基于粒子群优化算法的AVAF反演方法

王震宇, 刘俊州, 时磊, 刘颖, 夏红敏. 基于粒子群优化算法的AVAF反演方法[J]. 地球物理学进展, 2019, 34(6): 2346-2352. doi: 10.6038/pg2019CC0408
引用本文: 王震宇, 刘俊州, 时磊, 刘颖, 夏红敏. 基于粒子群优化算法的AVAF反演方法[J]. 地球物理学进展, 2019, 34(6): 2346-2352. doi: 10.6038/pg2019CC0408
WANG Zhen-yu, LIU Jun-zhou, SHI Lei, LIU Ying, XIA Hong-min. AVAF inversion method based on particle swarm optimization algorithm[J]. Progress in Geophysics, 2019, 34(6): 2346-2352. doi: 10.6038/pg2019CC0408
Citation: WANG Zhen-yu, LIU Jun-zhou, SHI Lei, LIU Ying, XIA Hong-min. AVAF inversion method based on particle swarm optimization algorithm[J]. Progress in Geophysics, 2019, 34(6): 2346-2352. doi: 10.6038/pg2019CC0408

基于粒子群优化算法的AVAF反演方法

详细信息
    作者简介:

    王震宇,男,1986年生,工程师,主要从事岩石物理建模及地震储层预测等研究工作.(E-mail:wangzy.syky@sinopec.com)

  • 中图分类号: P631

AVAF inversion method based on particle swarm optimization algorithm

  • 岩石物理实验和实际观测研究表明,纵波速度的频散现象通常都与地层的含气性有着密切的关系,它是纵波反射系数随频率变化所导致的.但是,传统的AVA反演方法忽略了这种速度频散现象,因此引入了误差,增加了含气预测的风险.本文我们提出了一种适用于频变反射系数和速度频散的反演方法,采用传播矩阵方程来进行正演模拟.而考虑到加入频散信息的AVAF反演问题具有高度的非线性特征,我们基于粒子群优化(PSO)算法来进行AVAF反演.经过模型与实际数据的测试证明,我们的反演方法适用于包含频散信息的地震数据,且具有一定的抗噪性,即使是在含噪数据下,也能够挖掘出数据中的纵波速度频散信息,为之后利用纵波速度的频散规律来解释储层含气性提供可靠的依据.
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出版历程
收稿日期:  2019-02-25
修回日期:  2019-09-11
刊出日期:  2019-12-20

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