Abstract: Seismic inversion belongs to nonlinear optimum problem.An Improved Simulated Annealing Genetic Algorithm（ISAGA） for seismic parameters inversion by combining Modified Genetic Algorithm（MGA） with Simulated Annealing Algorithm（SAA） is developed.In the proposed method firstly the initial population is selected by filtering and restoring.Secondly the annealing selection mechanism is proposed which the elder population is permitted to compete.The new algorithm provides not only with strong global search capability of GA，but also with strong local search capability of SAA.Thirdly to improve the convergence speed and the diversity of the population the rates of crossover and mutation are modified self-adaptively.The simulation results indicate that the ISAGA can result in fast convergence rate and high optimization precision；moreover it avoids many shortcomings such as initial model sensitivity.The obtained section can well reflect the geology characteristics.

Key words: seismic parameters inversion, simulated annealing genetic algorithm, simulated annealing algorithm, genetic algorithm, hybrid optimization

摘要： 地震参数反演属于典型的非线性优化问题。针对遗传算法和模拟退火算法各自的优缺点，将改进的遗传算法与模拟退火算法相结合，提出了改进的退火遗传算法（ISAGA）。该方法通过筛选和修复进行初始种群的选择，采用允许父代参与竞争的退火选择机制，并根据模拟退火思想对交叉和变异概率进行自适应的调整，从而增加了种群的多样性并提高了收敛速度。该方法既具备了遗传算法强大的全局搜索能力，也拥有模拟退火算法强大的局部搜索能力。经理论模型试算结果表明，该方法不仅收敛速度快，优化精度高，抗干扰能力强，而且避免了局部收敛和依赖初始模型等问题，计算所得反演参数更接近于实际观测值。

关键词: 地震参数反演, 模拟退火遗传算法, 模拟退火算法, 遗传算法, 混合优化