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<p>Microseismic source localization is a core component of microseismic monitoring technology, crucial for reducing the impact of geological disasters and enhancing early warning and prevention of tunnel water inrush disasters; the accuracy of microseismic source localization significantly influences the efficacy of microseismic analysis. In the actual microseismic signal propagation process, influenced by the non-homogeneous and discontinuous features of rock structure and the variability of water content, the microseismic signal velocity model becomes complicated and challenging to determine accurately, leading to low accuracy in microseismic source localization. To address these issues, this paper explores the application of fusing the artificial bee colony and cuckoo search optimization algorithms (ABC-CS) in microseismic source localization, with the ultimate goal of applying it to the prevention and control of tunnel water and mud inrush. This method adopts the concept of joint positioning, which does not require prior determination of the propagation velocity of microseismic signals in the monitoring area. Its objective is to minimize the temporal discrepancy between the arrival times of microseismic signals detected by sensors and the calculated (theoretical) time differences. By employing intelligent optimization algorithms, the velocity model and the coordinates of the seismic source to be determined are both treated as unknowns for simultaneous solution, overcoming the problem of poor localization effectiveness in classical positioning methods caused by the use of inaccurate velocity models. In this paper, microseismic source localization analysis is conducted in both homogeneous and inclined stratigraphic models using this method. The superiority of the proposed method is verified through on-site blasting tests, showing significant improvements in global optimization capability, localization accuracy—with a positioning error of approximately 5&#xa0;m—and the stability of output solutions, which lays a solid foundation for its eventual application in the prevention and control of tunnel water and mud inrush disasters.</p>

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Microseismic source localization method based on ABC-CS improved fusion algorithm

  • 期刊论文

  • 发表日期：

    2026年3月20日

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  • Wenfeng Tu,
  • Chao Jia,
  • Liping Li,
  • Shuai Cheng,
  • Diyang Chen,
  • Yanhuan Zhang,
  • Zhangcheng Yan,
  • Feng Zhang

摘要

Microseismic source localization is a core component of microseismic monitoring technology, crucial for reducing the impact of geological disasters and enhancing early warning and prevention of tunnel water inrush disasters; the accuracy of microseismic source localization significantly influences the efficacy of microseismic analysis. In the actual microseismic signal propagation process, influenced by the non-homogeneous and discontinuous features of rock structure and the variability of water content, the microseismic signal velocity model becomes complicated and challenging to determine accurately, leading to low accuracy in microseismic source localization. To address these issues, this paper explores the application of fusing the artificial bee colony and cuckoo search optimization algorithms (ABC-CS) in microseismic source localization, with the ultimate goal of applying it to the prevention and control of tunnel water and mud inrush. This method adopts the concept of joint positioning, which does not require prior determination of the propagation velocity of microseismic signals in the monitoring area. Its objective is to minimize the temporal discrepancy between the arrival times of microseismic signals detected by sensors and the calculated (theoretical) time differences. By employing intelligent optimization algorithms, the velocity model and the coordinates of the seismic source to be determined are both treated as unknowns for simultaneous solution, overcoming the problem of poor localization effectiveness in classical positioning methods caused by the use of inaccurate velocity models. In this paper, microseismic source localization analysis is conducted in both homogeneous and inclined stratigraphic models using this method. The superiority of the proposed method is verified through on-site blasting tests, showing significant improvements in global optimization capability, localization accuracy—with a positioning error of approximately 5 m—and the stability of output solutions, which lays a solid foundation for its eventual application in the prevention and control of tunnel water and mud inrush disasters.