Underground Source Localization Method Based on Adaptive Particle Swarm Optimization
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    Abstract:

    In the shallow seismic source location, the positioning model is the key to achieve high-precision positioning. However, due to the complex structure of the shallow underground medium, the extraction of characteristic parameters is difficult, and the number of sources is small, and the single-shot vibration data is limited, resulting in the traditional travel time positioning model is not accurate in the shallow microseismic positioning area. Aiming at the above problems, based on the travel time positioning model, combined with deep polarization information, and improved the traditional particle swarm optimization algorithm, this study proposes a high-precision source localization method based on its fast convergence speed and high positioning accuracy. The experimental simulation results show that the population optimization and cross-mutation PSO algorithm can effectively reduce the risk of the algorithm falling into the local extremum when solving the hybrid positioning model, and verify the accuracy of the algorithm, which can effectively improve the microseismic positioning.

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辛伟瑶,李剑,韩焱,李禹剑.基于自适应粒子群优化算法的地下震源定位方法.计算机系统应用,2019,28(12):165-170

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History
  • Received:May 09,2019
  • Revised:May 30,2019
  • Adopted:
  • Online: December 13,2019
  • Published: December 15,2019
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