Bayesian Microseismic Localization Method Based on the Maximum Entropy Hamiltonian Monte Carlo Markov Chain Approach

In recent years, as coal mining operations delve deeper, the incidence of rockburst-prone mines has also increased, prompting increasing attention towards microseismic monitoring technology. Localization algorithms for microseismic events are central to microseismic monitoring. This study introduces a novel Bayesian localization algorithm that leverages the maximum entropy strategy—MaxEnt-HMC. The MaxEnt-HMC method integrates Bayesian inference with Hamiltonian Monte Carlo (HMC), enhancing both localization precision and computational efficiency. By embracing the principle of maximum entropy, the method maximizes information retention during sampling, efficiently explores high-dimensional parameter spaces, and minimizes sample autocorrelation. We demonstrated the robustness of the MaxEnt-HMC method through extensive numerical simulations. When compared to traditional grid search methods and standard HMC methods, this approach showed superior performance. Subsequent field tests conducted in a coal mine environment further highlighted the method's high precision and efficiency, establishing it as a valuable tool for real-time microseismic monitoring. This study overcomes the limitations of traditional localization methods, offering a potent and efficient solution with broad applications in monitoring induced microseismic events, including those associated with mining, hydraulic fracturing, oil and gas extraction, and other activities.