A Robust Source Wavelet Phase Inversion Method Based on Correlation Norm Waveform Inversion

Zhang, Pan; Han, Liguo; Lu, Zhanwu; Shang, Xujia; Zhang, Donghao

doi:10.1109/lgrs.2023.3316352

Cited by 2 publications

(1 citation statement)

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“…This kind of source wavelet inversion method has been used in FWI and reverse time migration [39][40][41]. However, just like traditional FWI, this source wavelet inversion method will also be affected by many factors, among which the amplitude error is one of the key factors [42]. When there are amplitude errors or bad traces in the seismic data, inversion will be significantly affected.…”

Section: Introductionmentioning

confidence: 99%

Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion

Qi,

Huang,

Zhang

et al. 2023

Applied Sciences

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The elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimation significantly impacts both the convergence and final result quality. The source wavelet, serving as the initial condition for the wave equation’s forward modeling algorithm, directly influences the matching degree between observed and synthetic data. This study introduces a novel method for estimating the source wavelet utilizing cross-correlation norm elastic waveform inversion (CNEWI) and outlines the EFWI algorithm flow based on this CNEWI source wavelet inversion. The CNEWI method estimates the source wavelet by employing normalized cross-correlation processing on near-offset direct waves, thereby reducing the susceptibility to strong amplitude interference such as bad traces and surface wave residuals. The proposed CNEWI method exhibits a superior computational efficiency compared to conventional L2-norm waveform inversion for source wavelet estimation. Numerical experiments, including in ideal scenarios, with seismic data with bad traces, and with multi-component data, validate the advantages of the proposed method in both source wavelet estimation and EFWI compared to the traditional inversion method.

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