Evaluation of the characterization of acoustic emission of brittle rocks from the experiment to numerical simulation

Abstract

Acoustic emission (AE) characterization is an effective technique to indirectly capture the failure process of quasi brittle rock. In previous studies, both experiments and numerical simulations were adopted to investigate the AE characteristics of rocks. However, as the most popular numerical model, the moment tensor model (MTM) cannot be constrained by the experimental result because there is a gap between MTM and experiments in principle, signal processing and energy analysis. In this paper, we developed a particle-velocity-based model (PVBM) that enabled direct monitoring and analysis of the particle velocity in the numerical model and had good robustness. The PVBM imitated the actual experiment and could fill in gaps between the experiment and MTM. AE experiments of marine shale under uniaxial compression were carried out, and the results were simulated by MTM. In general, the variation trend of the experimental result could be presented by MTM. Nevertheless, the magnitudes of AE parameters by MTM presented notable differences of more than several orders of magnitude compared with those by the experiment. We sequentially used PVBM as a proxy to analyse these discrepancies and systematically evaluate the AE characterization of rocks from the experiment to numerical simulation, considering the influence of wave reflection, energy geometrical diffusion, viscous attenuation, particle size and progressive deterioration of rock material. The combination of MTM and PVBM could reasonably and accurately acquire AE characteristics of the actual AE experiment of rocks by making full use of their respective advantages.

Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes

“…Numerous laboratory studies have shown that the onset of failure is associated with bursts of acoustic emission (AE) events taking place during crack initiation and growth, and the number and amplitude of AE events generally increase as the sample approaches failure 14 – 24 . Recent friction studies on laboratory faults have shown that machine learning (ML) algorithms can actually predict the timing and magnitude of lab quakes using AE data 15 , 16 , 25 – 32 .…”

Experimental Study on Acoustic Emission Characteristics of Granite and Sandstone under Uniaxial Compression

“…The failure of rock is essentially the process of internal crack initiation and expansion until macroscopic cracks are formed; therefore, there is an inevitable connection between the acoustic emission phenomenon of rock and the force failure of rock [8,9]. According to the indoor acoustic emission test law, the acoustic emission characteristics of different rock failure processes are studied; it plays an extremely important role in the study of rock deformation, failure, and instability; the use of acoustic emission detection technology can effectively monitor the real-time information of changes in the rock [10][11][12], and predicting the process of rock failure [13][14][15] has a wide range of engineering application value [16][17][18].…”

“…Literature [19] proposed a new rock damage expression method by studying the acoustic emission characteristics of rock samples with different lithology under uniaxial compression and established the functional relationship between damage variables and stress. Literature [8] studied the acoustic emission characteristics of rocks based on the MTM model combined with the developed PVBM model. Literature [20] found that the existence of cracks in rock will lead to more obvious acoustic emission directivity of Kaiser effect, which provides a basis for improving the measurement accuracy of ground stress and rock fracture toughness.…”

Stress levels of precursory strain localization subsequent to the crack damage threshold in brittle rock

“…Thanks to the acoustic emission (AE) technique in the laboratory, monitoring analyses can be conducted to observe the pre-failure microcrack accumulation [15][16][17][18][19][20][21][22][23][24] as well as to detect damage acceleration in brittle rocks consisting of pre-existing flaws [25][26][27][28][29][30][31]. Propagation and distribution of microcracks with increasing axial stress can be followed in rock samples by measuring the energy released.…”