基于自适应粒子群优化算法的地下震源定位方法

在地下震动目标定位领域中，定位模型是实现高精度定位的关键，但是由于地下空间的介质分布散乱，结构复杂，群波混叠现象较为严重，导致特征参量提取难度大，且震动数量较少，单次震动数据有限，造成传统的走时定位模型在地下空间微震定位区域中精确度不高.针对上述问题，本文通过结合浅层走时信息以及深层偏振信息，并在传统粒子群算法的基础上改进种群策略，引入交叉变异机制，利用其收敛速度快，定位精度高等优点，提出了一种基于走时-偏振混合定位模型的地下震源高精度定位方法.进行试验仿真，结果表明：通过种群改进以及交叉变异的PSO算法，解算混合定位模型时，能在一定程度有效地提高算法的全局收敛性，并验证了该算法的准确性，可有效提高微震定位的精确度.

Underground Source Localization Method Based on Adaptive Particle Swarm Optimization

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.