Joint wave-equation traveltime inversion of diving/direct and reflected waves for P- and S-wave velocity macromodel building
Joint wave-equation traveltime inversion of diving/direct and reflected waves for P- and S-wave velocity macromodel building
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潜水/直达波和反射波的联合波动方程走时反演，用于建立纵波和横波速度宏观模型
DOI：
10.1190/geo2020-0762.1
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发表时间：
2021-05
期刊：
影响因子：
3.3
通讯作者：
Bingluo Gu
中科院分区：
文献类型：
--
作者：
Zhiming Ren;Qianzong Bao;Bingluo Gu
全波形反演（FWI）存在局部最小值问题，需要足够准确的起始模型才能收敛到正确的解。波动方程走时反演（WETI）是反演速度模型长波长分量的有效工具。我们开发了一种联合潜水/直达波和反射波 WETI (JDRWETI) 方法来构建纵波和横波速度宏观模型。我们通过动态扭曲方案估计地震事件（潜水/直达波以及 PP 和 PS 反射）的走时变化，并使用潜水/直达波和反射波的时移构建失配函数。我们基于联合失配函数推导了伴随波动方程和相对于背景模型的梯度。我们应用核分解方案来提取潜水/直达波的核以及 PP 和 PS 反射的层析成像核。对于爆炸源，潜水/直达波和 PP 反射的核以及 PS 反射的核分别用于计算背景模型的纵波和横波梯度。我们通过两阶段反演工作流程实现 JDRWETI：首先，我们使用 P 波梯度反演 P 波和 S 波速度模型，然后我们使用 S 波梯度改进 S 波速度模型。合成数据集和现场数据集的数值测试表明，JDRWETI方法成功地恢复了纵波和横波速度模型的长波长分量，可用于后续弹性FWI的初始模型。此外，JDRWETI方法优于现有的反射WETI方法和级联潜水/直达波和反射波WETI方法，特别是当起始模型浅部存在较大速度误差时。 JDRWETI方法采用两阶段反演流程，即使对于不同纵波和横波速度结构的模型也能得到合理的反演结果。
Full-waveform inversion (FWI) suffers from the local minima problem and requires a sufficiently accurate starting model to converge to the correct solution. Wave-equation traveltime inversion (WETI) is an effective tool to retrieve the long-wavelength components of the velocity model. We have developed a joint diving/direct and reflected wave WETI (JDRWETI) method to build P- and S-wave velocity macromodels. We estimate the traveltime shifts of seismic events (diving/direct waves and PP- and PS-reflections) through the dynamic warping scheme and construct a misfit function using the time shifts of diving/direct and reflected waves. We derive the adjoint wave equations and the gradients with respect to the background models based on the joint misfit function. We apply the kernel decomposition scheme to extract the kernel of the diving/direct wave and the tomography kernels of PP- and PS-reflections. For an explosive source, the kernels of the diving/direct wave and PP-reflections and the kernel of the PS-reflections are used to compute the P- and S-wave gradients of the background models, respectively. We implement JDRWETI by a two-stage inversion workflow: First, we invert the P- and S-wave velocity models using the P-wave gradients, and then we improve the S-wave velocity model using the S-wave gradients. Numerical tests on synthetic and field data sets reveal that the JDRWETI method successfully recovers the long-wavelength components of P- and S-wave velocity models, which can be used for an initial model for the subsequent elastic FWI. Moreover, the JDRWETI method prevails over the existing reflection WETI method and the cascaded diving/direct and reflected wave WETI method, especially when large velocity errors are present in the shallow part of the starting models. The JDRWETI method with the two-stage inversion workflow can give rise to reasonable inversion results even for the model with different P- and S-wave velocity structures.
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