alleviate overfitting during the learning process, a regularized SCN is used. Interestingly, we replace the standard Sigmoid activation function with the Hard Sigmoid, which accelerates convergence and improves performance further. The experimental results demonstrate that the improved method outperforms other machine learning methods regarding coefficient of determination and Mean Absolute Error.