A prediction model for microseismic signals based on kernel extreme learning machine optimized by Harris Hawks algorithm.

Real-time monitoring of rock stability and effective control of pressure concentration areas are crucial for ensuring the safety of personnel and equipment during mineral resource extraction. Microseismic and blasting signals, as early indicators of rock rupture, can effectively predict potential disasters. This study proposes a binary Harris hawks optimization algorithm with kernel search (bKSHHO) for the recognition of microseismic and blasting signal data. By integrating bKSHHO with a kernel extreme learning machine (KELM), we construct a prediction model, termed bKSHHO-KELM, which predicts microseismic and blasting signals, thereby enabling early warning of rock hazards. Experimental studies validate the optimization capability of the proposed KSHHO algorithm by comparing it with ten peer algorithms using the IEEE CEC 2022 benchmark functions. The bKSHHO-KELM method was then applied to predict microseismic and blasting signals, achieving an accuracy of 95.625%, a recall of 93.964%, a precision of 92.632%, and an F1 score of 0.931. This provides an efficient and accurate early warning solution for microseismic hazards in mine safety management.