针对常规叠前AVO反演存在强烈依赖于初始模型、易陷入局部最优值等问题，对基本遗传算法进行了自适应改进，然后将改进遗传算法与粒子群算法相结合，发展了遗传-粒子群算法混合的GA-PSO协同进化智能优化算法；对比改进遗传算法、粒子群算法及GA-PSO协同进化算法反演的理论模型合成地震记录的纵波速度、横波速度及密度，表明后者具有精确的反演结果及更强的稳定性和抗噪能力；最后利用GA-PSO协同进化算法对实际地震数据进行叠前AVO非线性反演，验证了算法的应用效果和适用性。
Abstract
As the conventional prestack AVO inversion is dependent on the initial model and easily trapped into a local optimal solution,we propose a nonlinear AVO inversion based on the hybrid intelligent optimization algorithm.First we improve adaptively the conventional genetic algorithm.Then combining the improved genetic algorithm and particle swarm algorithm,we put forward a hybrid GA-PSO co-evolution algorithm.After that we apply the improved genetic algorithm,the particle swarm algorithm,and the GA-PSO co-evolution algorithm to model synthetic data.With the comparison of inverted P-wave velocity,shear wave velocity and density,the GA-PSO co-evolution algorithm shows better inversion result,better stability,and better anti-noise ability than the other two algorithms.In the end,AVO nonlinear inversion with the proposed algorithm is used to real data,and the results confirm its effectiveness and applicability.
关键词
遗传算法 /
粒子群算法 /
混合智能优化算法 /
非线性AVO反演
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Key words
genetic algorithm /
particle swarm optimization /
hybrid intelligent optimization /
nonlinear AVO inversion
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中图分类号：
P631
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参考文献
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脚注
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基金
本项研究受国家重大科技专项子专题"莺琼盆地高温高压天然气富集规律与勘探开发关键技术"（2016ZX05024-005）、中国地质科学院物化探研究所基本科研业务费项目（WHS201308）和2016年"中央高校基本科研业务费"新青年教师计划等联合资助。
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