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Fracture Insights and Predicting Failures: Acoustic Emission Study in Peteroa Volcano's Basalt Rock

Description: 

Acoustic Emission (AE) is a valuable non-destructive testing method for detecting and analyzing elastic waves that occur in a material subjected to external forces. This research is acutely attuned to the significance of AE in the context of volcanic monitoring and focuses on the application of AE in the study of Basalt rock samples extracted from the Peteroa volcano in Argentina. Uniaxial compression tests were performed on these rocks to investigate their fracture properties under different stress states until reaching failure. AE signals were continuously recorded during the experiments and AE parameters were utilized to evaluate the rock's fracture process. Also, 3D location of AE events was performed to monitor damage and map microfractures. Additionally, secondary AE parameters FM/RA, entropy, b-value, AE energy b-value and energy for frequency band were employed to predict and classify fracture modes, distinguishing between tensile and shear-dominated fractures. The analysis of accumulated AE hits and their correlation with the applied force-time curve revealed a significant increase in AE emission rate and energy around a specific critical loading level (2/3 load max approx) indicating a progressive damage of the rock specimen, possibly associated with microfracturing. Furthermore, a decrease in the AE event rate was observed when the force remained constant, suggesting changes in fracture activity during the compression process. Overall, the accumulated AE hit counts proved to be a better indicator of intermediate damage than the accumulated energy, showcasing their potential in tracking the evolution of compression-induced damage in the rock. The 3D localization of AE events was conducted using the Simulated Annealing algorithm. To improve localization accuracy and mitigate noise interference, a novel technique utilizing the Akaike information criterion (AIC) on modes obtained through Empirical Ensemble Mode Decomposition (EEMD) of AE signals was applied.

Session: Multidisciplinary Approaches for Volcanic Eruption Forecasting - I

Type: Oral

Date: 5/2/2024

Presentation Time: 02:00 PM (local time)

Presenting Author: Alejandra

Student Presenter: Yes

Invited Presentation: Yes

Authors