Precursory waves and eigenfrequencies identified from acoustic emission data based on Singular Spectrum Analysis and laboratory rock-burst experiments

Important task for acoustic emission (AE) monitoring involves detecting frequency shift phenomenon and intense periodic components. In the present research, we investigate time dynamics embedded in AE signal acquired in the laboratory rock burst experiment on limestone sample. By applying the Singular Spectrum Analysis (SSA)-based algorithm developed in this research, we reconstruct the decomposed components and then select the main component with a decision-making process based on the criterion that it should be significant both in the eigenvector space and spectral domain, termed eigenfrequency. The frequency shift phenomenon is represented by the eigenfrequencies of the first main component consistently. Precursory waves of the first main component represents time dynamics of the rock burst process by elastic wave over the low-level loading phase, high-frequency wave with self-oscillating envelopes at unloading, low-frequency quasi-shock waves during the rheological delay phase and low-frequency shock wave at complete rock burst failure. •SSA-based algorithm was developed for analyzing AE data from rock burst test.•Main part of AE data were reconstructed with the decomposed components.•Selected main components are significant in eigenvector and spectral spaces.•Main eigenfrequecies well represent frequency shift in rock burst process.•Main component waves represent dynamic precursors in rock burst process.