To address the challenges in rock acoustic emission localization

such as difficulty in picking up arrival times and unsolvable equation systems leading to low accuracy and even inability to locate

a novel localization algorithm was proposed. This algorithm is based on vector auto regressive -Akaike information criterion (VAR-AIC) time-of-arrival pickup and integrates an intelligent optimization algorithm. The improved VAR-AIC method enhances arrival time accuracy by utilizing instantaneous frequency to define the arrival time range and selecting a feature function for precise determination. The nonlinear positioning equations are transformed into an objective function using the Newton-Raphson method

enabling iterative solutions through optimization algorithms. Broken lead test results indicate that the VAR-AIC method significantly enhances acoustic emission arrival time pickup accuracy by determining the arrival time range through instantaneous frequency and selecting the feature function for precise determination. Comparing the performance of the atomic orbital search

gray wolf

and adaptive particle swarm algorithms reveals that the atomic orbital search algorithm not only ensures localization precision but also accelerates iteration speed. Consequently

a rock acoustic emission localization method of improve vector auto regression -Akaike information criterion-atomic orbital earth (IVA-AOS) combining VAR-AIC accurate time-to-time pickup and the atomic orbital search algorithm was proposed. Compared to traditional methods like the mutual correlation+Geiger algorithm

Newton iterative algorithm and PCI-Express8 machine self- contained algorithm

the IVA-AOS localization algorithm exhibits smaller localization errors and higher precision. This innovation effectively addresses the frequent unsolvable cases in traditional localization algorithms

offering new insights and methodologies for rock acoustic emission localization.