Open AccessMulti-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function
Author(s) -
Yong Hu,
Liguo Han,
RuShan Wu,
Yong Xu
Publication year - 2019
Publication title -
journal of geophysics and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.623
H-Index - 38
eISSN - 1742-2140
pISSN - 1742-2132
DOI - 10.1093/jge/gxz062
Subject(s) - inversion (geology) , weighting , algorithm , amplitude , frequency domain , time domain , synthetic data , gaussian , correlation , computer science , mathematics , geology , mathematical analysis , physics , optics , seismology , geometry , acoustics , quantum mechanics , computer vision , tectonics
Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.