Correlative Full-Intensity Waveform Inversion

Bin He, Yike Liu, Huiyi Lu, Zhendong Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Full-waveform inversion (FWI) is considered an effective technique for building high-resolution velocity models by fitting observed seismology waveforms. When the observed waveforms lack low frequencies or when the starting model is dissimilar to the true model, FWI usually suffers from cycle-skipping problems. To mitigate this difficulty, we propose a new correlative objective function that matches the phase differences between the seismic-waveform intensities to provide a good starting model. The waveform intensity separates the frequency band of the original data into an ultralow-frequency part and a higher frequency part, even when the original data lacking in low-frequency information. As the low-frequency part of the intensity is less prone to the cycle-skipping problem, it can be used to build an initial model for FWI. Furthermore, the source wavelets, in practice, are estimated from the observed data, which may be inaccurate in amplitude, phase shift, and time delay. To mitigate the inaccurate-source problem, a Wiener filter is constructed to build a source-independent objective function. Applications to the Marmousi model and Chevron blind-test model demonstrate that the proposed method converges to an acceptable initial model for conventional FWI. It is highly efficient and insensitive to inaccurate source wavelets, including inaccurate amplitude, phase shift, and time delays.
Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
DOIs
StatePublished - 2020

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