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Elastic full-waveform inversion of steeply dipping structures with prismatic waves
Geophysics ( IF 3.3 ) Pub Date : 2021-06-15 , DOI: 10.1190/geo2020-0482.1
Zheng Wu 1 , Yuzhu Liu 2 , Jizhong Yang 3
Affiliation  

High-resolution reconstruction of steeply dipping structures is an important but challenging subject in seismic exploration. Prismatic reflections that contain information on these structures are helpful for reconstructing steeply dipping structures. Elastic full-waveform inversion (EFWI) is a powerful tool that can accurately estimate subsurface parameters from multicomponent seismic data, which can provide information useful for characterizing oil and gas reservoirs. We have constructed the relationship between the forward and inverse problems related to the prismatic reflections by considering the multiparameter exact Hessian in realistic elastic media. We numerically analyze the characteristics of the multiparameter exact Hessian and determine that, when prismatic reflections are apparent in multicomponent data, the multiparameter delta Hessian has a strong influence. We develop this in more detail through forward analysis and determine that the multiparameter delta Hessian considers not only the prismatic reflections but also compensates for the primary reflections in multicomponent data. To use the prismatic waves, we develop a migration/demigration approach-based truncated Newton (TN) method in frequency-domain EFWI, whose storage requirements and computational costs are the same as those of the truncated Gauss-Newton (TGN) method. Realistic 2D numerical examples demonstrate that, compared with the TGN method based on the first-order Born approximation, the TN method can converge faster and obtain higher accuracy in the reconstruction of steeply dipping structures.

中文翻译:

具有棱柱波的陡倾结构的弹性全波形反演

陡倾结构的高分辨率重建是地震勘探中一个重要但具有挑战性的课题。包含这些结构信息的棱镜反射有助于重建陡倾结构。弹性全波形反演 (EFWI) 是一种强大的工具,可以从多分量地震数据中准确估计地下参数,为表征油气藏提供有用的信息。通过考虑现实弹性介质中的多参数精确 Hessian,我们构建了与棱镜反射相关的正反问题之间的关系。我们对多参数精确 Hessian 的特征进行了数值分析,并确定,当多分量数据中出现明显的棱镜反射时,多参数 delta Hessian 有很强的影响。我们通过前向分析对此进行了更详细的开发,并确定多参数 delta Hessian 不仅考虑了棱镜反射,而且还补偿了多分量数据中的主要反射。为了使用棱柱波,我们在频域 EFWI 中开发了一种基于迁移/反迁移方法的截断牛顿 (TN) 方法,其存储要求和计算成本与截断高斯-牛顿 (TGN) 方法相同。真实的二维数值例子表明,与基于一阶Born近似的TGN方法相比,TN方法在重建陡倾结构时收敛速度更快,精度更高。我们通过前向分析对此进行了更详细的开发,并确定多参数 delta Hessian 不仅考虑了棱镜反射,而且还补偿了多分量数据中的主要反射。为了使用棱柱波,我们在频域 EFWI 中开发了一种基于迁移/反迁移方法的截断牛顿 (TN) 方法,其存储要求和计算成本与截断高斯-牛顿 (TGN) 方法相同。真实的二维数值例子表明,与基于一阶Born近似的TGN方法相比,TN方法在重建陡倾结构时收敛速度更快,精度更高。我们通过前向分析对此进行了更详细的开发,并确定多参数 delta Hessian 不仅考虑了棱镜反射,而且还补偿了多分量数据中的主要反射。为了使用棱柱波,我们在频域 EFWI 中开发了一种基于迁移/反迁移方法的截断牛顿 (TN) 方法,其存储要求和计算成本与截断高斯-牛顿 (TGN) 方法相同。真实的二维数值例子表明,与基于一阶Born近似的TGN方法相比,TN方法在重建陡倾结构时收敛速度更快,精度更高。
更新日期:2021-06-15
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