Numerical statistical analysis on self-organizing behavior of microfracturing events in rock failure

Abstract

Current experimental investigations on microfracturing (or acoustic emission) events mainly focus on their location and distribution. A new function in rock failure process analysis (RFPA2D) code was developed to capture the size and number of damage element groups in each loading step. The rock failure process evolving from the initiation, propagation, and nucleation of microcracks was visually simulated by RFPA2D in this research. Based on the newly developed function, the statistical quantitative analysis of microfracturing events in rock was effectively conducted. The results show that microfracturing (failed element) events in the whole failure process accord with negative power law distribution, showing fractal features. When approaching a self-organized criticality state, the power exponent does not vary drastically, which ranges around 1.5 approximately. The power exponent decreases correspondingly as the stress increases. Through the analysis of the frequency and size of damaged element groups by rescaled range analysis method, the time series of microfracturing events exhibits the self-similar scale-invariant properties. Through the analysis by the correlation function method, the absolute value of the self-correlation coefficient of microfracturing sequence demonstrates a subsequent precursory increase after a long time delay, exhibiting long-range correlation characteristics. These fractal configuration and long-range correlations are two fingerprints of self-organized criticality, which indicates the occurrence of self-organized criticality in rock failure. Compared with the limited in situ monitoring data, this simulation can supply more sufficient information for the prediction of unstable failure and good understanding of the failure mechanism.