# GA-PSO Algorithm for Microseismic Source Location
## 4 Discussion and Conclusions  
The microseismic source location method is a core component of microseismic monitoring and a key factor in improving the accuracy of disaster prediction. To address the limitations of traditional location algorithms, such as inaccurate location results or poor convergence, this study combines the improved PSO algorithm with the GA. The PSO algorithm is enhanced by introducing a dynamic learning factor and adaptive inertia weight updating, which prevents the algorithm from falling into local optima in the later stages. The integration of the GA's strengths ensures faster convergence and higher location accuracy. To verify the accuracy of the algorithm, simulated spatial experiments were conducted. The results demonstrate that the GA-PSO algorithm achieves the smallest error values, significantly outperforming both the GA and PSO algorithms, as well as the PSO-ACO and NM-PSO algorithms. It exhibits improved convergence under the same number of iterations and achieves the lowest fitness value. In a comprehensive comparative experiment, this study simulated the positioning accuracy of the five algorithms under wave speed fluctuations of \(\pm\)1%, \(\pm\)3%, and \(\pm\)5%. The results show that the GA-PSO algorithm is least affected by these fluctuations, maintaining a positioning error consistently below 0.5 m.  
Finally, the algorithm was tested on a real engineering case. Compared with the GA and PSO algorithm, the GA-PSO algorithm demonstrates improved localization accuracy under the given experimental conditions, achieving an average localization error of approximately 20 m, which represents a 59% improvement over the GA and a 43% improvement over the PSO algorithm. The GA-PSO algorithm currently provides precise and efficient seismic source localization and is capable of detecting minor vibrations. It is particularly well suited for monitoring surface or subsurface intrusions, such as real-time detection of illegal mining activities. However, in large-scale sensor networks (such as those with more than 100 nodes), high-frequency data acquisition may introduce computational delays, posing challenges to the algorithm's computational efficiency. Furthermore, the current algorithm is designed \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **X/m** & **Y/m** & **Z/m** & **X-Axis Error** & **Y-Axis Error** & **Z-Axis Error** & **Absolute Error** \\ \hline Actual source location & 8732.70 & 6570.60 & 511.30 & & & & \\ PSO & 8720.56 & 6545.42 & 533.49 & 11.33 & 25.34 & \(-\)22.16 & 35.52 \\ GA & 8719.87 & 6523.03 & 513.30 & 12.83 & 47.57 & \(-\)2.00 & 49.31 \\ PSO-ACO & 8782.52 & 6555.73 & 500.02 & \(-\)49.82 & 14.87 & 11.28 & 53.20 \\ NM-PSO & 8721.29 & 6544.60 & 532.51 & 11.41 & 26.00 & \(-\)21.21 & 35.44 \\ GA-PSO & 8733.93 & 6550.59 & 510.12 & \(-\)1.23 & 20.01 & 1.18 & 20.08 \\ \hline \end{tabular}
\end{table} 
Table 8: Comparison of positioning results using five location methods under engineering validation. for single-source localization and has not been tested for applicability in multi-source scenarios. Another limitation is the algorithm's sensitivity to parameters, which may reduce its adaptability in complex environments. Future research will focus on optimizing computational efficiency by integrating parallel computing and distributed processing frameworks to enhance real-time performance in large-scale applications. To improve the algorithm's adaptability, future work will explore the integration of reinforcement learning strategies (such as Q-learning), thereby reducing the reliance on manual parameter tuning. The incorporation of deep learning-based clustering methods is expected to improve the algorithm's localization capability in multi-source events, making it more suitable for complex monitoring environments. Additionally, future studies will involve field experiments and large-scale simulations to validate the effectiveness of the proposed improvements and assess its robustness in real-world applications. Conceptualization, F.Z. ([PERSON]); methodology, Y.H.