通过整合多参数粘声全波形反演中的赫塞斯,推进衰减估算工作

GEOPHYSICS Pub Date : 2024-05-21 DOI:10.1190/geo2023-0634.1
G. Xing, Tieyuan Zhu
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引用次数: 0

摘要

准确的地下结构地震衰减模型不仅能提高保真度和分辨率,促进振幅角集的生成,从而加强后续的地震迁移过程,还能为地下物理特性提供宝贵的约束条件。利用全波场信息,多参数粘声全波形反演(Q-FWI)可同时估算地震速度和衰减(Q)模型。然而,Q-FWI 的一个主要挑战是串扰伪影的污染,即速度模型的不准确性被错误地映射到反演的衰减模型上。虽然加入 Hessian 可以减少这些伪影,但由于计算成本高昂,显式实现的成本过高。在本研究中,我们通过牛顿-共轭梯度(CG)框架制定并开发了一种 Q-FWI 算法,其中每次迭代的搜索方向都是通过内部共轭梯度(CG)循环确定的。特别是,利用二阶邻接态方法,以无矩阵方式将 Hessian 集成到每个共轭梯度步骤中。我们通过合成实验发现,与 L-BFGS 方法和共轭梯度 (CG) 方法相比,所提出的牛顿-共轭梯度 Q-FWI 能显著减少串扰伪影,尽管计算成本较高。在对几个关键实现细节的讨论中,我们还证明了近似高斯-牛顿赫塞斯、二阶邻接态方法和两阶段反演策略的重要性。
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Advancing attenuation estimation through integration of the Hessian in multiparameter viscoacoustic full-Waveform inversion
Accurate seismic attenuation models of subsurface structures not only enhance subsequent migration processes by improving fidelity, resolution, and facilitating amplitude-compliant angle gather generation, but also provide valuable constraints on subsurface physical properties. Leveraging full wavefield information, multiparameter viscoacoustic full-waveform inversion ( Q-FWI) simultaneously estimates seismic velocity and attenuation ( Q) models. However, a major challenge in Q-FWI is the contamination of crosstalk artifacts, where inaccuracies in the velocity model get mistakenly mapped to the inverted attenuation model. While incorporating the Hessian is expected to mitigate these artifacts, the explicit implementation is prohibitively expensive due to its formidable computational cost. In this study, we formulate and develop a Q-FWI algorithm via the Newton-CG framework, where the search direction at each iteration is determined through an internal conjugate gradient (CG) loop. In particular, the Hessian is integrated into each CG step in a matrix-free fashion using the second-order adjoint-state method. We find through synthetic experiments that the proposed Newton-CG Q-FWI significantly mitigates crosstalk artifacts compared to the L-BFGS method and the conjugate gradient (CG) method, albeit with a notable computational cost. In the discussion of several key implementation details, we also demonstrate the significance of the approximate Gauss-Newton Hessian, the second-order adjoint-state method, and the two-stage inversion strategy.
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