KAUST DepartmentEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Seismic Wave Analysis Group
Online Publication Date2020-05-05
Print Publication Date2020-07-01
Permanent link to this recordhttp://hdl.handle.net/10754/662821
MetadataShow full item record
AbstractSummary Full-waveform inversion (FWI) is an effective tool to retrieve a high-resolution subsurface velocity model. The source wavelet accuracy plays an important role in reaching that goal. So we often need to estimate the source function before or within the inversion process. Source estimation requires additional computational cost, and an inaccurate source estimation can hamper the convergence of FWI. We develop a source-independent waveform inversion utilizing a recently introduced wavefield reconstruction based method we refer to as efficient wavefield inversion (EWI). In EWI, we essentially reconstruct the wavefield by fitting it to the observed data as well as a wave equation based on iterative Born scattering. However, a wrong source wavelet will induce errors in the reconstructed wavefield, which may lead to a divergence of this optimization problem. We use a convolution-based source-independent misfit function to replace the conventional data fitting term in EWI to formulate a source-independent EWI (SIEWI) objective function. By convolving the observed data with a reference trace from the predicted data and convolving the predicted data with a reference trace from the observed data, the influence of the source wavelet on the optimization is mitigated. In SIEWI, this new formulation is able to mitigate the cycle-skipping issue and the source wavelet uncertainty, simultaneously. We demonstrate those features on the Overthrust model and a modified Marmousi model. Application on a 2D real dataset also shows the effectiveness of the proposed method.
CitationSong, C., & Alkhalifah, T. (2020). Source-independent efficient wavefield inversion. Geophysical Journal International. doi:10.1093/gji/ggaa196
SponsorsWe thank KAUST for its support and the SWAG group for the collaborative environment. This work utilized the resources of the Supercomputing Laboratory at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia, and we are grateful for that. We also thank CGG for providing the field data set and Geoscience Australia for providing the well-log information. We thank the assistant editor, the editor, Herve Chauris, and the reviewers for their critical and helpful review of the manuscript.
PublisherOxford University Press (OUP)