Abstract

In this paper, based on the sensitivity kernel, we propose a matrix decomposition (MD) method to calculate the gradient and the diagonal-approximate Hessian in frequency-domain elastic full waveform inversion. It does not need to store the sensitivity kernel and the diagonalapproximate Hessian directly. The main calculation depends on the forward modeling from sources and unrepeated receivers in frequency domain. For gradients, this method is a good replacement of adjoint-state (AD) FWI when the source number ns is more than the receiver number nr. MD method is also a more efficient and effective than common ways to calculate the diagonalapproximate Hessian. The numerical examples clearly prove effectiveness and potential of the MD method in reconstructing the P and S-wave velocities.

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