Adaptive Local Gauss-Newton Based Inverse Hessian Preconditioning for Elastic Full-Waveform Inversion

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ID: 322667
2026
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Abstract
Summary Full-waveform inversion (FWI) is a key tool for velocity model building. Multiparameter elastic FWI suffers from parameter coupling and unbalanced radiation sensitivities, which generate crosstalk and hinder convergence. The Gauss–Newton (GN) method alleviates these limitations by incorporating second-order curvature information, but its computational cost remains a limiting factor. We present an adaptive local inverse-Hessian preconditioning approach for elastic FWI that approximates the GN update without solving the global linear system. The method constructs a local inverse mapping between curvature responses and model perturbations using reference perturbations and associated curvature, which is defined as the Hessian-vector product in sense of GN. This formulation captures both diagonal and off-diagonal contributions of the inverse Hessian, providing an explicit treatment of parameter coupling. The local systems are solved by singular value decomposition with Tikhonov regularization to improve stability. Numerical experiments on synthetic models, including a crosstalk-sensitive anomaly test and the Marmousi II model, indicate that the proposed method yields update directions consistent with those of a fully converged GN solution at substantially lower cost. Compared with block-diagonal pseudo-Hessian and truncated GN implementations, the method shows reduced crosstalk, faster misfit reduction, and improved reconstruction under comparable computational constraints.
Reference Key
openalex_W7171366277 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Guochang Zu, Peng Song, Jun Tan, Jian Sun, Guangzhao Liu, Dong Liu
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag299
URL
https://doi.org/10.1093/gji/ggag299
Keywords Keywords not found

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