多种群遗传算法在微震震源定位中的应用

微震震源的精确和快速定位对坑道安全的预测至关重要。在设定均质均速模型条件下,两两检波器的观测走时和计算走时的拟合差绝对值之和为适应度函数,把微震震源定位转换为求解优化问题。采用格雷码对震源位置进行编码,提高了遗传算法的局部搜索能力；同时采用两个群体独立进化,分别利用轮盘和排序方法从两个群体中选择优秀个体,将各种群中的优秀个体进行交叉运算和变异产生新的个体,从而提高了遗传算法的全局搜索能力。通过实验证实优化后的遗传算法在微震震源定位中具有较高的性能和精度。

Application of the Multiple-population Genetic Algorithm in Micro-seismic Source Location

It is very important to accurately and rapidly locate the micro-seismic source for the prediction of tunnel safety. Under the condition of setting homogeneous medium and average speed model, the fitness function can be expressed as the sum of absolute value of fitting error of the observation time and the calculation time between two neighboring detectors, and then the micro-seismic source location can be converted into solving the optimization problems. The local search ability of genetic algorithm is enhanced by using the Gray Code to encode the micro-seismic source location. At the same time, the independent evolutionary approach is adopted to obsolete two groups through roulette and sorting, respectively. And the best are selected from two groups of individuals to crossover and mutation to produce new individual to improve the global search ability of genetic algorithm. The experiments confirm that the optimized genetic algorithm has high performance and precision in micro-seismic source location.