Time-Domain Full Waveform Inversion Using the Gradient Preconditioning Based on Transmitted Wave Energy
KAUST DepartmentEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2019-07-06
Print Publication Date2019-10
Permanent link to this recordhttp://hdl.handle.net/10754/652949
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AbstractThe gradient preconditioning approach based on seismic wave energy can effectively avoid the huge memory consumption of the gradient preconditioning algorithms based on the Hessian matrix. However, the accuracy of this approach is prone to be influenced by the energy of reflected waves. To tackle this problem, the paper proposes a new gradient preconditioning method based on the energy of transmitted waves. The approach scales the gradient through a precondition factor, which is calculated by the ‘approximate transmission wavefield’ simulation based on the nonreflecting acoustic wave equation. The method requires no computing nor storing of the Hessian matrix and its inverse matrix. Furthermore, the proposed method can effectively eliminate the effects of geometric spreading and disproportionality in the gradient illumination. The results of model experiments show that the time-domain full waveform inversion (FWI) using the gradient preconditioning based on transmitted wave energy can achieve higher inversion accuracy for deep high-velocity bodies and their underlying strata in comparison with the one using the gradient preconditioning based on seismic wave energy. The field marine seismic data test shows that our proposed method is also highly applicable to the FWI of field marine seismic data.
CitationSong P, Tan J, Liu Z, Zhang X, Liu B, et al. (2019) Time-Domain Full Waveform Inversion Using the Gradient Preconditioning Based on Transmitted Wave Energy. Journal of Ocean University of China. Available: http://dx.doi.org/10.1007/s11802-019-3783-z.
SponsorsThe authors appreciate the support of the NSFCShandong Joint Fund for Marine Science Research Centers (No. U1606401), the National Natural Science Foundation of China (Nos. 41574105 and 41704114), the National Science and Technology Major Project of China (No. 2016ZX05027-002) and Taishan Scholar Project Funding (No. tspd20161007).