Elastic Full Waveform Inversion With Source-Independent Crosstalk-Free Source-Encoding Algorithm

Elastic full waveform inversion (FWI) is more suitable to process multicomponent seismic data and can provide more subsurface medium information than acoustic FWI often with lower efficiency. Except for the parallel algorithms, source-encoding methods are usually adopted to improve the efficiency of...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 58; no. 4; pp. 2915 - 2927
Main Authors: Zhang, Qingchen, Mao, Weijian, Fang, Jinwei
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Acoustic noise
Acoustics
Algorithms
Computer simulation
Convolution
Crosstalk
Efficiency
elastic full waveform inversion (FWI)
Encoding
Estimation
Frequency-domain analysis
Harmonic functions
Linear programming
Orthogonality
Seismic data
source-encoding
source-independent
Trigonometric functions
Waveforms
Wavelet analysis
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Elastic full waveform inversion (FWI) is more suitable to process multicomponent seismic data and can provide more subsurface medium information than acoustic FWI often with lower efficiency. Except for the parallel algorithms, source-encoding methods are usually adopted to improve the efficiency of FWI, but it often includes crosstalk noise. Besides, the additional source estimation process, critical for a successful FWI, would counteract the high-efficiency advantage of the source-encoding algorithm. We propose an elastic FWI with source-independent crosstalk-free encoding algorithm to solve the above problems. Arbitrary-phase harmonic sine functions are used as new source wavelets to perform the time-domain wavefield simulation regardless of the true wavelet. Treating the harmonic wavelet as the encoding operator and based on the orthogonality of trigonometric functions within integer periods, the amplitude and phase of each source are recovered from the blended source and adjoint wavefields so that the influence of crosstalk noise is avoided. With the deblended data, the proposed algorithm can be naturally applied to unfixed-spread acquisition systems. Moreover, we can conveniently perform the multiscale inversion by controlling the frequencies of simultaneous-source signals as conventional frequency-domain FWI does. Synthetic examples show that the proposed algorithm has high efficiency and accuracy with a strong robustness to the incorrect wavelets.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2019.2957829