TY - JOUR
T1 - Full-intensity waveform inversion
AU - Liu, Yike
AU - He, Bin
AU - Lu, Huiyi
AU - Zhang, Zhendong
AU - Xie, Xiao-Bi
AU - Zheng, Yingcai
N1 - KAUST Repository Item: Exported on 2021-02-19
Acknowledgements: We thank S. Xu, H. Zhou, Y. Luo, G. T. Schuster, and M. Lu for their helpful suggestions and insightful comments. The research was partially funded by Statoil Petroleum (grant no. 4503288025), the National Nature Science Foundation of China (grant nos. 41730425 and 41430321), and The National Oil and Gas Major Project of China (grant no. 2017ZX05008-007).
PY - 2018/10/23
Y1 - 2018/10/23
N2 - Many full-waveform inversion schemes are based on the iterative perturbation theory to fit the observed waveforms. When the observed waveforms lack low frequencies, those schemes may encounter convergence problems due to cycle skipping when the initial velocity model is far from the true model. To mitigate this difficulty, we have developed a new objective function that fits the seismic-waveform intensity, so the dependence of the starting model can be reduced. The waveform intensity is proportional to the square of its amplitude. Forming the intensity using the waveform is a nonlinear operation, which separates the original waveform spectrum into an ultra-low-frequency part and a higher frequency part, even for data that originally do not have low-frequency contents. Therefore, conducting multiscale inversions starting from ultra-low-frequency intensity data can largely avoid the cycle-skipping problem. We formulate the intensity objective function, the minimization process, and the gradient. Using numerical examples, we determine that the proposed method was very promising and could invert for the model using data lacking low-frequency information.
AB - Many full-waveform inversion schemes are based on the iterative perturbation theory to fit the observed waveforms. When the observed waveforms lack low frequencies, those schemes may encounter convergence problems due to cycle skipping when the initial velocity model is far from the true model. To mitigate this difficulty, we have developed a new objective function that fits the seismic-waveform intensity, so the dependence of the starting model can be reduced. The waveform intensity is proportional to the square of its amplitude. Forming the intensity using the waveform is a nonlinear operation, which separates the original waveform spectrum into an ultra-low-frequency part and a higher frequency part, even for data that originally do not have low-frequency contents. Therefore, conducting multiscale inversions starting from ultra-low-frequency intensity data can largely avoid the cycle-skipping problem. We formulate the intensity objective function, the minimization process, and the gradient. Using numerical examples, we determine that the proposed method was very promising and could invert for the model using data lacking low-frequency information.
UR - http://hdl.handle.net/10754/629950
UR - https://library.seg.org/doi/10.1190/geo2017-0682.1
UR - http://www.scopus.com/inward/record.url?scp=85055559587&partnerID=8YFLogxK
U2 - 10.1190/geo2017-0682.1
DO - 10.1190/geo2017-0682.1
M3 - Article
AN - SCOPUS:85055559587
SN - 0016-8033
VL - 83
SP - R649-R658
JO - GEOPHYSICS
JF - GEOPHYSICS
IS - 6
ER -