Practice and validation of inversion of seismic source localisation based on genetic algorithm
Quan Zhang, Junpeng Zou*, Yu-Yong Jiao, Jiapeng Gao
Deep Resources Engineering. 2025, 2(1): 100164. 
doi.org10.1016j.deepre.2025.100164.pdf
Abstract: As the mining depth of coal resources increases, resulting in frequent mine earthquakes during mining. In this study, the rolling window ratio method is firstly chosen as the seismic phase recognition method to read the mine earthquake data received by the microseismic sensor. Secondly, the improved genetic algorithm is used as the optimization algorithm of the objective function to build the algorithmic framework of accurate inverse localization of mine earthquake. Finally, the accuracy of this algorithm for seismic source localization is validated using actual engineering cases. Results show that the first arrival time extraction by the rolling window ratio method has the advantages of high accuracy and fast algorithm operation speed. The Fast Fourier Transform-Butterworth joint noise reduction method has a good noise reduction effect, which successfully suppressing noise outside the mine earthquake signal and effectively improving the issue of excessive noise in the mine earthquake signal. Compared to microseismic monitoring data, the localization error for mine earthquakes remains within 5%.
Highlights:
• The first arrival time extraction by the rolling window ratio method has the advantages of high accuracy.
• The Fast Fourier Transform (FFT)-Butterworth joint noise reduction method has a good noise reduction effect.
• The FFT-Butterworth joint noise reduction method improves the issue of excessive noise in the mine earthquake signal.
Keywords: Mine earthquake; Epicenter localization; Fast Fourier Transform-Butterworth joint noise reduction method; Rolling window ratio
Cite: Zhang, Q.; Zou, J.P.; Jiao, Y.-Y.; Gao, J.P., Practice and validation of inversion of seismic source localisation based on genetic algorithm. Deep Resources Engineering 2025, 2 (1): 100164.https://doi.org/10.1016/j.deepre.2025.100164
