最小二乘逆时偏移(Least-Square Reverse Time Migration,LSRTM)相比于常规偏移具有更高的成像分辨率、振幅保幅性及均衡性等优势,是当前研究的热点之一.然而,目前LSRTM算法大多是基于二阶常密度标量声波方程建立的,忽略了密度变化对振幅的影响,因而基于振幅匹配策略的常规LSRTM很难在变密度介质下取得保真的成像结果.一阶速度-应力方程能够很好地处理变密度介质,但简单地将一阶速度-应力方程应用到LSRTM中缺乏理论基础.为此,本文从LSRTM的正问题入手,提出了基于交错网格的一阶速度-应力方程LSRTM理论方法.首先将一阶波动方程线性化,建立了一阶方程LSRTM的目标泛函,随后推导其伴随方程,并借助伴随状态法给出了迭代更新流程,最终建立了基于一阶速度-应力方程LSRTM的理论框架.进一步,通过在相位编码LSRTM中引入随机最优化思想,极大地减小了计算量、提高了计算效率.最后,通过模型试算验证了本算法的正确性和有效性. 相似文献
The common-reflection-surface (CRS) stacking is a new seismic imaging method, which only depends on seismic three parameters and near-surface velocity instead of macro-velocity model. According to optimized three parameters obtained by CRS stacking, we derived an analytical relationship between three parameters and migration velocity field, and put forward CRS gather migration velocity modeling method, which realize velocity estimation by optimizing three parameters in CRS gather. The test of a sag model proved that this method is more effective and adaptable for velocity modeling of a complex geological body, and the accuracy of velocity analysis depends on the precision of optimized three parameters. 相似文献